Note: Descriptions are shown in the official language in which they were submitted.
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MOTION PROFILE TEMPLATES AND MOVEMENT LANGUAGES FOR
WEARABLE DEVICES
FIELD
The present invention relates generally to electrical and electronic hardware,
computer
software, human-computing interfaces, wired and wireless network
communications, data processing
and computing devices. More specifically, techniques related to, motion
profile templates and
movement languages for Wearable devices arc described.
BACKGROUND
With the advent of greater computing capabilities in smaller personal and/or
portable form
factors and an increasing number of applications (i.e., computer and Internet
software or programs)
for different uses, consumers (i.e., users) have access to large amounts of
personal data. Information
and data are often readily available, but poorly captured using conventional
data capture devices.
Conventional devices typically lack capabilities that can capture, analyze,
communicate, or use data
in a contextually-meaningful, comprehensive, and efficient manner. Further,
conventional solutions
arc often limited to specific individual purposes or uses, demanding that
users invest in multiple
devices in order to perform different activities (e.g., a sports watch for
tracking time and distance, a
GPS receiver for monitoring a hike or run, a cyclometer for gathering cycling
data, and others).
Although a wide range of data and information is available, conventional
devices andapplications
fail to provide effective solutions that comprehensively capture data for a
given user across
numerous disparate activities and allow for easy and effective usability
solutions. Various types of
human-computing interfaces are available with conventional solutions, but
typically require manual
intervention that could be disruptive to either an activity or state by
requiring extensive user
interfacing.
Thus, what is needed is a solution for using or interfacing with data capture
devices without
the limitations of conventional techniques.
BRIEF DESCRIPTION OF THE DRAWINGS
Various embodiments or examples ("examples") are disclosed in the following
detailed
description and the accompanying drawings:
FIG. I illustrates an exemplary data-capable band system;
FIG. 2 illustrates a block diagram of an exemplary data-capable band;
FIG. 3 illustrates sensors for use with an exemplary data-capable band;
FIG. 4 illustrates an application architecture for an exemplary data-capable
band;
FIG. 5A illustrates representative data types for use with an exemplary data-
capable band;
FIG. 5B illustrates representative data types for use with an exemplary data-
capable band in
fitness-related activities;
FIG. 5C illuStrates representative data types for use with an exemplary data-
capable band in
sleep management activities;
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FIG. 5D illustrates representative data types for use with an exemplary data-
capable band in
medical-related activities;
FIG. 5E illustrates representative data types for use with an exemplary data-
capable band in
social media/networking-related activities;
FIGS. 6A to 6F depict a variety of motion signatures as input into a band,
such as a data-
capable band, according to various embodiments;
FIG. 7A illustrates a perspective view of an exemplary data-capable band;
FIG. 7B illustrates a side view of an exemplary data-capable band;
FIG. 8A illustrates a perspective view of an exemplary data-capable band;
FIG. 8B illustrates a side view of an exemplary data-capable band;
FIG. 9A illustrates a perspective view of an exemplary data-capable band;
FIG. 9B illustrates a side view of an exemplary data-capable band;
FIG. 10 illustrates an exemplary computer system suitable for use with a data-
capable band;
FIG. 11 depicts an exemplary inference engine of a band configured to detect
an activity
and/or a mode based on monitored motion;
FIG. 12 depicts a representative implementation of one or more bands and
equivalent
devices, as wearable devices, to form unique motion profiles;
FIG. 13 depicts an example of a motion capture manager configured to capture
motion and
portions therefore;
FIG. 14 depicts an example of a motion analyzer configured to evaluate motion-
centric
events;
FIG. 15 illustrates an exemplary data-capable band system configured to create
and share
motion profile templates;
FIG. 16A illustrates an exemplary system for wearable device data security;
FIG. 16B illustrates an exemplary system for media device content management
using
sensory input;
FIG. 16C illustrates an exemplary system for device control using sensory
input;
FIG. 16D illustrates an exemplary system for movement languages in wearable
devices;
FIG. 17A illustrates an exemplary process for media device content management
using
sensory input;
FIG. 17B illustrates an exemplary process for device control using sensory
input;
FIG. 17C illustrates an exemplary process for wearable device data security;
FIG. 17D illustrates an exemplary process for movement languages in wearable
devices; and
FIG. 18 illustrates an exemplary system for creating, storing, and performing
other operations
with regard to, motion profile templates.
DETAILED DESCRIPTION
Various embodiments or examples may be implemented in numerous ways, including
as a
system, a process, an apparatus, a user interface, or a series of program
instructions on a computer
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readable medium such as a computer readable storage medium or a computer
network where the .
program instructions arc sent over optical, electronic, or wireless
communication links. In general,
operations of disclosed processes may be performed in an arbitrary order,
unless otherwise provided
in the claims.
A detailed description of one or more examples is provided below along with
accompanying
figures. The detailed description is provided in connection With such
examples, but is not limited to
any particular example. The scope is limited only by the claims and numerous
alternatives,
modifications, and equivalents arc encompassed. Numerous specific details arc
set forth in the
following description in order to provide a thorough understanding. These
details are provided for
the purpose of example dnd the described techniques may be practiced according
to the claims
without some or all of these specific details. For clarity, technical material
that is known in the
technical fields related to the examples has not been described in detail to
avoid unnecessarily
obscuring the description.
FIG. l illustrates an exemplary data-capable band system. Here, system 100
includes
network 102, bands 104-112, server 114, mobile computing device 116, mobile
Communications
device 118, computer 120, laptop 122, and distributed sensor 124. Bands 104-
112 may be
= implemented as a data-capable device that may .be worn as a strap or band
around an arm, leg, ankle,
or other bodily appendage or feature. In other examples, bands 104-112 may be
attached direetly or
indirectly to other items, organic or inorganic, animate, or static. In still
other examples, bands 104-
112 may be used differently.
As described above, bands 104-112 may be implemented as wearable personal data
or data
capture devices (e.g., data-capable devices) that are worn by a user around a
wrist, ankle, arm, ear, or
other appendage, or attached to the body or affixed to clothing. One or more
facilities, sensing
elements, or sensors, both active and passive, may be implemented as part of
bands 104-112 in order
to capture various types of data from different sources. Temperature,
environmental, temporal,
motion, electronic, electrical, chemical, or other types of sensors (including
those described below in
connection with FIG. 3) may be used in order to gather varying amounts of
data, which may be
configurable by a user, locally (e.g., using user interface facilities such as
buttons, switches, motion-
activated/detected command structures (e.g., accelerometer-gathered data from
user-initiated motion
of bands 104-112), and others) or remotely (e.g., entering rules OF parameters
in a website or
graphical user interface ("GUI") that may be used to modify control systems or
signals in firmware,
circuitry, hardware, and software implemented (i.e., installed) on bands 104-
112). Bands 104-112
may also be implemented as data-capable devices that are configured for data
communication using
various types of communications infrastructure and media, as described in
greater detail below.
Bands 104-112 may also be wearable, personal, non-intrusive, lightweight
devices that arc
configured to gather large amounts of personally relevant data that can be
used to improve user
health, fitness levels, medical conditions, athletic performance, sleeping
physiology, and
physiological conditions, or used as a sensory-based userinterface ("Ul") to
signal social-related
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notifications specifying the state of the user through vibration, heat, lights
or other sensory based
notifications. For example, a social-related notification signal indicating a
user is on-line can be
transmitted to a recipient, who in turn, receives the notification as, for
instance, a vibration.
Using data gathered by bands 104-112, applications may be used to perfomi
various analyses
and evaluations that can generate information as to a person's physical (e.g.,
healthy, sick, weakened,
or other states, or activity level), emotional, or mental state (e.g., an
elevated body temperature or
heart rate may indicate stress, a lowered heart rate and skin temperature, or
reduced movement (e.g.,
excessive sleeping), may indicate physiological depression caused by exertion
or other factors,
chemical data gathered from evaluating outgassing from the skin's Surface may
be analyzed to
determine whether a person's diet is balanced or if various nutrients are
lacking, salinity detectors
may be evaluated to determine if high, lower, or proper blood sugar levels are
present for diabetes
management, and others). Generally, bands 104-112 may be configured to gather
from sensors
locally and remotely.
As an example, band 104 may capture (i.e., record, store, communicate (i.e.,
send or receive),
process, or the like) data from various sources (i.e., sensors that are
organic (i.e., installed, integrated,
or otherwise implemented with band 104) or distributed (e.g., microphones on
mobile computing
device 116, mobile communications device 118, computer 120, laptop 122;
distributed sensor 124,
global positioning system ("GPS") satellites, or others, without limitation))
and exchange data with
one or more of bands 106-112, server 114, mobile computing device 116, mobile
communications
device 118, computer 120, laptop 122, and distributed sensor 124. As shown
here, a local sensor
may be one that is incorporated, integrated, or otherwise implemented with
bands 104-112. A
remote or distributed sensor (e.g., mobile computing device 116, mobile
communications device
118, computer 120, laptop 122, or, generally, distributed sensor 124) may be
sensors that can be
accessed, controlled, or otherwise used by bands 104-112. For example, band
112 may be
configured to control devices that arc also controlled by a given user (e.g.,
mobile computing device
116, mobile communications device 118, computer 120, laptop 122, and
distributed sensor 124). For
example, a microphone in mobile communications device 118 may be used to
detect, for example,
ambient audio data that is used to help identify a person's location, or an
ear clip (e.g., a headset as
described below) affixed to an car may be used to record pulse or blood oxygen
saturation levels.
Additionally, a sensor implemented with a screen on mobile computing device
11.6 may be used to
read a user's temperature or obtain a biometric signature while a user is
interacting with data. A
further example may include using data that is observed on computer 120 or
laptop 122 that provides
information as to a user's online behavior and the type of content that she is
viewing, which may be
used by bands 104-112. Regardless of the type or location of sensor used, data
may be transferred to
bands 104-112 by using, for example, an analog audio jack, digital adapter
(e.g., USB, mini-USB),
or other, without limitation, plug, or other type of connector that may be
used to physically couple
bands 104-112 to another device or system for transferring data and, in some
examples, to provide
power to recharge a battery (not shown). Alternatively, a wireless data
communication interface or
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facility (e.g., a wireless radio that is configured to communicate data from
bands 104-112 using one
or more data communication protocols (e.g., IEEE 802.11a/b/g/n (WiFi), WiMax,
ANTrm, ZigBee ,
Bluetooth , Near .Field Communications ("NFC"), and others)) may be used to
receive or transfer
data. Further, bands 104-112 may be configured to analyze, evaluate, modify,
or otherwise use data
gathered, either directly or indirectly.
In some examples, bands 104-112 may be configured to share data with each
other or with an
intermediary facility, such as a database, website, web service, or the like,
which may be
implemented by server :114. In some embodiments, server 114 can be operated by
a third party
providing, for example, social media-related services (e.g., Facebook0). Bands
104-112 and other
related devices may exchange data with each other directly, or bands 104-112
may exchange data via
a third party server, such as a third party like Facebook , to provide social-
media related services.
Examples of other third party servers include those implemented by social
networking services,
including, but not limited to, services such as Yahoo! IMmt, GTalkTm, MSN
MessengerTm, Twitter
and other private or public social networks. The exchanged data may include
personal physiological
data and data derived from sensory-based user interfaces ("UI"). Server 114,
in some examples, may
be implemented using one or more processor-based computing devices or
networks, including
computing clouds, storage area networks ("SAN"), or the likc. As shown, bands
:104-112 may be
used as a personal data or area network (e.g., "PDN" or "PAN") in which data
relevant to a given
user or band (e.g., one or more of bands 104-112) may be shared. As shown
here, bands 104 and
112 may be configured to exchange data with each other over network 102 or
indirectly using server
114. Users of bands 104 and 112 may direct a web browser hosted on a computer
(e.g., computer
120, laptop 122, or the like) in order to access, view, modify, or perform
other operations with data
captured by bands 104 and 112. For example, two runners using bands 104 and
112 may be
= geographically remote (e.g., users are not geographically in close
proximity locally such that bands
being used by each user are in direct data communication), but wish to share
data regarding their race
times (pre, post, or in-race), personal records (i.e., "PR"), target split
times, results, performance
characteristics (e.g., target heart rate, target V02 max, and others), and
other information. If both
runners (i.e., bands 104 and 112) are engaged in a race on the same day, data
can be gathered for
comparative analysis and other uses. Further, data can be shared in
substantially real-time (taking
into account any latencies incurred by data transfer rates, network
topologies, or other data network
factors) as well as uploaded after a given activity or event has been
performed. In other words, data
can be captured by the user as it is worn and configured to transfer data
using, for example, a
wireless network connection (e.g., a wireless network interface card, wireless
local area network
("LAN") card, cell phone, or the like). Data may also be shared in a
temporally asynchronous
manner in which a wired data connection (e.g., an analog audio plug (and
associated software or
firmware) configured to transfer digitally encoded data to encoded audio data
that may be transferred
between bands 104-112 and a plug configured to receive, encode/decode, and
process data
exchanged) may be used to transfer data from one or more bands 104-112 to
various destinations
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(e.g., another of bands 104-112, server 114, mobile computing device 116,
mobile communications
device 118, computer 120, laptop 122, and distributed sensor 124). Bands 104-
112 may be
implemented with various types of wired and/or wireless communication
facilities and arc not
intended to be limited to any specific technology. For example, data may be
transferred from bands
104-112 using an analog audio plug (e.g., TR.RS, TRS, or others). In other
examples, wireless
communication facilities using various types of data comniunication protocols
(e.g.,-WiFi,
Bluetoothe, ZigBeeriO, ANTrm, and others) may be implemented as part of bands
104-112, which
may include circuitry, firmware, hardware, radios, antennas, processors,
microprocessors, memories,
or other electricalõ electronic, mechanical, or physical elements configured
to enable data
communication capabilities of various types and characteristics.
As data-capable devices, bands 104-112 may be configured to collect data from
a wide range
of sources, including onboard (not shown) and distributed sensors (e.g.,
server 114, mobile
computing device 116, mobile communications device 118, computer 120, laptop
122, and
distributed sensor 124) or other bands. Some or all data captured may be
personal, sensitive, or
confidential and various techniques for providing secure storage and access
may be implemented.
For example, various types of security protocols and algorithms may be used to
encode data stored or
accessed by bands 104-112. Examples of security protocols and algorithms
include authentication,
encryption, encoding, private and public key infrastructure, passwords,
checksums, hash codes and
hash functions (e.g., SHA, SHA-1, MD-5, and the like), or others may be used
to prevent undesired
access to data captured by bands 104-112. In other examples, data security for
bands 104-112 may
be implemented differently.
Bands 104-112 may be used as personal wearable, data capture devices that,
when worn, are
configured to identify a specific, individual user. By evaluating captured
data such as motion data
from an accelerometer, biometric data such as heart rate, skin galvanic
response, and other *metric
data, and using analysis techniques, both long and short-term (e.g., software
packages or modules of
any type, without limitation), a user may have a unique pattern of behavior or
motion and/or
biometric responses that can be used as a signature for identification. For
example, bands 104-112
may gather data regarding an individual person's gait or other unique
biometric, physiological or
behavioral characteristics. Using, for example, distributed sensor 124, a
biometric signature (e.g.,
fingerprint, retinal or iris vascular pattern, or others) may be gathered and
transmitted to bands 104-
112 that, when combined with other data, determines that a given user has been
properly identified
and, as such, authenticated. When bands 104-112 are worn, a user may be
identified and
authenticated to enable a variety of other functions such as accessing or
modifying data, enabling
= wired or wireless data transmission facilities (i.e., allowing the
transfer of data from bands 104-112),
modifying functionality or functions of bands 104-112, authenticating or
authorizing financial
transactions using stored data and information (e.g., credit card, PIN, card
security numbers, and the
like), running applications that allow for various operations to be performed
(e.g., controlling
physical security and access by transmitting a security code to a reader that,
when authenticated,
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unlocks a door by turning off current to an electromagnetic lock, and others),
and others. Different
functions and operations beyond those described may be performed using bands
104-112, which can
act as secure, personal, wearable, data-capable devices. The number, type,
function, configuration,
specifications, structure, or other features of system 100 and the above-
described elements may be
varied and arc not limited to the examples provided.
FIG. 2 illustrates a block diagram of an exemplary data-capable band. Here,
band 200
includes bus 202, processor 204, memory 206, notification facility 208,
accelerometer 210, sensor
212, battery 214, and communications facility 216. In some examples, the
quantity, type, function,
structure, and configuration of band 200 and the elements (e.g., bus 202,
processor 204, memory
206, notification facility 208, accelerometer 210, sensor 212, battery 214,
and communications
facility 216) shown May be varied and are not limited to the examples
provided. As shown,
processor 204 may be implemented as logic to provide control functions and
signals to memory 206,
notification facility 208, accelerometer 210, sensor 212, battery 214, and
communications facility
216. Processor 204 may be implemented using any type of processor or
microprocessor suitable for
packaging within bands 104-112 (FIG. 1). Various types of microprocessors may
be used to provide
data processing capabilities for band 200 and are not limited to any specific
type or capability. For
example, a MSP430F5528-type microprocessor manufactured by Texas Instruments
of Dallas, Texas
may be configured for data communication using audio tones and enabling the
use of an audid plug-
and-jack system (e.g., TRRS, TRS, or others) for transferring data captured by
band 200. Further, =
different processors may be desired if other functionality (e.g., the type and
number of sensors (e.g.,
sensor 212)) are varied. Data processed by processor 204 may be stored using,
for example, memory
206.
In some examples, memory 206 may be implemented using various types of data
storage
technologies and standards, including, without limitation, read-only memory
("ROM"), random
access memory ("RAM"), dynamic random access memory ("DRAM"), static random
access
memory ("SRAM"), static/dynamic random access memory ("SDRAM"), Magnetic
random access
memory ("MRAM"), solid state, two and three-dimensional memories, Flash, and
others. Memory
206 may also be implemented using one or more partitions.that are configured
for multiple types of
data storage technologies to allow for non-modifiable (i.e., by a user)
software to be installed (e.g.,
firmware installed on ROM) while also providing for storage of captured data
and applications using,
for example, RAM. Once captured and/or stored in memory 206, data may be
subjected to various
operations performed by other elements of band 200.
Notification facility 208, in some examples, may be implemented to provide
vibratory
energy, audio or visual signals, communicated through band 200. As used
herein, "facility" refers to
any, some, or all of the features and structures that arc used to implement a
given set of functions. In
some examples, the vibratory energy may be implemented using a motor or other
mechanical
structure. In some examples, the audio signal may be a tone or other audio
cue, or it may be
implemented using different sounds for different purposes. The audio signals
may be emitted
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directly using notification facility 208, or indirectly by transmission via
communications facility 216
to other audio-capable devices (e.g., headphones (not shown), a headset (as
described below with
regard to FIG. 12), mobile computing device 116, mobile communications device
118, computer
120, laptop 122, distributed sensor 124, etc.). In some examples, the visual
signal may be
implemented using any available display technology, such as lights, light-
emitting diodes (LEDs),
interferometric modulator display (IMOD), electrophoretic ink (E Ink), organic
light-emitting diode
(OLED), or other display technologies. As an example, an application stored on
memory 206 may
be configured to monitor a clock signal from processor 204 in order to provide
timekeeping
functions to band 200. For example, if an alarm is set for a desired time,
notification facility 208
may be used to provide a vibration or an audio tone, or a series of vibrations
or audio tones, when the
desired time occurs. As another example, notification facility 208 may be
coupled to a framework
(not shown) or other structure that. is used to translate or communicate
vibratory energy throughout
the physical structure of band 200. In other examples, notification facility
208 may be implemented
differently.
Power may be stored in battery 214, which may be implemented as a battery,
battery-module,
power management module, or the like. Power may also be gathered from local
power sources such
as solar panels, thermo-clectric generators, and kinetic energy generators,
among others that are
alternatives power sources to external power for a battery. These additional
sources can either power
the system directly or can charge a battery, which, in turn, is used to power
the system (e.g., of a
band). In other words, battery 214 may include a rechargeable, expendable,
replaceable, or other
type of battery, but also circuitry, hardware, or software that may be used in
connection with in lieu
of processor 204 in order to provide power management, charge/recharging,
sleep, or other
functions. Further, battery 214 may be implemented using various types of
battery technologies,
including Lithium Ion ("LI"), Nickel Metal Hydride ("NiMH"), or others,
without limitation. Power
drawn as electrical current may be distributed from battery via bus 202, the
latter of which may be
implemented as deposited or formed circuitry or using other forms of circuits
or cabling, 'including
flexible circuitry. Electrical current distributed from battery 204 and
managed by processor 204 may
be used by one or more of memory 206, notification facility 208, accelerometer
210, sensor 212, or
communications facility 216.
As shown, various sensors may be used as input sources for data captured by
band 200. For
example, accelerometer 210 may be used to gather data measured across one,
two, or three axes of
motion. In addition to accelerometer 210, other sensors (i.e., sensor 212) may
be implemented to
provide temperature, environmental, physical, chemical, electrical, or other
types of sensed inputs.
As presented here, sensor 212 may include one or multiple sensors and is not
intended to be limiting
as to the quantity or type of sensor implemented. Data captured by band 200
using accelerometer
210 and sensor 212 or data requested from another source (i.e., outside of
band 200) may also be
exchanged, transferred, or otherwise communicated using communications
facility 216. For
example, communications facility 216 may include a wireless radio, control
circuit or logic, antenna,
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transceiver, receiver, transmitter, resistors, diodes, transistors, or other
elements that arc used to
transmit and receive data from band 200. In some examples, communications
facility 216 may be
implemented to provide a "wired" data communication capability such as an
analog or digital
attachment, plug, jack, or the like to allow for data to be transferred. in
other examples,
communications facility 216 may be implemented to provide a wireless data
communication
capability to transmit digitally encoded data across one or more frequencies
using various types of
data communication protocols, without limitation. In still other examples,
band 200 and the above-
described elements may be varied in function, structure, configuration, or
implementation and are not
limited to those shown and described.
FIG. 3 illustrates sensors for use with an exemplary data-capable band. Sensor
212 may be
implemented using various types of sensors, some of which are shown. Like-
numbered and named
elements may describe the same or substantially similar clement as those shown
in other
descriptions.. Here, sensor 212 (FIG. 2) may be implemented as accelerometer
302,
altimeter/barometer 304, light/infrared ("IR") sensor 306, pulse/heart rate
("HR") monitor 308, audio
sensor (e.g., microphone, transducer, or others) 310, pedometer 312,
velocimetcr 314, GPS receiver
316, location-based service sensor (e.g., sensor for determining location
within a cellular or micro
cellular network, which may ormay not use GPS or other satellite
constellations for fixing a
position) 318, motion detection sensor 320, environmental sensor 322, chemical
sensor 324,
electrical sensor 326, or mechanical sensor 328.
As shown, accelerometer 302 may be used to capture data associated with motion
detection
along 1,2, or 3-axes of measurement, without limitation to any specific type
of specification of
sensor. Accelerometer 302 may also be implemented to measure various types of
user motion and
may be configured based on the type of sensor, firmware, software, hardware,
or circuitry used. As
another example, altimeter/barometer 304 may be used to measure environment
pressure,
atmospheric or otherwise, and is not limited to any specification or type of
pressure-reading device.
In some examples, altimeter/barometer 304 may be an altimeter, a barometer, or
a combination
thereof. For example, altimeter/barometer 304 may be implemented as an
altimeter for measuring
above ground level ("AGL") pressure in band 200, which has been configured for
use by naval or
military aviators. As another example, altimeter/barometer 304 may be
implemented as a barometer
for reading atmospheric pressure for marine-based applications. In other
examples,
altimeter/barometer 304 may be implemented differently.
Other types of sensors that may be used to measure light or photonic
conditions include
light/IR sensor 306, motion detection sensor 320, and environmental sensor
322, the latter of which
may include any type of sensor for capturing data associated with
environmental conditions beyond
light. Further, motion detection sensor 320 may be configured to detect motion
using a variety of
techniques and technologies, including, but not limited to comparative or
differential light analysis
(e.g., comparing foreground and background lighting), sound monitoring, or
others. Audio sensor
310 may be implemented using any type of device configured to record or
capture sound.
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In some examples, pedometer 312 may be implemented using devices to measure
various
types of data associated with pedestrian-oriented activities such as running
or walking. Footstrikcs,
snide length, stride length or interval, time, and other data may be measured.
Velocimeter 314 may
be implemented, in some examples, to measure velocity (e.g., speed and
directional vectors) without
limitation to any particular activity. Further, additional sensors that may be
used as sensor 212
include those configured to identify or obtain location-based data. For
example, GPS receiver 316
may be used to obtain coordinates of the geographic location of band 200
using, for example, various
types of signals transmitted by civilian and/or military satellite
constellations in low, medium, or
high earth orbit (e.g., "LEO," "MEO," or "GEO"). In other examples,
differential GPS algorithms
may also be implemented with GPS receiver 316, which may be used to generate
more precise or
accurate coordinates. Still further; location-based services sensor 318 may be
implemented to obtain
location-based data including, but not limited to location, nearby services or
items of interest, and the
like. As an example, location-based services sensor 318 may be configured to
detect an electronic
signal, encoded or otherwise, that provides information regarding a physical
locale as band 200
passes. The electronic signal may include, in some examples, encoded data
regarding the location
and information associated therewith. Electrical sensor 326 and mechanical
sensor 328 may be
configured to include other types (e.g., haptic, kinetic, piezoelectric,
piczomechanical, pressure,
touch, thermal, and others) of sensors for data input to band 200, without
limitation. Other types of
sensors apart from those shown may also be used, including magnetic flux
sensors suCh as solid-state
compasses and the like, including gyroscopic sensors. While the present
illustration provides
numerous examples of types of sensors that may be used with band 200 (FIG. 2),
others not shown or
described may be implemented with or as a substitute for any sensor shown or
described.
FIG. 4 illustrates an application architecture for an exemplary data-capable
band. Here,
application architecture 400 includes bus 402, logic module 404,
communications module 406,
security module 408, interface module 410, data management 412, audio module
414, motor
controller 416, service management module 418, sensor input evaluation module
420, and power
management module 422. In some examples, application architecture 400 and the
above-listed
elements (e.g., bus 402, logic module 404, communications module 406, security
module 408,
interface module 410, data management 412, audio module 414, motor controller
416, service
management module 418, sensor input evaluation module 420, and power
management module 422)
may be implemented as software using various computer programming and
formatting languages
such as Java, C++, C, and others. As shown here, logic module 404 may be
firmware or application
software that is installed in memory 206 (FIG. 2) and executed by processor
204 (FIG. 2). Included
with logic module 404 may be program instructions or code (e.g., source,
object, binary executables,
or others) that, when initiated, called, or instantiated, perform various
functions.
For example, logic module 404 may be configured to send control signals to
communications
module 406 in order to transfer, transmit, or receive data stored in memory
206, the latter of which
may be managed by a database management system ("DBMS") or utility in data
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module 412. As another example, security module 408 may be controlled by logic
module 404 to
provide encoding, decoding, encryption, authentication, or other functions to
band 200 (FIG. 2).
Alternatively, security module 408 may also be implemented as an application
that, using data
captured from various sensors and stored in memory 206 (and accessed by data
management module
412) may bc used to provide identification functions that enable band 200 to
passively identify a user
or wearer of band 200. Still further, various types of security software and
applications may be used
and are not limited to those shown and described.
Interface module 410, in some examples, may be used to manage user interface
controls such
as switches, buttons, or other types of controls that enable a user to Manage
various functions of band
200. For example, a 4-position switch may be turned to a given position that
is interpreted by
interface module 410 to determine the proper signal or feedback to send to
logic module 404 in order
to generate a particular result. In other examples, a button (not shown) may
be depressed that allows
a user to trigger or initiate certain actions by sending another signal to
logic module 404. Still
further, interface module 410 may be used to interpret data from, for example,
accelerometer 210
(FIG. 2) to identify specific movement or motion that initiates or triggers a
given response. In other
examples, interface module 410 may be used to manage different types of
displays (e.g., LED,
IMOD, E Ink, OLED, etc.). In other examples, interface module 410 may be
implemented
differently in function, structure, or configuration and is not limited to
those shown and described.
As shown, audio module 414 may be configured to manage encoded or unencoded
data
gathered from various types of audio sensors. In some examples, audio module
414 may include one
or more codecs that are used to encode or decode various types of audio
waveforms. For example,
analog audio input may be encoded by audio module 414 and, once encoded, sent
as a signal or
collection of data packets, messages, segments, frames, or the like to logic
module 404 for
transmission via communications module 406. In other examples, audio module
414 may be
implemented differently in function, structure, configuration, or
implementation and is not limited to
those shown and described. Other elements that may be used by band 200 include
motor controller
416, which may be firmware or an application to control a motor or other
vibratory energy source
(e.g., notification facility 208 (FIG. 2)). Power used for band 200 may be
drawn from battery 214
(FIG. 2) and managed by power management module 422, which may be firmware or
an application
used to manage, with or without user input, how power is consumer, conserved,
or otherwise used by
band 200 and the above-described elements, including one or more sensors
(e.g., sensor 212 (FIG.
2), sensors 302-328 (FIG. 3)). With regard to data captured, sensor input
evaluation module 420
may be a software engine or module that is used to evaluate and analyze data
received from one or
more inputs (e.g., sensors 302-328) to band 200. When received, data may be
analyzed by sensor
input evaluation module 420, which may include custom or "off-the-shelf'
analytics packages that
arc configured to provide application-specific analysis of data to determine
trends, patterns, and
other useful information. In other examples, sensor input module 420 may also
include firmware or
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software that enables the generation of various types and formats of reports
for presenting data and
any analysis performed thereupon.
Another element of application architecture 400 that may be included is
service management
module 418. In sonic examples, service management module 418 may be firmware,
software, or an
application that is configured to manage various aspects and operations
associated with executing
software-related instructions for band 200. For example, libraries or classes
that are used by
software or applications on band 200 may be served from an online or networked
source. Service
management module 418 may be implemented to manage how and when these services
are invoked
in order to ensure that desired applications.are executed properly within
application architecture 400.
As discrete sets, collections, or groupings of functions, services used by
band 200 for various.
purposes ranging from communications to operating systems to call or document
libraries may be
managed by service management module 418. Alternatively, service management
module 418 may
be implemented differently and is not limited to the examples provided herein.
Further, application
architecture 400 is an example of a software/system/application-level
architecture that may be used
to implement various software-related aspects of band 200 and may be varied in
the quantity, type,
configuration, function, structure, or type of programming or formatting
languages used, without
limitation to any given example.
FIG. 5A illustrates representative data types for use with an exemplary data-
capable band.
Here, wearable device 502 may capture various types of data, including, but
not limited to sensor
data 504, manually-entered data 506, application data 508, location data 510,
network data 512,
system/operating data 514, and user data 516. Various types of data may be
captured from sensors,
such as those described above in connection with FIG. 3. Manually-entered
data, in some examples,
, may be data or inputs received directly and locally by band 200 (FIG. 2).
In other examples,
manually-entered data may also be provided through a third-party websitc that
stores the data in a
database and may be synchronized from server 114 (FIG. 1) with one ormore of
bands 104-112.
Other types of data that may be captured including application data 508 and
system/operating data
514, which may be associated with firmware, software, or hardware installed or
implemented on
band 200. Further, location data 510 may be used by wearable device 502, as
described above. User
data 516, in some examples, may be data that include profile data,
preferences, rules, or other
information that has been previously entered by a given user of wearable
device 502. Further,
network data 512 may be data is captured by wearable device with regard to
routing tables, data
paths, network or access availability (e.g., wireless netwerk access
availability), and the like. Other
types of data may be captured by wearable device 502 and are not limited to
the examples shown and
described. Additional context-specific examples of types of data captured by
bands 104-112 (FIG. 1)
are provided below.
FIG. 5B illustrates representative data types for use with an exemplary data-
capable band in
fitness-related activities. Here, band 519 may be configured to capture types
(i.e., categories) of data
such as heart rate/pulse monitoring data 520, blood oxygen saturation data
522, skin temperature data
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524, salinity/emiSsion/outgassing data 526, location/GPS data 528,
environmental data 530, and
accelerometer data 532. As an example, a runner may use or wear band 519 to
obtain data associated
with his physiological condition (i.e., heart rate/pulse monitoring data 520,
skin temperature,
salinity/cmission/outgassing data 526, among others), athletic efficiency
(i.e., blood oxygen.
saturation data 522), and performance (i.e., location/GPS data 528 (e.g.,
distance or laps run),
environmental data 530 (e.g., ambient temperature, humidity, pressure, and the
like), accelerometer
532 (e.g., biomechanical information, including gait, stride, stride length,
among others)). Other or
different types of data may be captured by band 519, but the above-described
examples are
illustrative of some types of data that may be captured by band 519. Further,
data captured may be
uploaded to a wcbsite or online/networked destination for storage and other
uses. For example,
fitness-related data may be used by applications that are downloaded from a
"fitness marketplace"
where athletes may find, purchase, or download applications for various uses.
Some applications
may be activity-specific and thus may be used to modify or alter the data
capture capabilities of band
519 accordingly. For example, a fitness marketplace may be a wcbsite
accessible by various types of
mobile and non-mobile clients to locate applications for different exercise or
fitness categories such
as running, swimming, tennis, golf, baseball, football, fencing, and many
others. When downloaded,
a fitness marketplace may also be used with user-Specific accounts to manage
the retrieved
applications as well as usage with band 519, or to use the data to provide
services such as online
personal coaching or targeted advertisements. More, fewer, or different types
of data may be
captured for fitness-related activities.
FIG. 5C illustrates representative data types for use with an exemplary data-
capable band in
sleep management activities. Here, band 539 may be used for sleep management
purposes to track
various types of data, including heart rate monitoring data 540, motion sensor
data 542,
accelerometer data 544, skin resistivity data 546, user input data 548, clock
data 550, and audio data
552. In some examples, heart rate monitor data 540 may be captured to evaluate
rest, waking, or
various states of sleep. Motion sensor data 542 and accelerometer'data 544 may
be used to
determine whether a user of band 539 is experiencing a restful or fitful
sleep. For example, some
motion sensor data 542 may be captured by a light sensor that measures ambient
or differential light
patterns in order to determine whether a user is sleeping on her front, side,
or back. Accelerometer
data 544 may also be captured to determine whether a user is experiencing
gentle or violent
disruptions when sleeping, such as those often found in afflictions of sleep
apnea or other sleep
disorders. Further, skin resistivity data 546 may be captured to determine
whether a user is
running a temperature, sweating, experiencing chills, clammy skin, and
others). Still further, user
input data may include data input by a user as to how and whether band 539
should trigger
notification facility 208 (FIG. 2) to wake a user at a given time or whether
to use a series of
increasing or decreasing vibrations or audio tones to trigger a waking state.
Clock data (550) may be
used to measure the duration of sleep or a finite period of time in which a
user is at rest. Audio data
may also be captured to determine whether a user is snoring and, if so, the
frequencies and amplitude
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therein may suggest physical conditions that a user may be interested in
knowing (e.g., snoring,
breathing interruptions, talking in one's sleep, and the like). More, fewer,
or different types of data
may be captured for sleep management-related activities.
FIG. 5D illustrates representative data types for use with an exemplary data-
capable band in
medical-related activities. Here, band 539 may also be configured for medical
purposes and related-
types of data such as heart rate monitoring data 560, respiratory monitoring
data 562, body
temperature data 564, blood sugar data 566, chemical protein/analysis data
568, patient medical
records data 570, and healthcare professional (e.g., doctor, physician,
registered nurse, physician's
assistant, dentist, orthopedist, Surgeon, and others) data 572. In some
examples, data may be
captured by band 539 directly from wear by a user. For example, band 539 may
be able to sample
and analyze sweat through a salinity or moisture detector to identify whether
any particular
chemicals, proteins, hormones, or other organic or inorganic compounds arc
present, which can be
analyzed by band 539 or communicated to server 114 to perform further
analysis. If sent to server
114, further analyses may be performed by a hospital or other medical facility
using data captured by
band 539. In Other examples, more, fewer, or different types of data may be
captured for medical- =
related activities.
FIG. 5E illustrates representative data types for use With an exemplary data-
capable band in
social media/networking-related activities. Examples of social
media/networking-related activities
include activities related to Internet-based Social Networking Services
("SNS"), such as Facebooke,
Twitter , etc. Here, band 519, shown with an audio data plug, may be
configured to capture data for
use with various types of social media and networking-related services,
websites, and activities.
Accelerometer data 580, manual data 582, other user/friends data 584, location
data 586, network
data 588, clock/timer data 590, and environmental data 592 arc examples of
data that may be
gathered and shared by, for example, uploading data from band 519 using, for
example, an audio
plug such as those described herein. As another example, accelerometer data
580 may be captured.
and shared with other users to share motion, activity, or other movement-
oriented data. Manual data
582 may be data that a given user also wishes to share with other users.
Likewise, other user/friends
data 584 may be from other bands (not shown) that can be shared or aggregated
with data captured
by band 519. Location data 586 for band 519 may also be shared with other
users. In other
examples, a user may also enter manual data 582 to prevent other users or
friends from receiving
updated location data from band 519. Additionally, network data 588 and
clock/timer data may be
captured and shared with other users to indicate, for example, activities or
events that a given user
(i.e., wearing band 519) was engaged at certain locations. Further, if a user
of band 519 has 'friends
who are not geographically located in close or near proximity (e.g., the user
of band 519 is located in
San Francisco and her friend is located in Rome), environmental data can be
captured by band 519
(e.g., weather, temperature, humidity, sunny or overcast (as interpreted from
data captured by a light
sensor and combined with captured data for humidity and temperature), among
others). In other
examples, more, fewer, or different types of data may be captured for medical-
related activities.
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FIG. 6A to 6F depict a variety of motion signatures as input into a band, such
as a data-
capable band. In FIG. 6A, diagram 600 depicts a user's arm (e.g., as a
locomotive member or
appendage) with a band 602 attached to user wrist 603. Band 602 can envelop or
substantially
surround user wrist 603 as well. FIGS. 6B to 6D illustrate different "motion
signatures" defined by
various ranges of motion and/or motion patterns (as well as number of
motions). In some examples,
each of the motion signatures may identify a mode of operation. In other
examples, a motion
signature may provide a different kind ofinput. For example, FIG. 6B depicts
an up-and-down
motion, FIG. 6C depicts a rotation about the wrist, and FIG. 6D depicts a side-
to-side motion. in
another example, FIG. 6E depicts an ability to detect a change in mode as a
function of motion and
deceleration (e.g., when a user claps hands or makes contact with a surface
620 to get band 602 to
change modes). In still another example. FIG. 6F depicts an ability to detect
"no motion" initially
and experience an abrupt acceleration of the band (e.g., user taps band with
finger 630 to change
modes). In some examples, motion signatures may be motion patterns that are
predetermined, with
the user selecting or linking a specific motion signature to invoke a specific
mode. In other
examples, a user may define unique motion signatures. In some embodiments, any
number of detect
motions can be used to define a motion signature. Thus, in some examples,
different numbers of the
same Motion can activate different modes. For example, two of the up-and-down
motions depicted
in FIG. 6B can activate one mode, whereas four up-and-down motions can
activate another mode. In
other examples, any combination of motions (e.g., two up-and-down motions of
FIG. 6B and two
taps of FIG. 6E) can be used as an input, regardless of whether a mode of
operation or otherwise
(e.g., to communicate to another device, to display information, or other
action).
FIG. 7A illustrates a perspective view of an exemplary data-capable band
configured to
receive overmolding. Here, band 700 includes framework 702, covering 704,
flexible circuit 706,
covering 708, motor 710, coverings 714-724, plug 726, accessory 728, control
housing 734, control
736, and flexible circuits 737-738. In some examples, band 700 is shown with
various elements (i.e.,
covering 704, flexible circuit 706, covering 708, motor 710, coverings 714-
724, plug 726, accessory
728, control housing 734, control 736, and flexible circuits 737-738) coupled
to framework 702.
Coverings 708, 714-724 and control housing 734 may be configured to protect
various types of
elements, which may be electrical, electronic, mechanical, structural, or of
another type, without
limitation. For example, covering 708 may be used to protect a battery and
power management
module from protective material formed around band 700 during an injection
molding operation. As
another example, housing 704 may be used to protect a printed circuit board
assembly ("PC BA")
from similar damage. Further, control housing 734 may be used to protect
various types of user
interfaces (e.g., switches, buttons (e.g., control 736), lights, light-
emitting diodes, or other control
features and functionality) from damage. In other examples, the elements shown
may be varied in
quantity, type, manufacturer, specification, function, structure, or other
aspects in order to provide
data capture, communication, analysis, usage, and other capabilities to band
700, which may be worn
by a user around a wrist, arm, leg, ankle, neck or other protrusion or
aperture, without restriction.
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Band 700, in sbmc examples, illustrates an initial unlayercd device that may
be protected using the
techniques for protective overmolding as described above. Alternatively, the
number, type, function,
configuration, ornamental appearance, or other aspects shown may be varied
without limitation.
FIG. 7B illustrates a side view of an exemplary data-capable band. Here, band
740 includes
framework 702, covering 704, flexible circuit 706, covering 708, motor 710,
battery 712, coverings
714-724, plug 726, accessory 728, button/switch/LED 730-732, control housing
734, control 736,
and flexible circuits 737-738 and is shown as a side view of band 700. In
other examples, the
number, type, function, configuration, ornamental appearance, or other aspects
shown may be varied
without limitation.
FIG. 8A illustrates a perspective of an exemplary data-capable band having a
first molding.
Here, an alternative band (i.e., band 800) includes molding 802, analog audio
TRRS-type plug
. (hereafter "plug") 804, plug housing 806, button 808, framework 810,
control housing 812, and
indicator light 814. In some examples, a first protective overmolding (i.e.,
molding 802) has been
applied over band 700 (FIG. 7) and the above-described elements (e.g.,
covering 704, flexible circuit
706, covering 708, motor 710, coverings 714-724, plug 726, accesSory 728,
control housing 734,
control 736, and flexible circuit 738) leaving some elements partially exposed
(e.g., plug 804, plug
housing 806, button 808, framework 810, control housing 812, and indicator
light 814). However,
internal PCBAs, flexible connectors, Circuitry, and other sensitive elements
have been protectively
covered with a first or inner molding that can be configured to further
protect band 800 from
subsequent moldings formed over band 800 using the above-described techniques.
In other
examples, the type, configuration, location, shape, design, layout, or other
aspects of band 800 may
be varied andarc not limited to those shown and described. For example, TRRS
plug 804 may be
removed if a wireless communication facility is instead attached to framework
810, thus having a =
transceiver, logic, and antenna instead being protected by molding 802. As
another example, button
808 may be removed and replaced by another control mechanism (e.g., an
accelerometer that
provides motion data to a processor that, using firmware and/or an
application, can identify and
resolve different types of motion that band 800 is undergoing), thus enabling
molding 802 to be
extended more fully, if not completely, over band 800. In other examples, the
number, type,
function, configuration, ornamental appearance, or other aspects shown may be
varied without
limitation.
FIG. 8B illustrates a side view of an exemplary data-capable band. Here, band
820 includes
molding 802, plug 804, plug housing 806, button 808, control housing 812, and
indicator lights 814
and 822. In other examples, the number, type, function, configuration,
ornamental appearance, or
other aspects shown may be varied without limitation.
FIG. 9A illustrates a perspective view of an exemplary data-capable band
having a second
molding. Here, band 900 includes molding 902, plug 904, and button 906. As
shown another
overmolding or protective material has been formed by injection molding, for
example, molding 902
over band 900. As another molding or covering layer, molding 902 may also be
configured to
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receive surface designs, raised textures, or patterns, which may be used to
add to the commercial
appeal of band 900. In sonic examples, band 900 may be illustrative of a
finished data-capable band
(i.e., band 700 (FIG. 7), 800 (FIG. 8) or 900) that may be configured to
provide a wide range of
electrical, electronic, mechanical, structural, photonic, or other
capabilities.
Here, band 900 may be configured to perform data communication with one or
more other
data-capable devices (e.g., other bands, computers, networked computers,
clients, servers, peers, and
the like) using wired or wireless features. For example, plug 900 may be used,
in connection with
firmware and software that allow for the transmission of audio tones to send
or receive encoded data,
which may be performed using a variety of encoded waveforms and protocols,
without limitation. In
other examples, plug 904 may be removed and instead replaced with a wireless
communication
facility that is protected by molding 902. If using a wireless communication
facility and protocol,
band 900 may communicate with other data-capable devices such as cell phones,
smart phones,
computers (e.g., desktop, laptop, notebook, tablet, and the like), computing
networks and clouds, and
other types of data-capable devices, without limitation. In still other
examples, band 900 and the
elements described above in connection with FIGs. 1-9, may be varied in type,
configuration,
function, structure, or other aspects, without limitation to any of the
examples shown and described.
FIG. 9B illustrates a side view of an exemplary data-capable band. Here, band
910 includes
molding 902, plug 904, and.button 906. In other examples, the number, type,
function,
configuration, ornamental appearance, or other aspects shown may be varied
without limitation.
FIG. 10 illustrates an exemplary computer system suitable for use with a data-
capable band.
In some examples, computer system 1000 may be used to implement computer
programs,
applications, methods, processes, or other software to perform the above-
described techniques.
Computer system 1000 includes a bus 1002 or other communication mechanism for
communicating
information, which interconnects subsystems and devices, such as processor
1004, system memory
1006 (e.g., RAM), storage device 008 (e.g., ROM), disk drive 1010 (e.g.,
magnetic or optical),
communication interface 1012 (e.g., modem or Ethernet card), display 1014
(e.g., CRT or LCD),
input device 1016 (e.g., keyboard), and cursor control 1018 (e.g., mouse or
trackball).
According to some examples, computer system 1000 performs specific operations
by
processor 1004 executing one or more sequences of one or more instructions
stored in system
memory 1006. Such instructions may be read into system memory 1006 from
another computer
readable medium, such as static storage device 1008 or disk drive .1010. In
some examples, hard-
wired circuitry may be used in place of or in combination with software
instructions for
implementation.
The term "computer readable medium" refers to any tangible medium that
participates in
providing instructions to processor 1004 for execution. Such a medium may take
many forms,
including but not limited to, non-volatile media and volatile media. Non-
volatile media includes, for
example, optical or magnetic disks, such as disk drive 1010. Volatile media
includes dynamic
memory, such as system memory 1006.
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Common forms of computer readable media includes, for example, floppy disk,
flexible disk,
hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical
medium, punch
cards, paper tape, any other physical medium with patterns of holes, RAM,
PROM, EPROM,
FLASH-EPROM, any other memory chip or cartridge, or any other medium from
which a computer
can read.
Instructions may further be transmitted or received using a transmission
medium. The term
"transmission medium" may include any tangible or intangible medium that is
capable of storing,
encoding or carrying instructions. for execution by the machine, and includes
digital or analog
communications signals or other intangible medium to facilitate communication
of such instructions.
Transmission media includes coaxial cables, copper wire, and fiber optics,
including wires that
comprise bus 1002 for transmitting a computer data signal. =
In some examples, execution of the sequences of instructions may be performed
by a single
computer system 1000. According to some examples, two or more computer systems
1000 coupled
by communication link 1020 (e.g., LAN, PSTN, or wireless network) may perform
the sequence of
instructions in coordination with one another. Computer system 1000 may
transmit and receive
messages, data, and instructions, including program, i.e., application code,
through communication
link 1020 and communication interface 1012. Received program code may be
executed by processor
1004 as it is received, and/or stored in disk drive 1010, Or other non-
volatile storage for later
execution.
FIG. 11 depicts an exemplary inference engine of a band configured to detect
an activity
and/or a mode based on monitored motion. In some embodiments, inference engine
1104 of a band
can be configured to detect an activity or mode, or a state of a band, as a
function of at least data =
derived from one or more sources of data, such as any number of sensors.
Examples of data obtained
by the sensors include, but are not limited to, data describing motion,
location, user characteristics
(e.g., heart rate, body temperature, etc.), enVironmental characteristics
(e.g., time, degree of ambient
light, altitude, magnetic flux (e.g,, magnetic field of the earth), or any
other source of magnetic flux),
GPS-generated position data, proximity to other band wearers, etc.), and data
derived or sensed by
any source of relevant information. Further, inference engine 1104 is
configured to analyze sets of
data from a variety of inputs and sources of information to identify an
activity, mode and/or state of a
band. In one example, a set of sensor data can include GPS-dcrivcd data, data
representing magnetic
flux, data representing rotation (e.g., as derived by a gyroscope), and any
other data that can be
relevant to inference engine 1104 in its operation. The inference engine can
use positional data
along with motion-related information to identify an activity or mode, among
other purposes.
According to some embodiments, inference engine 1104 can be configured to
analyze real-
time sensor data, such as user-related data 1101 derived in real-time from
sensors and/or
environmental-related data 1103 derived in real-time from sensors. In
particular, inference engine
1104 can compare any of the data derived in real-time (or from storage)
against other types of data
(regardless of whether the data is real-time or archived). The data can
originate from different
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sensors, and can obtained in real-time or from memory as user data 1152.
Therefore, inference
engine 1104 can be configured to compare data (or sets of data) against each
other, thereby Matching
sensor data, as well as other data, to determine an activity or mode.
Diagram 1100 depicts an example of an inference engine 1104 that is configured
to determine
an activity in which the user is engaged, as a function of motion and, in some
embodiments, as a
function of sensor data, such as user-related data 1101 derived from sensors
and/or environmental-
related data 1103 derived from sensors. Examples of activities that inference
engine 1104 evaluates
include sitting, sleeping, working, running, walking, playing soccer or
baseball, swimming, resting,
socializing, touring, visiting various locations, shopping at a store, and the
like. These activities may
be associated with different motions of the user, and, in particular,
different motions of one or more
locomotive members (e.g., motion of a user's arm or wrist) that are inherent
in the different
activities. For example, a user's wrist motion during running may be more
"pendulum-like" in its
motion pattern, whereas, the wrist motion during swimming (e.g., freestyle
strokes) may be more
"circular-like" in its motion pattern. Diagram 1100 also depicts a motion
matcher 1120, which is
IS configured to detect and analyze motion to determine the activity (or
the most probable activity) in
which the user is engaged. To further refine the determination of the
activity, inferente engine 1104
includes a user characterizer 1110 and an environmental detector 1111 to
detect sensor-data for
purposes of comparing subsets of sensor data (e.g., one or more types of data)
against other subsets
of data. Upon determining a match between sensor data, inference engine 1104
can use the matched
sensor data, as well as motion-related data, to identify a specific activity
or mode. User characterizer
1110 is configured to accept user-related data 1101 from relevant sensors.
Examples of user-related
data 1101 include heart rate, body temperature, or any other personally-
related information with
which inference engine 1104 can determine, for example, whether a user is
sleeping or not. Further,
environmental detector 1111 is configured to accept environmental-related data
1103 from relevant
sensors. Examples of environmental-related data 1103 include time, ambient
temperature, degree of
brightness (e.g., whether in the dark or in sunlight), location data (e.g.,
GPS data, or derived from
wireless networks), or any other environmental-related information with which
inference engine
1104 can determine whether a user is engaged in a particular activity.
A band can operate in different modes of operation. One mode of operation may
be an
"active mode." Active mode can be associated with activities that involve
relatively high degrees of
motion at relatively high rates of change. Thus, a band enters the active mode
to sufficiently capture
and monitor data with such activities, with conservation of power consumption
as being less critical.
In this mode, a controller, such as mode controller 1102, operates at a higher
sample rate to capture
the motion of the band at, for example, higher rates of speed. Certain safety
or health-related
monitoring can be implemented in active mode, or, in response to engaging in a
specific activity.
For example, a controller of a band can monitor a user's heart rate against
normal and abnormal heart
rates to alert the user to any issues during, for example, a strenuous
activity. In some embodiments,
a band can be configured as set forth in FIG. 5B and user characterizer 1110
can process user-related
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information from sensors described in relation to FIG. 5B. Another mode of
operation may be a
"sleep mode." Sleep mode can be associated with activities that involve
relatively low degrees of
motion at relatively low rates of change, or with particular types of motion
(e.g., related to breathing,
tossing and turning, snoring, and other types of motion related to sleep).
Thus, a band enters the
sleep mode to sufficiently capture and monitor data associated with such
activities, for example,
while preserving power. In some embodiments, a band can be configured as set
forth in FIG. 5C and
user characterizer 1110 can process user-related information from sensors
described in relation to
FIG. 5C. Yet another mode may be "normal mbdc," in which the band operates in
accordance with
typical user activities, such aS during work (e.g., typing, standing, sitting,
carrying a light object,
walking a short distance, and other activities associated with work), travel
(e.g., driving, boarding a
train, holding a newspaper, carrying a bag or briefcase, and other activities
associated with travel),
movement around the house, bathing, a daily chore (e.g., vacuuming, washing a
dish, making a bed,
writing a letter or e-mail, wiping a surface, and other activities associated
with a daily chore),
walking the dog, and other activities. A band can operate in any munber of
different modes,
including a health monitoring mode, which can implement, for example, the
features set forth in FIG.
5D, or a "social mode" of operation in Which the user interacts with other-
users of similar bands or
communication devices, and, thus, a band can implement, for example, the
features set forth in FIG.
5E. Any of these modes can be entered or exited either explicitly. (e.g.,
using motion signatures,
buttons, or other forms of input, as described herein) or implicitly. In still
other examples, a band
may operate in different modes using different types of sensor data than those
described herein.
Diagram 1100 also depicts a motion matcher 1120, which is configured to detect
and analyze
motion to determine the activity (or the most probable activity) in which the
user is engaged. In
various embodiments, motion matcher 1120 can form part of inference engine
1104 (not shown), or
can have a structure and/or function separate therefrom (as shown).
Regardless, the structures and/or
functions of inference engine 1104, including user characterizer 1110 and
environmental detector
1111, and motion matcher 1120 may cooperate to determine an activity in which
the user is engaged
and transmit data indicating the activity (and other related information) to a
controller (e.g., a mode
controller 1102) that is configured to control operation of a mode, such as an
"active mode," of the
band.
Motion matcher 1120 of FIG. II may include a motion/activity deduction engine
1124,. a
motion capture manager 1122 and a motion analyzer 1126. Motion matcher 1.120
can receive
motion-related data 1103 from relevant sensors, including those sensors that
relate to space or
position and to time. Examples of such sensors include accelerometers, motion
detectors,
velocimeters, altimeters, barometers, or other sensors. A wide variety of
sensors may be
.35 implemented to provide motion-related data 1103 to motion matcher 1120.
Motion capture- manager
1122 may be configured to capture portions of motion, and to aggregate those
portions of motion to
form an aggregated motion pattern or profile. Further, motion capture manager
1122 may be
configured to store motion patterns as profiles 1144 in database 1140 for real-
time or future analysis
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or use. As described in more detail below, these motion profiles may be used
as templates for future
reference, either by the user that created the profile or by other users.
Motion profiles 1144 may
include sets, of data relating to instances of motion or aggregated portions
of motion (e.g., as a
function of time and space, such as expressed in X, Y, Z coordinate systems).
For example, motion capture manager 1122 may be configured to capture motion
relating to
the activity of walking and motion relating to running, each motion being
associated with a specific
profile 1144. To illustrate, consider that motion profiles 1144 of walking and
running share some
portions of motion in common. Ear example, the user's wrist motion during
running and walking
share a "pendulum-like" pattern over time, but differ in sampled positions of
the band. During
walking, the wrist and band is generally at waist-level as the user walks with
arms relaxed (e.g.,
swinging of the arms during walking can result in a longer arc-like motion
pattern over distance and
time), whereas during running, a user typically raises the wrists and changes
the orientation of the .
band (e.g., swinging of the arms during running can result in a shorter are-
like motion pattern).
Motion/activity deduction engine 1.124 may be configured to access profiles
1144 and deduce, for
example, in real-time whether the activity is walking or running.
Motion/activity deduction engine 1124 may be configured to analyze a portion
of motion and
deduce the activity (e.g., as an aggregate of the portions of motion) in which
the user is engaged and
provide that information to the inference engine 1104, which, in turn,
compares user characteristics
and environmental characteristics against the deduced activity to confirm or
reject the determination.
For example, if motion/activity deduction engine 1124 deduces that monitored
motion indicates that
the user is sleeping, then the heart rate of the user, as a user
characteristic, can be used to compare
against thresholds in user data 1152 of database 1150 to confirm that the
user's heart rate' is
consistent with a sleeping user. User data 1152 may also include past location
data, whereby historic
location data can be used to determine whether a location is frequented by a
user (e.g., as a means of
identifying the user). Further, inference engine 1104 may be configured to
evaluate environmental
characteristics, such as whether there is ambient light (e.g., darkness
implies conditions for resting),
the time of day (e.g., a person's sleeping times typically can be between 12
midnight-and 6 am), or
other related information.
In operation, motion/activity deduction engine 1124 may be configured to store
motion-
related data to form motion profiles 1144 in real-time (or near real-time). In
some embodiments, the
motion-related data can be compared against motion reference data 1146 to
determine "a match" of
motions. Such a match may be sufficiently similar or it may be exact,
depending on the context.
Motion reference data 1146, which includes reference motion profiles (i.e.,
motion profile templates)
and patterns, may be derived by motion data captured for the user during
previous activities,
whereby the previous activities and motion thereof serve as a reference
against which to compare.
Motion reference data 1146 also may include ideal or statistically-relevant
motion patterns against
which motion/activity deduction engine 1124 determines a match by determining
which reference
profile data 1146 "best fits" the real-time motion data. As used herein,
"reference motion profiles"
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and "motion profile templates" arc used interchangeably to refer to a
predetermined set of motion
data. in some examples, motion/activity deduction engine 1124 can operate to
determine a motion
pattern, and, thus, determine an activity. Note that motion reference profile
data 1146, in some
embodiments, serves as a "motion fingerprint" for a user and can be unique and
personal to a specific
user. Therefore, motion reference profile data 1146 can be used by a
controller to determine whether
subsequent use of a band is by the authorized user or whether the current
user's real-time motion
data is a mismatch against motion reference profile data 1146. If there is
mismatch, a controller can
activate a security protocol responsive to the unauthorized use to preserve
information or generate an
alert to be communicated external to the band.
Motion analyzer 1126 may be configured to analyze motion, for example, in real-
time,
among other things. For example, lithe user is swinging a baseball bat or golf
club (e.g., when the
band is located on the wrist) or the user is kicking a soccer ball (e.g., when
the band is located on the
ankle), motion analyzer 1126 evaluates the captured motion to detect, for
example, a deceleration in
motion (e.g., as a motion-centric event), which can be indicative of an
impulse event, such as striking
an object, like a golf ball. Motion-related characteristics, such as space and
time, as well as other
environment and user characteristics can be captured relating to the motion-
centric event. A motion-
centric event, for example, is an event that can relate to changes in position
during motion, as well as
changes in time or velocity. In some embodiments, inference engine 1104 stores
user characteristic
data and environmental data in database 1150 as user data 1152 for archival
purposes, reporting
purposes, or any other purpose. Similarly inference engine 1104 and/or motion
matcher 1120 can
store motion-related data as motion data 1142 for real-time and/or future use
(e.g., as a template).
According to some embodiments, stored data can be accessed by a user or any
entity (e.g., a third
party) to adjust the data of databases 1140 and 1150 to, for example, optimize
motion profile data or
sensor data to ensure more accurate results. In an example, a user may access
motion profile data in
database 1150. In another example, a user may adjust the.fiinctionality of
inference engine 1104 to
ensure more accurate or precise determinations. For example, if inference
engine 1104 detects a
user's walking motion as a running motion, the user may modify the behavior of
the logic in the
band to increase the accuracy and optimize the operation of the band. A user
may make the above-
described adjustments in various ways (e.g., direct programming, downloaded
software modules or
applications, etc.). According to other embodiments, motion profiles may be
stored as templates
available for access by a user or any entity (e.g., a third party) to compare
and hone a user's activity
motions.
FIG. 12 depicts a representative implementation of one or more bands and
equivalent
devices, as wearable devices, to form unique motion profiles. In diagram I
200, bands and an
equivalent device are disposed on locomotive members of the user, whereby the
locomotive
members facilitate motion relative to and about a center point 1230 (e.g., a
reference point for a
position, such as a center of mass). A headset 1210 may be configured to
communicate with bands
1211, 1212, 1213 and 1214 and is disposed on a body portion 1202 (e.g., the
head), which is subject
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to motion relative to center point 1230. Bands 1211 and 1212 may be disposed
on locomotive
portions 1204 of the uscr (e.g., the arms or wrists), and bands 1213 and 1214
may be disposed on
locomotive portion 1206 of the user (e.g., the legs or ankles), as shown. Also
as shown, headset
1210 may be disposcd at distance 1220 from center point 1230, bands 1211 and
1212 are disposed at
distance 1222 from center point. 1230, and bands 121.3 and 1214 are disposed
at distance 1224 from
center point 1230. A great number of users have different values of distances
1220, 1222, and 1224.
Further, different wrist-to-elbow and elbow-to-shoulder lengths for different
users affect the relative
motion of bands 1211 and 1212 about center point 1230, and similarly,
different hip-to-knee and
knee-to-ankle lengths for different users affect the relative motion of bands
1213 and 1214 about
center point 1230. Moreover, a great number of users have unique gaits and
styles of motion. The
above-described factors, as well as other factors, may facilitate the
determination of a unique motion
profile for a user per activity (or in combination of a number of activities).
The uniqueness of the
motion patterns in which a user performs an activity enables the use of motion
profile data to provide
a "motion fingerprint." A "motion fingerprint" is unique to a user and can be
compared against
detected motion profiles to determine, for example, whether a use of the band
by a subsequent
wearer is unauthorized. In some cases, unauthorized users do not typically
share common motion
profiles. Note that while four are shown, fewer than four can be used to
establish a "motion =
fingerprint," or more can be shown (e.g., a band can be disposed in a pocket
or otherwise carried by
the user). For example, a user can place a single bands at different portions
of the body to capture
motion patterns for those body parts in a serial fashion. Then, each of the
motions patterns can be
combined to form a "motion fingerprint." In some cases, a single band 1211 is
sufficient to establish
a "motion fingerprint." In other cases, one or more of bands 1211, 1212, 1213
and 1214 may be .
configured to operate with multiple users, including non-human users, such as
pets or other animals.
FIG. 13 depicts an example of a motion capture manager configured to capture
motion and
portions therefore. Diagram 1300 depicts an example of a motion matcher 1360
and/or a motion
capture, manager 1361, one or both of which are configured to capture motion
of an activity or state
of a user and generate one or more motion profiles, such as motion profile
1302 and motion profile
1352. Database 1370 is configured to store motion profiles 1302 and 1352. Note
that motion
profiles 1302 and 1352 arc shown as graphical representation of motion data
for purposes of
discussion, and can be stored in any suitable data structure or arrangement.
Note, too, that motion
profiles 1302 and 1352 can represent real-time motion data with which a motion
matcher 1360 uses
to determine modes and activities.
To illustrate operation of motion capture manager 1361, consider that motion
profile -1302
represents motion data captured for a running or walking activity. The data of
motion profile 1302
indicates the user is traversing along the Y-axis with motions describable in
X,17, Z coordinates or
any other coordinate system. The rate at which motion is captured along the Y-
axis is based on the
sampling rate and includes a time component. For a band disposed on a wrist of
a user, motion
capture manager 1361 captures portions of motion, such as repeated motion
segments A-to-B and B-
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to-C. In particular, .motion capture manager 1361 is configured to detect
motion for an arm 1301a in
the +Y direction from the beginning of the forward swinging arm (e.g., point
A) to the end of the-
foRvard swinging arm (e.g., point B). Furthecmotion capture manager 1361 is
configured to detect
motion for arm 1301b in the -Y direction from the beginning of the backward
swinging arm (e.g.,
point B) to the end of the backward swinging arm (e.g., point C). Note that
point C is at a greater
distance along the Y-axis than point A as the center point or center mass of
the user has advanced in
the +Y direction. Motion capture manager 1361 continues to monitor and capture
motion until, for
example, motion capture manager 1361 detects no significant motion (i.e.,
below a threshold) or an
activity or mode is ended.
In some embodiments, a motion profile can be captured by motion capture
manager 1361 in a
"normal mode" of operation and sampled at a first sampling rate ("sample rate
1") 1332 between
samples of data 1320, which is a relatively slow sampling rate that is
configured to operate with
normal activities. Samples of data 1320 represent not only motion data (e.g.,
data regarding X, Y,
and Z coordinates, time, accelerations, velocities, etc.), but canalso
represent or link to user related
information captured at those sample times. According to some embodiments,
motion matcher 1360
analyzes the motion; and, if the motion relates to an activity associated with
an "active mode,"
motion matcher 1360 signals to a controller, such as a mode controller, to
change modes (e.g., from
normal to active mode). During active mode, the sampling rate increases to a
second sampling rate
("sample rate 2") 1334 between samples of data 1320 (e.g., as well as between
a sample of data 1320
and a sample of data 1340). An increased sampling rate can facilitate,.for
example, a more accurate
set of captured motion data. To illustrate the above, consider that a user is
sitting or stretching prior
to a work out. The user's activities likely arc occurring in a normal mode of
operation. But once
motion data of profile 1302 is detected, a motion/activity deduction engine
can deduce the activity of
running, and then can infer the mode ought to be the active mode. The logic of
the band then can
place the band into the active mode. Therefore, the band can change modes of
operation implicitly
(i.e., explicit actions to change modes need not be necessary). In some cases,
a mode controller can
identify an activity as a "running" activity, and then invoke- activity-
specific functions, such as an
indication (e.g., a vibratory indication) to the user every one-quarter mile
or 15 minute duration
during the activity.
FIG. 13 also depicts another motion profile 1352. Consider that motion profile
1352
represents motion data captured for swimming activity (e.g., using a freestyle
stroke). Similar to
profile 1302, the motion pattern data of motion profile 1,352 indicates the
user is traversing along the
Y-axis. The rate at which motion is captured along the Y-axis is based on the
sampling rate of
samples 1320 and 1340, for example. For a band disposed on a wrist of a user,
motion capture
manager 1361 captures the portions of motion, such as motion segments A-to-B
and B-to-C. In
. particular, motion capture manager 1361 is configured to detect motion
for an arm 135Ia in the +Y
direction from the beginning of a forward arc (e.g., point A) to the end of
the forward arc (e.g., point
B). Further, motion capture manager 1361 is configured to detect motion for
arm 135 lb in the -Y
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direction from the beginning of reverse arc (e.g., point B) to the end of the
reverse arc (e.g., point C).
Motion capture manager 1361 continues to monitor and capture motion until, for
example, motion
capture manager 1361 detects no significant motion (i.e., below a threshold)
or an activity or mode is
ended.
In operation, a mode controller can determine that the motion data of profile
1352 is
associated with an active mode, similar with the above-described running
activity, and can place the
band into the active mode, if it is not already in that mode. Further, motion
matcher 1360 can
analyze the motion pattern data of profile 1352 against, for example, the
motion data of profile 1302
and conclude that the activity associated with the data being captured for
profile 1352 does not relate
to a running activity. Motion matcher 1360 then can analyze profile 1352 of
the real-time generated
motion data, and, if it determines a match with reference motion data for the
activity of swimming,
motion matcher 1360 can generate an indication that the user is performing
"swimming" as an
activity. Thus, the band and its logic can implicitly determine an activity
that a user is performing
(i.e., explicit actions to specify an activity need not be necessary).
Therefore, a mode controller then
IS can invoke swimming-specific functions, such as an application to
generate an indication (e.g., a
vibratory indication) to the user at completion of every lap, or can count a
number of strokes. In
some embodiments, motion matcher 1360 and/or a motion capture manager 1361 can
be configured
to implicitly determine modes of operation, such as a sleeping mode of
operation (e.g., the mode
controller, in part, can analyze motion patterns against a motion profile that
includes sleep-related
motion data) (not shown). Motion matcher 1360 and/or a motion capture manager
1361 also may be
configured to determine an activity out of a number of possible activities.
FIG. 14 depicts an example of a motion analyzer configured to evaluate motion-
centric
events. Diagram 1400 depicts an example of a motion matcher 1460 and/or a
motion analyzer 1466
for capturing motion of an activity or state of a user and generating one or
more motion profiles,
such as a motion profile 1402. To illustrate, consider that motion profile
1402 represents motion
data captured for an activity of swinging a baseball bat 1404. The motion
pattern data of motion
profile 1402 indicates the user begins the swing at position 1404a in the -Y
direction. The user
moves the band and the bat to position 1404b, and then swings the bat toward
the -Y direction when
contact is made with the baseball at position 1404c. Not that the set of data
samples 1430 includes
data samples 1430a and 1430b at relatively close proximity to each other in
profile 1402. This
indicates a deceleration (e.g., a slight, but detectable deceleration) in the
bat when it hits the baseball.
Thus, motion analyzer 1466 can analyze motion to determine motion-centric
events, such as striking
a baseball, striking a golf ball, or kicking a soccer ball. Data regarding the
motion-centric events can
be stored in database 1470 for additional analysis or archiving purposes, for
example.
In some examples, multiple motion profiles (e.g., motion profiles 1302, 1352
and 1402) may
be created for an activity type. For example, different motion profiles may be
created for various
types of running (e.g., a light jog, a sprint, short distance, long distance,
competitive running,
leisurely running, etc.). In other examples, different motion profiles may be
created for different
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swim strokes, riding different types of bicycles (e.g., mountain vs. road),
different swings of a bat,
swings of different golf clubs, etc. Motion reference data 1146 may include
reference motion
profiles or patterns for all of these variances for each activity type.
FIG. 15 illustrates an exemplary data-capable band system configured to create
and share
motion profile templates. System 1500 includes band 1510, one or more networks
1520, computer
1522, laptop 1524, mobile communications device 1526, and mobile computing
device 1,528. The
elements in system 1500 may be implemented as described above with respect to
corresponding
elements in FIG. 1. Band 1510 is depicted as including motion capture manager
1561 and memory
1562, which includes motion profile template 1560. In some examples, triernory
1562 may be
implemented to store multiple motion profile templates. The elements in band
1510 also may be
implemented as described above with respect to corresponding elements in FIGS.
11, 13 and 14. In
some examples, a user may choose to store a motion profile as motionprofile
template 1560. For
example, a user wearing band 1510 may have a particularly successful golf
swing for a particular
hole at a particular golf course. Motion capture manager 1561 may capture that
golf swing as motion
profile template 1560 and store it in memory 1562 for future reference. The
user may measure and
compare future golf swings against motion profile template 1560. In some
examples, band 1510
may share that stored motion profile template with any of thedther devices or
networks in system
1500, or with another band (not shown). Band 1510 may do so using any wired or
wirelesS,
communication options, as described in more detail above. In some examples,
band 1510 may share
this information with other users through applications implemented on any of
the data and
communications capable devices depicted in system 1500 (e.g., networks 1520,
computer 1522,
- laptop 1524, mobile communications device 1526, and mobile computing device
1528). The other
users may then download motion profile template 1560 onto their bands (not
shown), and use motion
profile template 1560 as a reference for their golf swings.
Likewise, a user Wearing band 1510 may obtain (e.g., download) motion profile
templates
created by other users onto band 1510 to use as a reference for their own
activities. For example, a
user may obtain a motion profile template created by an instructor of, expert
in, or professional of, an
activity (e.g., a tennis instructor or professional athlete). In another
example, friends or colleagues
may share motion profile templates for competitions associated with any sport
or activity (e.g.,
golfing, running, swimming, cycling, driving, walking, climbing, typing,
sleeping). In yet other
examples, users may share motion profile templates for instructional or
recreational uses.
In other embodiments, expert, ideal or instructional motion profile templates
may be provided
through an application (e.g., software application, online store or
marketplace, etc.) (not shown). In
some examples, expert, ideal or instructional motion profile templates may be
implemented with a
feedback and/or reward system, which may offer a user incentives (e.g.,
points, real or virtual coins,
gifts, etc.), encouragement, or offers (e.g., discounts on products or
services related to the activity,
access to exclusive events, etc.) associated with a user's improvement in
reference to a motion
profile template. The application may be implemented on any of the data and
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capable devices depicted in system 1500 (e.g., networks 1520, eomputer 1522,
laptop 1524, mobile
communications device 1526, and mobile computing device 1528). In some
examples, the
application may enable the upload of motion profile templates for sharing. In
other examples, an
application may enable the creation of motion profile templates using textual
or other human-
.
readable input. As used herein, "human-readable" refers to any text, graphic,
noise, texture, or other
format that may be sensed (e.g., read, seen, felt, heard, or otherwise sensed)
by a human.
In other embodiments, motion profile templates may be used to monitor and/or
correct
behaviors. For example, motion profile template 1560 may be implemented with.
other modules,
programs or applications (not shown) to detect an alcoholic's drinking habit
or a smoker's smoking
habit. In some examples, band 1510 may be configured to provide negative
feedback when it
determines that a user is drinking alcohol or smoking. Band 1510 also may be
configured to provide
positive feedback when a user goes for certain periods of time without
drinking alcohol or smoking.
In still other embodiments, band 1510 may be used with the exemplary
identification and security
systems described below to control a variety of devices personal to the user
of band 1510.
. 15 FIG. 16A illustrates an exemplary system for wearable device data
security. Exemplary
system 1600 comprises network 102, band 112, and server 114. In some example,
band 112 may
capture data that is personal, sensitive, or confidential, as described
herein. In some examples,
security protOtols and algorithms, as described herein, may be implemented on
band 112 to
authenticate a user's identity and authorize access to band 112. As used
herein, "authenticate" or
"authentication" refers to confirming, or the confirmation of, a user's
identity. Examples of security
protocols and algorithms include authentication, encryption, encoding, private
and public key
infrastructure, passwords, checksums, hash codes and hash functions (e.g..,
SHA, SHA-1, MD-5, and
the like), or others may be used tci prevent undesired access to data captured
by band 112. In other
examples, authentication of a user's identity for band 112 may be implemented
differently. This
authentication may be implemented to prevent unwanted use or access by others.
In other exaMples,
the security protocols and algorithms may be performed by server I 14, in
which case band 112 may
communicate with server 114 via network 102 to authenticate a user's identity.
Use of the band to
capture, evaluate or access a user's data, as described herein, may be
predicated on authentication of
the user's identity.
In some examples, band 112 may identify of a user by the user's unique pattern
of behavior
or motion. Band 112 may capture and evaluate data from a user to create a
unique key personal to
the user (e.g., based upon a user's characteristic motion). In some examples,
the key may be
associated with an individual user's physical attributes, including gait,
biometrie or physiological
signatures (e.g., resting heart rate, skin temperature, salinity of emitted
moisture, etc.), or any other
sets of data that may be captured by band 112, as described in more detail
above. In some examples,
the key may be based upon a set of physical attributes that are known in
combination to be unique to
a user. Once the key is created based upon the predetermined, or pre-
programmed, set of physical
attributes, it may be used in an authentication process to authenticate a
user's identity, and to prevent
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access to, or capture and evaluation of, data by an unauthorized uscr. For
example, if an
unauthorized user puts on band 112 and starts performing an activity, band 112
may be unable to
authenticate use by this unauthorized user, and may shut off, or otheivise
enter a locked mode in
which band 112 does not collect data, and data stored in band 112 may not be
accessed (e.g.,
downloaded, viewed, or otherwise accessed).
In some examples, authentication using the key may be carried out directly by
band 112. In
other examples, band 112 may be used with other bands (not shown) that may be
owned by the same
individual (i.e., user) to authenticate a user's identity. For example,
multiple bands that arc owned
by the same individual may be configured for different sensors or types of
activities, but may also be
configured to share data with each other, or otherwise work together, to carry
out an authentication
of a user's identity. In order to prevent unauthenticated or unauthorized
individuals from accessing a
given user's data, band 112 may be configured using various types of
authentication, identification,
or other security techniques among one or more bands, including for example
handl 12. As an
example, band 112 may be in direct data communication with other bands (not
shown) or indirectly
through an authentication system or service, for example implemented using
server 114. In still
other examples, band 112 may send data to server 114, which in turn carries
out an authentication
and returns a prompt, or other notification, to band 112 to unlock, or
otherwise provide access to,
band 112 for use. In other examples, data security and identity authentication
for band 112 may be
implemented differently.
FIG. 16B illustrates an exemplary system for media device content management
using
sensory input. Here, system 1660 includes band 1612, sensors 1614-1620, data
connection 1622,
media device 1624, and playlists 1626-1632. As used throughout this
description, band 1612 may
also be referred to interchangeably as a "wearable device." Sensors 1614-1620
may be implemented
using any type of sensor such as a 2 or 3-axis accelerometer, temperature,
humidity, barometric
pressure, skin resistivity (i.e., galvanic skin response (GSR)), pedometer, or
any other type of sensor,
without limitation. Data connection 1622 may be implemented as any type of
wired or wireless
connection using any type Of data communication protocol (e.g., Bluctooth,
wireless fidelity (i.e.,
WiFi), LAN, WAN, MAN, near field communication (NFC), or others, without
limitation) between
band 1612 and media device 1624. Data connection 1622 may be configured to
transfer data bi-
directionally or in a single direction between media device 1624 and band
1612. In some examples,
data connection 1622 may be implemented by using a 3.5min audio jack (e.g.,
TRRS-type, TRS-
type, or other type of connector) that connects to an appropriate plug (i.e.,
outlet) and transmits
electrical signals that may be interpreted for transferring data.
Alternatively, a wireless radio,
transmitter, transceiver, or the like may be implemented with band 1,612. In
some examples, when a
motion is detected via an installed accelerometer on the band 1612, a
transmission of a control signal
to media device 1624 may be initiated to, for example, begin playing playlist
1630, change from
playlist 1630 to another playlist (e.g., playlists 1626-1628 or 1632), forward
to another song on
playlist 1630, and the like.
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As shown, media device 1624 may be any type of device that is configured to
display, play,
interact, show, or otherwise present various types of media, including audio,
visual, graphical,
images, photographical, video, rich media, multimedia, or a combination
thereof, without limitation.
Examples of media device 1624 may include audio playback devices (e.g.,
players configured to play
various formats of audio and video files including .nip3, .wav, and others,
without limitation),
connected or wireless (e.g., Bluctooth, WiFi, W1_,A1\1, and others, as
described.herein) speakers,
radios, audio devices installed on portable, desktop, or mobile computing
devices, and other devices.
In some examples, playlists 1626-1632 may be configured to play various types
of files of various
formats, as representatively illustrated by "File 1, File 2, File 3" in
association with each playlist.
Each file on a given playlist may be any type of media and played using
various types of formats or
applications implemented on media device 1624.
As an example, sensors 1614-1620 may detect various types of inputs locally
(i.e., on band
1612) or remotely (i.e., on another device that is in data communication with
band 1612) such as an
activity or motion (e.g., running, walking, swimming, jogging, jumping,
shaking, turning, cycling, or
others), a biological state (e.g., healthy, ill, diabetic, awake, asleep, or
others), a physiological state
normal gait, limping, injured, sweating, high heart rate, high blood pressure,
or others), or a
psychological state (e.g., happy, depressed, angry, and the like). Other types
of inputs may be sensed
by sensors 1614-1620, which may be configured to gather data and transmit that
information to an
application that uses the data to infer various conclusions related to the
above-described states or
activities, among others. in some examples, each of sensors 1614-1620 may
comprise a plurality, or
a set, of individual sensors, each configured to capture data associated with
a particular parameter
associated with an activity, a biological state, a physiological state, or a
psychological state. Based
on the data gathered by sensors 1614-1620 and, in some examples, user or
system-specified
parameters, band 1612 may be configured to generate control signals (e.g.,
electrical or electronic
signals that are generated at various types or amounts of voltage in order to
produce, initiate, trigger,
or otherwise cause certain actions or functions to occur). For example, ,data
may be transferred from
=
sensors 1614-1620 to band 1612 indicating that a user has started running.
Band 1612 may be
configured to generate a control signal to media device 1624 over data
connection 1622 to initiate
playing files in a given playlist in order. A shake of a user's wrist, for
example, in a given direction
or axis may cause band 1612 to generate a different control signal that causes
media device 1624 to
change the play order, to change files, to forward to another file, or to
initiate some other action.. In
some examples, a given movement (e.g., a user shakes her wrist on which band
1612 is worn) may
be resolved into data associated with motion occurring along each of 3-
different axes. Band 1612
may be configured to detect motion using an accelerometer (not shown), which
then resolves the
detected motion into data associated with three separate axes of movement,
translated into data or
electrical control signals that may be stored in a memory that is local and/or
remote to band 1612.
Further, the stored data of a given motion may be associated with a specific
action such that, when
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detected, control signals may be generated by band 1612 and sent over data
connection 1622 to
media device 1624 or other types of devices, without limitation.
As another example, if sensor 1616 detects that a user is lying prone and her
heart rate is
slowing (e.g., decelerating towards a previously-recorded resting heart rate),
a control signal may be
generated by band 1612 to begin playback of a song appropriate for bedtime
(e.g., Brahms' Lullaby,
another lullaby, or other desired bedtime song) using, for example, a
Bluetooth-connected headset
speaker (i.e., media device 1624). In yet another example, if sensor 1618
detects a physiological
state change (e.g., a user is walking with a gait or limp as opposed to
normally observed
physiological behavior), media device 1624 may be controlled by band 1612 to
initiate playback of a
file on a graphical user interface of a connected device (e.g., a mobile
computing or communications
device) that provides a tutorial on running or walking injury treatment,
recovery and/or prevention.
As yet another example, if sensor 1620 detects one or more parameters that a
user is happy (e.g.,
sensor 1620 detects an accelerated, but regular heart rate, rapid or erratic
movements, increased body
temperature, increased speech levels, and the like), band 1612 may send a
control signal to media
device 1624 to display an inquiry as to whether the user wishes to hear songs
played from her "happy
playlist" (not shown). The above-described.examples are provided for purposes
of illustrating the
use of managing various types of media and media content using band 1612, but
litany others may be
implemented without restriction to those provided.
FIG. 16C illustrates an exemplary system for device control using sensory
input. Here,
system 1640 includes band 1612, sensors 1614-620, data connection 1642, and
device types 1644-
1654. Those elements shown that are like-named and numbered may be designed,
implemented, or
configured as described above or differently. As shown, the detection by band
1612 of a given
activity, biological state, physiological state, or psychological state may be
gathered as data from
sensors 1614-1620 and used to generate various types of control signals.
Control signals, in some
examples, may be transmitted via a wired or wireless data connection (e.g.,
data connection 1642) to
one or multiple device types 1644-1654 that are in data communication with
band 1612. Device
types 1644-1654 may be any type of device, apparatus, application, or other
mechanism that may be
in data connection with, coupled to (indirectly or directly), paired (e.g.,
via ,Bluctooth or another data
communication protocol), or otherwise configured to receive control signals
from band 1612.
As shown, band 1612 may send control signals to various types of devices
(e.g., device types
1644-1654), including payment systems (1644), environmental (1646), mechanical
(1648), electrical
(1650), electronic (1652), award (1654), and others, without limitation. In
some examples, band
1612 may be associated with an account to which a user may link a credit card,
debit card, or other
type of payment account that, when properly authenticated, allows for the
transmission of data and
control signals (not shown) over data connection 1642 to payment system (i.e.,
device) 1644. In
other examples, band 1612 may be used to send data that can be translated or
interpreted as control
signals or voltages in order to manage environmental control systems (e.g.,
heating, ventilation, air
conditioning (HVAC), temperature, air filter (e.g., hepa, pollen, allergen),
humidify, and others,
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without limitation). Input detected from one or more of sensors 1614-1620 may
be transformed into
data received by band 1612. Using firmware, application software, or other
user or system-specified
parameters, when data associated with input from sensors 1614-1620 are
received, control signals
may be generated and sent by band 1612 over data connection 1642 to
environmental control system
30 As another example, a user may have an account associated with band 1612
and enrolls in a
participatory fitness program that, upon achieving certain milestones, results
in the receipt of an
award or promotion. For example, sensor 1614 may detect that a user has
associated his account
with a program to receive a promotional discount towards the purchase of a
portable Bluetooth
communications headset. However, the promotion may be earned once the user has
completed,
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system 1654, which may be configured to retrieve the desired promotion from
another database (e.g.,
a promotions database, an advertisement server, an advertisement network, or
others) and then send
the promotion electronically back to band 1612 for further display or use
(e.g., redemption) on a
device in data connection with band 1612 (not shown). Other examples of the
above-described
= 5 device types and other device types not shown or described may be
implemented and are not limited
to those provided.
FIG. 16D illustrates an exemplary system for movement languages in wearable
devices.
Here, system 1660 includes band 1612, sensors 1614-1620, data connection 1622,
pattern/movement
language library (i.e., pattern library) 1664, movement patterns (i.e.,
patterns) 1666-1672, data
connection 1674, and server 1676. In some examples, band 1612 may be
configured to compile a
"movement language" that may be stored in pattern library 1664, which can be
either local (i.e.. in
memory on band 1612) or remote (i.e., in a database or other data storage
facility that is in data
connection with band 1612, either via wired or wireless data connections). As
used herein, a
"movement language" may refer to the description of a given movement as one or
more inputs (e.g.,
sensory, manual, or other inputs) that may be transformed into a discrete set
of data that, when
observed again, can be identified as correlating to a given movement. In some
examples, a
movement may be described as a collection of one or more motions. In other
examples, biological,
psychological, and physiological states or events may also be recorded in
pattern library 1664.
These various collections of data may be stored in pattern library 1664 as
patterns 1666-1672. In
some examples, a movement or pattern (e.g., patterns 1666-1672) may be unique
to a user. In other
examples, a movement or pattern (e.g., patterns 1666-1672) may be common or
characteristic to a
group of users (e.g., male, female, tall, short, old, young, athletic, obese,
paraplegic, runner,
swimmer, cyclist, and other groups).
A movement, when detected by an accelerometer (not shown) on band 1612, may be
associated with a given data set and used, for example, to perform one or more
functiOns when
detected again. Parameters may be specified (i.e., by either a user or system
(i.e., automatically or
semi-automatically generated)) that also allow for . tolerances .to determine
whether a given
movement falls within a given category (e.g., jumping may be identified as a
set of data that has a
tolerance of +/- .5 meters for the given individual along a z-aXis as input
from a 3-axes
accelerometer).
Using the various types of sensors (e.g., sensors 1614-1620), different
movements, motions,
moods, emotions, physiological, psychological, or biological events can be
monitored, recorded,
stored, compared, and used for other functions by band 1612. Further,
movements may also be
downloaded from a remote location (e.g., server 1676) to band 1612. Input
provided by sensors
1614-1620 and resolved into one or more of patterns 1666-1672 and used to
initiate or perform one
or more functions, such as authentication (FIG. 16A), playlist management
(FIG. 16B), device
control (FIG. 16C), among others. In other examples, systems 1610, 1640, 1660
and the respective
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above-described elements may be varied in design, implementation,
configuration, function,
structure, or other aspects and are not limited to those provided.
FIG. 17A illustrates an exemplary process for media device content management
using
sensory input. Here, process 1700 begins by receiving an input from one or
more sensors that may
be coupled to, integrated with, or arc remote from (i.e., distributed on other
devices that arc in data
communication with) a wearable device (1702). The received input is processed
to determine a
pattern (1704). In some examples, processing received sensory input may
include aggregating the
input into a set of inputs, categorizing the input into various categories of
data, parsing the input,
running an algorithm on the input, copying the input, tagging the input, or
otherwise processing the
input, without limitation. Once a pattern has been determined, then a compare,
lookup, or other
reference operation may be performed against a pattern library (i.e., a
database or other storage
facility configured to storc data associated with one or more patterns)
(1706). As used herein,
."pattern library" may be used to store patterns associated with movements,
motion, moods, states,
activities', events, or any other grouping of data associated with a pattern
as determined by evaluating
input from one or more sensors coupled to a wearable device (e.g., band .104
(FIG. 1), and others).
For example, a pattern associated with walking may comprise a set, or
grouping, of sensory data
corresponding to a movement or other parameter (e.g., physiological,
biological, environmental,
contextual) associated with walking (e.g., an arm movement, a leg movement, a
temperature (e.g.,
skin, core body, or other temperature), a galvanic skin response, or other
parameter). In another
example, a pattern associated with sleeping may comprise a set, or grouping,
of sensory data
corresponding to a movement or other characteristic or parameter associated
with sleeping (e.g.,
temperature (e.g., skin, core body, or other temperature), a galvanic skin
response, lying in a prone
position for a period of time, lower heart rate, or other parameter). In still
other examples, a pattern
may be associated with other movements, motion, moods, states, activities, or
events. If a given
pattern is found in a pattern library, a control signal relating to the
underlying activity or state may be
generated and sent by a wearable device to a media application (e.g., an
application that may be
implemented using hardware, software, circuitry, or a combination thereof)
that is configured to
present media content (1708). Based on the control signal, a media file may be
selected and
presented (1710). For example, a given pattern may be recognized by band 1612
(FIG. I6A) as a
shaking motion that is associated with playing a given list of music files
(e.g., playlist). When the
pattern is recognized and based on input provided by a user, band 1612 may be
configured to send a
control signal to skip to the next music file (e.g., song) in the playlist. As
described in detail above
in connection with FIG. 16A, any type of media file, content, or format may be
used and is not
limited to those described. Further, process 1700 and the above-described
elements may be varied in
order, function, detail, or other aspects, without limitation to examples
provided.
FIG. 17B illustrates an exemplary process for device control using sensory
input. Here,
process 1720 begins by receiving an input from one or more sensors, which may
be coupled to or in
data communication with a wearable device (1722). Once received, the input is
processed to
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determine a pattern (1724). Using the determined pattern, an operation is
performed to reference a
pattern library to determine whether a pre-defined or pre-existing control
signal is identified (1726).
If a control signal is found that correlates to the determined pattern, then
wearable device 1612 (FIG.
16A) (e.g., data-capable strapband, or the like) may generate the identified
control signal and send it
to a given destination (e.g., another device or system in data communication
with wearable device
1612). If, upon referencing a pattern library, a pre-defined or pre-existing
control signal is not found,
then another control signal may be generated and sent by wearable device 1612.
Regardless, after
determining a cOntrol signal to send using input from one or more sensors,
wearable device 1612
generates the control signal for transmission to a device to either provide a
device or device content
control or management function (1728). In Other examples, process 1720 and the
above-described
elements may be varied in order, function, detail, or other aspects, without
limitation to examples
provided.
FIG. 17C illustrates an exemplary process for wearable device data security.
Here, process
1740 begins by receiving an input from one or more sensors, which may be
coupled to or in data
communication with a wearable device (1742). Once received, the input is
processed to determine a
pattern (1744). Using the determined pattern, an operation is,performed to
reference a pattern library
to determine whether the pattern indicates a given signature that, for
authentication purposes, may be
used to perform or engage in a secure transaction (e.g., transferring funds or
monies, sending or
receiving sensitive personal information (e.g., social security numbers,
account information,
addresses, spouse/partner/children information, and the like)) (1746). Once
identified, the signature
may be transformed using various techniques (e.g., hash/hashing algorithms
(e.g., MDA, SHA-I, and
others, without limitation), checksum, encryption, encoding/decoding, and
others, without limitation)
into data formatted for transmission from wearable device 1612 (FIG. 16A) to
another device and/or
application (1748). After transforming the signature into data, the data is
transmitted from wearable
device 1612 to another device in data communication with the former (1750). In
other examples, the
data may be transmitted to other destinations, including intermediate
networking routing equipment,
servers, databases, data storage facilities, services, web services, and any
other type of system or
apparatus that is configured to authenticate. the signature (i.e., transmitted
data), without limitation.
In still other examples, process 1740 and the above-described elements may be
varied in order,
function, detail, or other aspects, without limitation to examples provided.
FIG. 17D illustrates an exemplary process for movement languages in wearable
devices.
Here, process 1760 begins by receiving an input from one or more sensors,
which may be coupled to
or in data communication with a wearable device (1762). Once received, the
input is processed to
determine a pattern (1764). An inquiry may be performed to determine whether
the pattern has been
previously stored and, if not, it is stored as a new record in a database to
indicate that a pattern is
associated with a given set of movements, motions, activities, moods, states,
or the like. If the
determined pattern does have a previously stored pattern associated with the
same or substantially
similar set of sensory inputs (i.e., input received from one or more sensors),
then the new pattern
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may be discarded or used to update the pre-defined or pre-existing pattern. In
other examples,
patterns that conflict with those previously stored may be evaluated
differently to determine whether
to store a given pattern in a pattern library. For example, if a pattern is
identified as being associated
with cycling, but is different from a previously stored pattern associated
with cycling (e.g., on a
different type of bicycle, on different terrain, using different gears, etc.),
then the pattern may be
stored as another (e.g., second, third, or other) cycling pattern. In another
example, if a pattern is
identified as being associated with sleeping, but is different from a
previously stored pattern
associated with sleeping, then the pattern may be stored as another sleeping
pattern. in some
examples, an algorithm may be implemented to determine whether a conflicting
pattern should be
stored as another version of a previously stored pattern, or discarded. In
sonic examples, more than
one pattern library may be stored on a wearable device. In some examples, a
pattern library may be
stored on a remote database and used by a wearable device that is in data
communication with the
remote database. After determining whether to store the pattern in a pattern
library, the patterns may
be aggregated in a movement library to develop a "movement language" (i.e., a
collection of
patterns) that may be used to interpret activities, states, or other user
interactions with a wearable
device in order to perform various functions, without limitation (1768). For
example, once it is
determined to store a pattern in a pattern library, the pattern may be added
to a collection, or set, of
patterns that are associated with an activity or motion (e.g., running,
walking, Swimming, jogging,
jumping, shaking, turning, cycling, or others), a biological state (e.g.,
healthy, ill, diabetic, awake,
asleep, or others), a physiological state (e.g., normal gait, limping,
injured, sweating, high heart rate,
high blood pressure, or others), or a psychological state (e.g., happy,
depressed, angry, and the like).
In other examples, process 1760 and the above-described elements may be varied
in order, function,
detail, or other aspects, without limitation to examples provided.
FIG. 18 illustrates an exemplary system for creating, storing, and performing
other
operations, with regard to motion profile templates. System 1800 may be
configured to include
XML 1802, compiler 1804, graphical user interface (GUI) 1806, user input 1808,
modes 1810-1816,
database management system (DBMS) 1818, database 1820, recompiler 1822,
template 1824, and
operations 1826-1836. In some examples, system 1800 may be implemented using
XML 1802,
which may be implemented using any type of XML markup language and may be
compiled into
binary form by compiler 1804 to form template 1824. In some examples, template
1824 may
comprise tags denoting actions and sensors, for example associated with a
motion profile (see, e.g.,
FIGS. 13 and 15). Template 1824 may include (i.e., support) simple operations,
including IF
operation 1826, THEN operation 1828, ELSE operation 1830, and WHILE operation
1832.
Template 1824 also may include (i.e., support) other operations 1834 and other
operation statements
1836. In some examples, system 1800 may be implemented with GUI 1806, which
may be a user
interface (i.e., graphical user interface) configured to enable a user to
interact with (e.g., view output,
provide input, or otherwise interact with) a system (i.e., system 1800). En
some examples, GUI 1806
may receive user input 1808 in any format, including human-readable formats
(e.g., by typing into a
=
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field, uploading data from another device, making selections on a form, or
other human-readable
input formats), which may be added or communicated to XML 1802 using GUI 1806.
In some
examples, GUI 1806 may be implemented with various modes of operation,
including record mode
1810, retrieve mode 1812, process mode 1814 and other mode 1816. For example,
record mode
1810 may enable a user to record a template. In some examples, user may record
a template by
perforining an action using one or more data-capable bands and transmitting or
uploading that data
using GUI 1806. In another example, retrieve mode 1812 may enable a user to
retrieve a template.
For example, GUI 1806 may retrieve template 1824 from the database using DBMS
1818. In yet
another example, process mode 1814 may enable a user to conduct other
processes associated with a
template (e.g., overwrite, download, etc.). In still another example, other
mode 1816 may comprise
yet an additional mode of operation available using GUI 1806. For example,
other mode 1816 may
comprise another manner in which a user may create a template by providing
various types of user
input 1808, as described above. In another example, GUI 1806 may be configured
with a human-
readable "drag-and-drop" interface that may enable a user to choose parameters
for a template from
various options or categories of options.
In some examples, template 1824 may be stored in database 1820. In some
examples,
template 1824 may be stored in binary form, and may be recompiled by
recornpiler 1822 (e.g., to
display actions performed on template 1824 for a user, to be reviewed by a
user, etc.). In some
examples, template 1824 may describe an activity with biological, biometric,
physical, physiological,
psychological or other parameters. In other examples, one or more compiled
templates may be
formed into an addict (e.g., Java-based plug-in application, other Java
addicts, or other addicts). In
still other examples, template 1824 may be implemented with a priority for
dower management uses.
In yet other examples, template 1824 may be sold, or bartered for, along with
other templates on a
marketplace (e.g., a fitness marketplace, Amazon Market PlaceTm, eBay , other
online auction
market, or.other marketplace) or an SNS (e.g., Facebooktg.), Twitter , etc.).
Once created, template
1824 may be downloaded onto any data-capable band, including any of the data-
capable bands
described herein, either using GUI 1806 or other interfaces.
Although the foregoing examples have been described in some detail for
purposes of clarity
of understanding, the above-described inventive techniques are not limited to
the details provided.
There are many alternative ways of implementing the above-described invention
techniques. The
disclosed examples are illustrative and not restrictive.
36
