Note: Descriptions are shown in the official language in which they were submitted.
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METHOD OF CHARACTERIZING PHYSICAL PERFORMANCE
FIELD OF THE INVENTION
The present invention relates generally to human performance measurement.
More particularly, the present invention relates to a method of characterizing
and
calculating physical performance.
BACKGROUND OF THE INVENTION
As the baby boom market continues to age and obesity in all age groups
continues to take center stage, health delivery services, in particular the
exercise and
wellness sectors are being called upon to adapt to the unique and
sophisticated needs of
the market. Clearly, the trend in today's market is to maximize the benefits
and efficiency
of physical activity without spending countless hours at a gym. People are
realizing that
ultimate health should combine nutrition and lifestyle with a balanced
exercise program
for real, lasting results and improved physical well being, whether at work,
home or play.
Companies too are feeling the squeeze from employee obesity, workplace
injuries,
absenteeism and the overall state of ill health in the workplace.
Of the 100 million individuals in the United States that participate in
exercise
programs, over 80% fail to maintain a balanced long-term health program. They
quickly
become overwhelmed with the complexities of balancing dietary intake,
nutrition, exercise
activity and lifestyle, and thus fail to devote the necessary time and
discipline needed to
impact true and lasting health. Time is a very precious commodity today; as a
result,
people can be unwilling to devote the necessary time to the manual day-to-day
management of a comprehensive health program. In most cases, individuals are
left to
their own motivation and planning in the development and execution of their
health and
exercise programs. Some facilities provide limited guidance once members join,
but
quickly dissolve that one-on-one personal management service.
Equipment sold today lacks the technology, objectivity, science and tracking
to
successfully associate the physical functions of exercise, the physiological
outcomes of
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the body with the effectiveness of a well-balanced nutritional intake program.
This leaves
the user to experiment without any real objective, benchmark or tracked
outcome, making
it extremely difficult for users to reach and maintain optimum health.
When a person exercises, either at home or in a fitness club, they usually
have
some goal in mind, such as getting fitter, staying fit, increasing strength,
loosing weight
etc. To get the most benefit from exercise it is important that the user knows
exactly what
goal they have been set and how they are performing, both on an immediate real-
time
basis and over time. This leaves the exerciser with a number of key questions:
How well
have I done? How much energy did I exert and how many Calories did I burn? Did
I
perform well against my target or exercise program? What was my target? Did I
do better
this time, compared to last time or my historical data? Am I improving and
progressing my
fitness level? Exactly how fit am I?
To set goals and track fitness can require measuring how much energy a person
has exerted during exercise, i.e. Calories, power and workload the muscles are
performing while continuously monitoring heart rate against a training zone.
The current
method of establishing a person's absolute maximum performance on any given
piece of
exercise equipment is to get that person to exercise to exhaustion while
measuring the
parameters of interest: heart rate, oxygen consumption, weight lifted etc.
This data
provides an individual's maximum performance at that point in time i.e. the
individual's
100% output or ability. However this may be only 60% of the standard for that
individual's
age or sex. Such standards (high, average, poor, etc) are available for
aerobic fitness
(V02max) as established on a treadmill, bike, or step test and some physical
performance tests.
This method, for most people, is impractical, since as you are improving in
fitness,
you would be required to re-take the tests to track any change in fitness
level. While a
person may feel that they have exercised well, and are improving, without some
absolute
measure of performance it is difficult to know for sure. Therefore a simpler,
more practical
way is required that measures performance against goals as the person is
exercising,
tracks fitness and progression and can be tailored to each individual.
There are known approaches in which a counter is used to measure revolutions
of
a weight stack. A display portion of the system allows the user to log in
using a PIN,
providing exercise execution information such as seat and weight settings,
target sets
and reps, and rep counts. A computer can be provided in a fitness club, at
which a user
can observe the statistics based on the user's workout. Exercise machines are
networked into a central database, and the system can be accessed on a workout
floor, a
staff computer workstation, or via the Internet. Staff can create workout
templates and
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performance monitoring tools, and send members customized messages via the
system.
The performance monitoring allows staff to identify in real-time which members
need
assistance and provides targeted feedback to the staff. Progress reports and
graphs are
available to users. Non-machine activities such as jogging, swimming and
fitness classes
can be logged in to the system. Reports to management can also be generated
that detail
both member and staff demographic data.
While such known approaches provide computer-based solutions for fitness
training, those solutions are essentially electronic versions of a performance
card on
which measured repetition and set data is stored and possibly compared to a
target
value. The feedback provided is minimal, and only provides information
relating to
targets for sets and repetitions, not in terms of overall health targets.
It is, therefore, desirable to provide a method of calculating physical
performance,
which can be used in association with a human performance system, that
overcomes
some of the drawbacks of existing solutions.
SUMMARY OF THE INVENTION
It is an object of the present invention to obviate or mitigate at least one
disadvantage of previous health delivery services and health management
systems.
Embodiments of the present invention utilize proprietary technologies and
advanced scientific analysis to deliver a complete automated health management
solution
deployable to a plurality of health verticals.
Embodiments of the present invention will preferably provide users with
automated, personalized health management while tracking, assisting,
motivating and
encouraging them to achieve the maximum results in less time. This automated
technology and process can encompass many different areas, such as diet,
nutrition,
physical activity, and lifestyle.
In an aspect of the invention, there is provided a method of measuring
physical
workload for a task, including the following steps: receiving task execution
data;
calculating an exerted energy value in response to the received task execution
data; and
generating a physical performance index value based on the exerted energy
value.
The step of calculating the exerted energy value can include calculating a
work
value based on the received task execution data, and determining the exerted
energy
value based on the calculated work value. The received task execution data can
include
task baseline data or user performance data. The method can further include,
prior to the
step of receiving, the step of measuring the task execution data.
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The step of generating the physical performance index value based on the
exerted energy value can include multiplying the exerted energy by a machine
constant.
The machine constant can be determined based on: a ratio of energy exerted in
performing the task and a ratio of time spent performing the task; a ratio of
energy
exerted in performing the task and a machine maximum energy value; or a ratio
of
cylinder force constants.
The physical performance index value can include a measure of exercise
intensity. The method can further include: displaying a physical performance
indicator to a
user, the physical performance indicator being determined based on the
physical
performance index value; determining a user's physical performance based on a
comparison of the physical performance index value with a baseline value in a
pre-
defined profile; determining a user's physical suitability to perform a second
task based
on a comparison between the physical performance index and a second task
baseline
value; or determining a degree of a user's physical function based on a
percentage
difference between the physical performance index value and a physical
function baseline
value.
In another aspect, the present invention provides a method of characterizing a
physical performance device, including the following steps: receiving task
execution data;
calculating an exerted energy value in response to the received task execution
data;
generating a physical performance index value based on the exerted energy
value; and
compiling or generating a physical performance device profile including the
physical
performance index value.
The step of calculating the exerted energy value can include calculating a
work
value based on the received task execution data, and determining the exerted
energy
value based on the calculated work value.
The method can further include one or more of the following steps: identifying
a
functional muscle group associated with the device; determining a machine
inefficiency
based on a comparison of a measured PI value for the device and a standard
device type
PI value; prior to the step of receiving, measuring task execution data; and
transmitting
the physical performance device profile to a storage means.
In other aspect, the present invention provides a computer-readable medium
including statements and instructions which, when executed by a computer,
cause the
computer to perform the steps of the method of measuring physical workload for
a task,
as described above.
In a further aspect, the present invention provides a computer-readable medium
including statements and instructions which, when executed by a computer,
cause the
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computer to perform the steps of the method of characterizing a physical
performance
device, as described above.
In a yet further aspect, the present invention provides a computer readable
medium comprising a data structure, the data structure including a physical
performance
index value generated by the method of measuring physical workload for a task,
as
described above.
In a still further aspect, the present invention provides a computer readable
medium comprising a data structure, the data structure including a physical
performance
device profile generated by the method of characterizing a physical
performance device,
as described above.
In another aspect the present invention resides in a method of characterizing
physical fitness of a user, comprising: selecting an exercise machine, the
exercise
machine being a first type of machine, the exercise machine having a
performance index
scale from a minimum exercise machine performance index scale value to a
maximum
exercise machine performance index scale value, the exercise machine having a
scaling
factor, the exercise machine having a sensor to determine a movement of a
component
of the exercise machine and having an energy expenditure value per the
movement,
identifying a muscle associated with operation of the exercise machine,
operating the
exercise machine, measuring the movement of component of the exercise machine,
determining an exerted energy value from the movement of the component of the
exercise machine and the energy expenditure value per movement; determining an
actual performance index for the muscle during the operating of the exercise
machine by
multiplying the exerted energy value by the scaling factor, and communicating
the actual
performance index for the muscle to one of the user and a computer system.
Other aspects and features of the present invention will become apparent to
those ordinarily skilled in the art upon review of the following description
of specific
embodiments of the invention in conjunction with the accompanying figures.
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BRIEF DESCRIPTION OF THE DRAWINGS
Embodiments of the present invention will now be described, by way of example
only, with reference to the attached Figures, wherein
Fig. 1 is a flowchart illustrating a method according to an embodiment of the
present invention;
Figs. 2A-2C illustrate three types of hydraulic exercise machines; and
Fig. 3 is a block and flow diagram of a system with which a method of
embodiment of the present invention can be used.
DETAILED DESCRIPTION
Generally, the present invention provides a method of measuring physical
workload for a task. After receiving task execution data, an exerted energy
value is
calculated in response to the received task execution data. A physical
performance
index value is then generated based on the exerted energy value. The exerted
energy
value can be calculated based on a calculated work value, which itself is
based on the
received task execution data. A physical performance device, such as an
exercise
machine, can be characterized based on the performance index value. The method
can
include compiling a physical performance device profile including the physical
performance index value, and optionally transmitting the profile to a storage
means. The
method can also include identifying a functional muscle group associated with
the
device. The measured physical workload can be a measured value of user
performance,
which can be compared to a target value to determine a user's physical
performance.
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While some known systems and methods can count repetitions and sets, and
possibly compare the data with a baseline level, such systems do not provide
any
indication of a level of exertion or human performance for a particular user
when
performing the repetitions and sets observed. There is no analysis, data
management, or
feedback in known systems. The present invention provides additional analysis
tools,
and provides information on energy exerted, calories burned, and many other
useful
parameters not provided by the known systems. It can be described as relating
to
automated monitoring of exercise equipment and the calculation or estimation
of an
individual energy output during the use of this equipment. In other words,
known systems
provide tracking of fitness data while the present invention provides total
management of
fitness data. Though embodiments will be described in relation to fitness and
exercise
machines, the present invention can be used to measure physical performance on
any
machine or device requiring physical exertion, and compare a measured value
with a
performance target.
An embodiment of the present invention measures a person's physical exertion
via a machine on which the exertion is being made, in such a way that it can
be
compared with a performance target. The methods described herein are
advantageously
employed in the context of a computerized physical performance measurement
system.
An example of such a system is described in the inventor's commonly-assigned
FCT
Application No. PCT/CA2005/001626 (Published as WO 2006/042420 Al) entitled
"System For Measuring Physical Performance And For Providing Interactive
Feedback"
filed of even date herewith. Following a description of some embodiments of
the present
invention, a description of such a system will be provided below, in order to
provide a
suitable context for the application of methods and other aspects of
embodiments of the
present invention.
Performance Index (PI)
Performance index (PI) is a global measure that establishes a physical
performance level in relation to, or for, a piece of exercise equipment. The
PI is a
measure against which a person can set targets. It can also represent a
person's overall
physical performance and body efficiency during exercise. The PI measure can
be
applied to any form of exercise, from aerobics to gym equipment and specialist
training.
PI is a unique measure according to an embodiment of the present invention,
which is
based on the energy a person uses while exercising.
Because different exercises and exercise machines will exercise the body in
different ways and use different amounts of energy, it is advantageous to
characterize
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each machine with respect to the PI scale, so that an accurate measure of PI
can be
made. In a presently preferred embodiment, PI is based on a linear scale from
zero to
about 1000, with the premise that an average person with a high level of
fitness will
perform at a PI of about 1000.
The use of PI as a universal standard means that it can be applied to any type
of
exercise equipment. Exercise routines on a weight stack, exercise bike, rowing
machine,
treadmill etc. can all be defined and related to a standard PI value, e.g.
PI=1000. By
installing an electronic controller on an exercise machine and applying the
correct type of
sensor, energy can be measured and thus performance calculated.
Suppose a user is aiming for a target PI of 100. If that user employs two
completely different and independent products and product types as part of an
exercise
program, including the method and resistance type, the end result is that the
user's PI is
constant. If you are aiming for a PI of 100, you are aiming of a PI of 100 on
any machine
that has been classified according to this system. It is not necessary to have
different
measures or classifications for each different machine or even each different
type of
machine, since the PI measure can be applied to any type of machine. As such,
PI can
be described as a standardized measure of energy, or of exerted energy.
Measuring PI
In order for PI to be used as a standard measure against which user
performance
can be gauged or ranked, there needs to be a generic way of calculating a PI
value for a
given exercise machine, or physical performance task/job. Having a consistent
way of
calculating a PI value is one reason why it can be used as a standard measure.
This
method can be used either for determining a baseline PI value to characterize
a physical
exertion device, or for determining an actual PI value while a user is
exercising.
Fig. 1 illustrates a method of calculating a physical performance value. While
the
preferred physical performance value to be calculated is PI, this method can
be used to
determine any measure of intensity of physical performance or exertion. Step
102 shows
a first step of receiving task execution data. The received task execution
data can be task
baseline data that was previously measured and is stored in a memory. In
another case,
the received execution data can be measured data relating to a
characterization of a task.
In the case of determining in real-time a user's current PI value, the task
execution data
can be user performance data. The method can additionally include the step of
measuring task execution data, such as measuring user performance data. This
additional step is preferably performed before step 102.
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Step 104 in Fig. 1 shows a second step of calculating an exerted energy value
associated with a task in response to the received task execution data. Step
104 can
include a sub-step of calculating a work value and determining the exerted
energy value
based on the calculated work value. Known mathematical relationships between
work
and energy for each particular job/task are used in order to determine the
exerted energy
value based on the calculated work value. Step 106 shows a third step of
determining, or
generating or calculating, a performance index value based on the exerted
energy value.
The method can also include a further step of displaying a physical
performance indicator
to a user, the physical performance indicator being determined based on the
physical
performance index value.
PI values are scaled versions of an energy value, either a measured exerted
energy value or a target exerted energy value. The target exerted energy value
can
alternatively be referred to as a measure of required exerted energy. A
machine
maximum energy value is determined for an exercise machine. This determination
is
based on the maximum amount of energy that would be required to operate that
machine
at full capacity for a given period of time. A maximum PI value of 1000 is
correlated to
the machine maximum energy value. PI values between 0 and 1000 are then
associated
with corresponding energy values between zero and the machine maximum energy
value, based on an appropriate calculation, preferably in relation to a linear
scale. A PI
value table can then be generated based on those associations, and stored on a
computer readable medium. Therefore, when a user is doing an exercise on a
machine,
an exerted energy value can be calculated based on received user performance
data,
and a measured PI value can be calculated based on a comparison of the
calculated
exerted energy value with values in a performance index table for the machine.
For characterizing a physical performance device, such as an exercise machine,
the performance index value is determined by multiplying the exerted energy
value by a
scaling factor. The scaling factor can be a machine constant for the exercise
machine
being used. The machine constant can be determined based on a relationship
between a
ratio of energy performed and a ratio of time spent. For example, for a
hydraulic cylinder,
the machine constant is determined based on a comparison between a ratio of
energy
performed in forward and reverse motion of the cylinder, and a ratio of time
spent on the
forward and reverse motion of the cylinder. This method can alternatively be
implemented
as, or described as, a method of estimating energy expended, and
tracking/integrating
that over time. Obviously, the machine constant is different for each
different machine.
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Example PI Determinations for Different Machines
In terms of the implementation of the present invention with different types
of
machines, different sensors are used for each different type of machine, or
product type.
Different sets of data are collected from each machine type. The sets of data
are
interpreted differently for each product type. However, a common end result is
always
achieved, namely a qualification of performance index.
For a weight stack, the determination of PI includes considering the amount of
weight being lifted in a given period of time over a given distance. From this
raw data, the
distance and the time and load values are used to calculate velocity,
acceleration, energy
and every other mathematical calculation that is necessary. All of that
calculated data is
correlated to the scale of human performance index.
Spinning has taken traditional bikes and turned them into a means for complete
exercise programs. The next evolution in programs is feedback and automation
being
incorporated into those programs. To use an embodiment of the present
invention with
spinning applications, a pressure foil mechanism is used and is mounted
between the
plastic of the brake pad and the felt of the brake pad. As a result, the
sensor measures
the pressure on the surface area of the bike's brake pad. The more the brake
is
squeezed, the more pressure will be sensed. At the same time, the system
includes an
optical sensor that detects wheel position. Therefore, the system can measure
how hard
the brake pad is being squeezed and how fast the wheel is spinning; on the
basis of
those measurements, position, resistance, and heat generated can be
determined. In
relation to time and those other factors, the energy being exerted can be
measured. Once
the energy value is obtained, this can be related to the PI scale. On the
basis common PI
number, the system can provide indications of the settings to be used on the
particular
machine, such as putting a spinning machine on setting number 9.
For hydraulic machines, a similar PI determination is made. However, work in
this
case is not determined with respect to a load being lifted. In such a case,
the system
looks at sensor data in terms of position versus time based on the
characteristics of the
piston, such as the characteristics of the orifice and the viscosity of the
fluid or liquid and
the amount of time it would take for a person to move a particular amount of
fluid from
one chamber to the other. In an exemplary characterization or calibration
method, a
piston is taken and characterized based on these parameters. The piston is
then tested
on the fixtures and devices of a testing system for the present invention so
that it can be
characterized, that is to indicate the optimal efficiency of the piston. A
separate
characterization machine is preferably used for such purposes. A resulting
table is
provided based on the characterization testing of the piston. The table can be
graphed
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and once a full matrix is obtained, that matrix can be included in software
which is part of
a system using the present invention. These features provides the ability to
characterize
or calibrate a piston and any number of different pistons, as well as to
create a unique
profile for each such piston and to put that profile directly into software
tables. Once the
piston has been characterized, the rest of the logic can be applied in terms
of work,
velocity, time, piston setting and other parameters in order to equate that to
PI as well.
Machine Inefficiencies and Muscle Group Identification
One aspect of the present invention can be described as a method of
characterizing a physical performance device, including the following steps:
receiving task
execution data; calculating an exerted energy value in response to the
received task
execution data; generating a physical performance index value based on the
exerted
energy value; and compiling a physical performance device profile including
the physical
performance index value.
Aside from a machine's PI value, there are other parameters that can be
included
in a machine characterization, or a machine profile.
Inefficiencies of a particular machine can be taken into account. A machine
inefficiency can be determined based on a comparison of a measured PI value
for the
device and a standard device type PI value, which can be a standard PI value
for the
machine type, or machine class. A determination can be made as to how much
energy
(or additional physical energy) is being exerted by a user as a result of the
calculated
machine inefficiency. A relationship can also be made between the human energy
exerted and the human performance index scale. This analysis is preferably
done for
each available machine in a particular machine type or class. This can be part
of the
machine characterization, or appraisal or calibration, process. Therefore, a
machine
profile preferably includes a machine inefficiency parameter, the scale of
which is
preferably suitable to account for machine performance that is either better
or worse than
an expected value. A report rendered to a user can preferably account for such
machine
inefficiencies when analyzing the user's performance data.
A machine characterization also preferably includes an indication of the
functional
muscle group(s) that the machine exercises. This can be based on a step of
identifying a
functional muscle group associated with the device. A functional muscle group
parameter
can include an indication of whether the machine primarily or secondarily
works particular
muscle groups, and what the ratio or percentage is. Machines can then be
classified
based on the functional muscle groups that are worked by the machines. Once
the work
requirement is known, as well as the energy distribution by muscle ratios, and
once this is
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related to the indexing of PI, this results in a system as described above,
including
feedback relating to Pl. For example, a report rendered to a user can
preferably generate
muscle-group based reports based on user performance data at each machine that
works
that muscle group, taking into account the percentage of work for each muscle
group at
each machine.
Based on known machine information related to muscle groups being exercised
by that machine, a method according to an embodiment of the present invention
can
include a step of calculating a PI value for each muscle group. In the case of
determining
a target value, the method can include a step of calculating a PI capacity, or
maximum PI
value, for each muscle group.
Other Industrial Applications
In addition to the examples described above in relation to the
exercise/fitness
industry, there are many other applications for embodiments of the present
invention.
A method according to an embodiment of the present invention can determine a
user's physical performance, and compare it with a baseline value, such as a
job value.
The job value can be calculated by determining the total job energy required.
For
example, in the case of the job of lifting a box, the total job energy
required can be
calculated based on a measured weight of the box, the height that the box must
be lifted,
and any other value. Based on a knowledge of the muscles required to perform
the job, a
job profile can be generated based on a proportionate distribution of the
total job energy.
The method can provide an identification of an area of shortfall by comparing
a user's
measured PI value with a job PI value. Since muscle-group level information on
the target
and the measured values is available, the method can provide an identification
of the
particular muscle group, or part of the body, which is the cause of the
shortfall. In that
way, the method can also provide an improvement recommendation based on the
identified area of shortfall.
A computer can store a plurality of predefined profiles. Those predefined
profiles
can include parameters, such as a PI index, used to classify or appraise a
user by age,
gender and occupation. A user's measured physical performance can be compared
to a
pre-defined profile for that type of individual. A system can be used to
assign a muscle
specific PI Index and a overall global body PI Index to each user. The user's
measured
PI value(s) can be used in the following contexts:
Work Related Job Matching:
a. Matching employees to the jobs they are expected to perform at
work.
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b. Objectively identifying injury probability based on collected data from
various workouts by comparing observed performance to job profiles.
c. Modifying, or identifying potential modifications, to the ergonomics or
physical demands of a job to closer match the physical function of an
individual
performing such a job.
d. Conditioning, or identifying potential training or conditioning
programs, to
condition the individual to better match the required physical demands of
their job.
Rehabilitation and Medical Application:
a. Tracking the physical function and improvements of people in therapy.
b. Matching the physical function of people in rehab to identify return to
work
readiness.
c. Evaluating the effectiveness of therapy based on injury type and
physical
disability, impairment.
d. Used by insurance companies to establish the degree of functional loss
resulting from injury be objectively establishing the amount of PI loss.
Sports Teams:
a. Matching sports players to pre-defined ideal profiles based on played
position and actual sport.
b. Determining and track individual muscle behaviours prior to the onset of
physical injury.
The above-described scenarios can be described as an extension of a previously-
described method according to an embodiment of the present invention further
comprising the step of determining a user's physical performance based on a
comparison
of the physical performance index value with a baseline value in a pre-defined
profile.
For example, this can include determining a user's physical suitability to
perform a
second task based on a comparison between the physical performance index and a
second task baseline value. This can also include determining a degree of a
user's
physical function based on a percentage difference between the physical
performance
index value and a physical function baseline value. In any of these cases, the
method can
include displaying, or otherwise providing, a physical performance indicator
to a user, the
physical performance indicator being determined based on the physical
performance
index value.
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Example of PI and Equipment Characterization in an Exercise Context
During an exercise, an electronic controller can use a sensor to measure
parameters of an exercise machine's resistive element. For example, for a
hydraulic
machine, the sensor can measure cylinder velocity. Knowing the cylinder
setting, it is
possible to calculate the force being exerted, and over time the amount of
energy being
used. Each piece of exercise equipment requires a machine constant setting
that will be
used to convert energy to Pl. Based on the type of exercise to be performed
e.g. weight
loss program, strength program etc. a standard setup is established that
consists of the
exercise duration, number of strokes and the cylinder resistance setting and
Calorie burn.
This then becomes the standard and represents a performance level of PI=1000.
The present invention can store this data in a database of physical
performance
measurement device profiles, or exercise machine profiles. In an embodiment, a
storage
means, or central database, of a computerized exercise system can comprise the
database of exercise machine profiles. For a hydraulic machine, the exercise
machine
profile can include some or all the following information: manufacturer model;
type; and
cylinder used. This system also allows individual cylinder settings to be used
on those
machines that are fitted with easy adjustment mechanisms.
The characterization of equipment can be done using a computer based software
program that can be attached to the equipment to measure and determine the
various
machine parameters. An overview of this process is given.
Once the machine is calibrated, this data can be used by a Kiosk to calculate
the
various information tables required by an Exercise Controller (EC), attached
to a piece of
exercise equipment. This data is based on the exercise profile, either
automatically
determined or setup by the user.
After completion of the exercise, the data that the EC sends back to the Kiosk
requires analysis to give user feedback on performance. For each piece of
exercise
equipment that has an Exercise Controller attached, a first step is to
determine its PI
rating where an Average Person will score a PI of 1000. This can be done by
setting:
Time duration of Exercise TAP; Number or Reps at full stroke NAP; and Machine
Resistance Setting CAP.
These numbers can initially be set from experience, but can later be adjusted
as
exercise data is obtained and a better understanding of PI and AP is made.
From these
numbers, and knowing the characteristics of the machine, we can determine the
energy
used for that exercise and calculate an equipment constant (K) that will
relate this amount
of energy to PI=1000.
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For a particular user and exercise, the Kiosk will set the target PI, exercise
duration and number of reps. From this data, the energy the user needs to
expend to
achieve this exercise level can be calculated. This is the 100% level for Pl.
This energy
level can then be scaled from 0 to 100%. A user can put in more energy, to
exceed their
target, however warning levels need to be set to indicate too much effort is
being done
and to warn against possible injury. Therefore levels of 125% and 150% are
also set.
Example of PI and Equipment Characterization for a Hydraulic Device
A detailed explanation will now be provided with respect to calculations of PI
for
hydraulic exercise equipment. Similar calculations for other types of
equipment will be
evident to one of ordinary skill in the art.
Each cylinder has a particular characteristic that relates piston velocity to
the force
required to move it. This can be measured on a dynamometer and approximated to
a
polynomial equation of the form:
F = av2 + bv + c
where f is the force and v the velocity. If cylinder is configured where the
force is
different in the forward and reverse directions, two force factors are
required.
Over the low velocity range that the cylinder is used, with a maximum of
10mm/sec. this can be approximated to a straight line, therefore the equation
becomes:
F=f v
where f=Force factor (Ns/mm) for a particular cylinder direction and setting,
F=Cylinder Force (N), and v=velocity (mm/s).
In addition each piston may have up to 10 settings through the adjustment of
the
bleed valve (some cylinders will have less, therefore the table may not be
fully
populated). Each of these bleed valve or "hardness" settings has a different f
value.
Therefore a database of cylinder type, hardness setting and the forward and
reverse
force factor value is required.
When in use on each machine, the equations v=d/t and F=fv can be used to
relate all these variables to get energy as follows;
E = Fd = f(d2/t)
where d=distance (mm), t=time (s) and E=Energy (mJ) required to move the
cylinder over
distance d in time t.
An exemplary table for a cylinder with 8 settings is shown in Table 1 below:
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Parameter Value
Manufacturer Rancho
Model RS99120
Valve Setting Value (fwd) Value (rev)
1 1.25 0.65
2 1.87 0.87
3 2.10 0.91
4 2.65 1.23
3.60 1.54
6 3.80 1.76
7 4.20 1.87
8 4.56 2.34
Table 1
Exercise machines with hydraulic cylinders fall into a number of different
5 categories based on how the cylinders are configured. Categorizing the
machine in this
way enables one equation to be used for the energy calculations. Figs. 2A-2C
illustrate
three types of hydraulic exercise machines.
The forward and reverse force factors for the machines can be calculated as
follows:
Type 1: Single cylinder machine (shown in Fig. 2A)
fFwD=CYLFwD
fREv=CYLREv
Type 2: Dual cylinder machine with cylinders working in the same direction
(shown in
Fig. 2B)
f FwD=CYL1FwD+CYL2FwD
f REv=CYL1REv+CYL2REv
Type 3: Dual cylinder machine with opposing motion (shown in Fig. 2C)
fFwD=CYL1FwD+CYL2REv
fREv=CYL1REv+CYL2FwD
Other types of machines may become apparent in the future.
Therefore a database of exercise machines is required, that describes the
Manufacturer, Model, Type and Cylinder used. This database can comprise
machine
profiles as discussed earlier. Also, a fitness club may set the cylinder
hardness for each
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machine differently, therefore when a machine is selected, the cylinder
setting is
preferably also selected so that the force factors for the local club are
known.
A distance measuring device is provided for use on the cylinder, since this
has
specific characteristics and may be non-linear. Some devices may not measure
from
zero, so the stroke minimum and stroke maximum can also be provided in the
database.
Table 2 below represents an exemplary entry for a machine:
Parameter Value
Manufacturer ABC 123 Inc
Model Tricep Bicep Pull
Type 2
Cylinder Rancho RS991I8
f forward 1.26
f reverse 2.85
Stroke Min (cm) 10
Stroke Max (cm) 30
Sensor GPSD12
Sensor Formula 6524.2*x**-0.8273
Table 2
The EC carries out a number of calculations to determine the Performance Index
and Range of Motion. These values can be calculated by the Kiosk and sent to
the EC in
the form of data tables. A lookup table is used by the EC to display the range
of motion
on 9 LED's. The data sent to the EC can be the binary value that represents
the reading
from the distance sensor. Therefore the Kiosk can calculate these values into
a table.
As an example the Sharp GPSD12 has a non-linear output that can be calculated
using the values and equations below. Define the following parameters: L =
Stroke
Length; Lx = Stroke Maximum; WIN = Stroke Minimum; R = Stroke Resolution; and
V =
Sensor Voltage as a 10-bit resolution input. Taking into account that L = LmAx
¨ WIN, R=
To perform further determinations: Calculate cylinder position from Wiry to L.
in
steps of R; Calculate Sensor Voltage using the formula for the sensor; and use
the fact
that V = 6524.2x-"273. Results are shown in Table 3 below.
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LED Cylinder Sensor Voltage
Position (cm)
1 10.00 1024
2 12.22 822
3 14.44 716
4 16.67 636
5 18.89 574
6 21.11 523
7 23.33 482
8 25.55 447
9 27.78 417
Table 3
Performance Index and PI Calculation
Once the PI is established, then the energy can be calculated and by using the
above energy equation, the energy put into the machine by the user can be
determined,
by sensing the change in the cylinder distance over time. Because the EC
measures
cylinder position at regular intervals based on the processor clock time or
ticks, it is
possible for the Kiosk to calculate all this out and send to the EC a table
that relates the
PI% to a change in distance per clock tick. The energy calculation will depend
on the
machine type and take into account different cylinder constants for forward
and reverse
motion.
Once the force factors have been calculated as above, the PI can be calculated
in
the same way for all machine types. The following variables will be used in
the discussion
that follows: T= exercise time (sec); E= total energy to be expended (mJ); f
FWD = forward
cylinder force factor (Ns/mm); fREv = reverse cylinder force factor (Ns/mm);
EFwD =
Energy to be used in forward motion (nil); EREv = Energy to be used in reverse
motion
(ml); IER = instantaneous energy rate to be used in forward motion; t =
Exercise
controller clock time (msec); Td = time of forward motion; TREv = time for
reverse motion;
and fr = force factor ratio.
The Energy is calculated from the machine constant. Since the number of
forward
and reverse strokes is the same, the energy needed for both forward and
reverse motion
can be calculated by factoring the energy by the ratio of the cylinder force
constants as
follows:
f r = f FwD / (f FWD 4- f rev)
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EFwD =Ex.!,
EREV = E - EFWD
However because the energy needed to move the cylinder in each direction is
different it is likely that the forward stroke will be longer in time that the
reverse stroke.
This is particularly true if the return stroke offers little or no resistance.
Assuming that the
ratio of the time for the forward and reverse strokes is the same ratio as the
force factors,
then the time in forward motion and the time in reverse motion can be
calculated as
follows:
TFWD = T X fr
TREV T - TFWD
Since the total exercise time and the clock tick are known, the Instantaneous
Energy Rate (IER) can be calculated as:
IER = (Em,d x t) / (Tim x 1000)
The IER is the same for both directions, since both E and T are factored the
same.
So when the lookup table of PI% is calculated, the distance to be travelled by
the cylinder
needs to reflect the fact that equal effort must be put in on both strokes,
and on the easier
stroke the velocity (and the distance travelled) must be higher. Using the
forward and
reverse cylinder force factor takes this into account as follows:
dm,d = 4(IER/ftwd)
drev = 4IER= f
=rev
A look-up table for the EC can now be generated. Table 4 below shows an
exemplary look-up table having 14 entries, with only the last column of data
being sent
(other data is put in for an example). The distance is scaled by 10,000 so
that it is more
easily understood by the EC.
PI IER Forward Forward Reverse Reverse
(%) (mi/Tick) Distance Scaled Distance Scaled
(cm) Distance (cm) Distance
(cm) (cm)
1 8 4 0.0497 497 0.0255 255
2 16 8 0.0703 703 0.0510 510
3 24 12 0.0861 861 0.0165 765
4 32 16 0.0994 994 0.1020 1020
5 40 20 0.11 1112 0.1275 1275
6 48 24 0.124 1240 0.1530 1530
7 56 28 0.131 1310 0.1785 1785
8 64 32 0.141 1410 0.2040 2040
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9 72 36 0.149 1490 0.2295 2295
80 40 0.157 1570 0.2550 2550
11 88 44 0.165 1650 0.2805 2805
12 100 50 0.176 1180 0.3188 3188
13 125 62.5 0.196 1960 0.3985 3985
14 150 75 0.215 2150 0.4782 4782
Table 4
In the above example the forward and reverse cylinder factors are different,
therefore the table reflects this. In other words, the reverse force factor is
less than the
5
forward one and the cylinder must be moved a longer distance each clock tick
(faster) to
maintain the same energy.
One additional check should be done before sending this table to the EC. A
typical
AID converter has a particular resolution. Consider the case where the
resolution of the
ND converter is 10 bits or 1 in 1023. For a typical cylinder, the stroke may
be 200mm,
10
therefore the measurement accuracy will be +/-0.0196cm. If the lowest value in
the table
is less than this resolution, the EC will not be able to resolve the smaller
movements and
cannot calculate the PI%. Therefore the clock rate may need to be adjusted to
a slower
rate for calculating position.
Heart Rate
A heart rate table can be set by calculating the heart rate zones. A measured
resting heart rate (HRrest) and an estimate of the maximum heart rate (HRmõ),
preferably
adjusted for age, can be used to define a personal heart rate training zone
for aerobic
activities. The formula for estimating HRmõ is: 208 - 0.7AGE in beats per
minute.
An additional aid to determining the heart rate training zone is to calculate
the
Heart Rate Reserve (HRR), which takes into account the resting heart rate. The
heart
rate reserve method can be calculated as follows:
Subtract the resting heart rate (HRrest) from the estimated maximal heart rate
(HRmõ) to get the heart rate reserve (HRR):
HRR = HRme, - HRrest
e.g. for an individual aged 30 with a resting heart rate of 60 beats/minute:
HRR= 187-60= 127
The heart rate zone will depend on the overall fitness of the client, or user,
and
what type of exercise target they are going for. This will typically be based
on a self-
reported assessment of fitness from the client. The individual will have to
make an
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educated guess as to where he/she fits on a relatively broad, three-tier
classification,
such as:
1. Low Fitness/Beginner 50%-60%
2. Intermediate 60%-80%
3. Advanced 80%-90%
The heart rate analysis is done to indicate how much time the users heart was
in
each of the 3 zones. This can be done by looking at the HR for each stroke and
the time
for that stroke and then calculating a percentage.
The ROM% can be calculated by dividing the cylinder position at the end of
each
stroke by the maximum range of motion for the machine as defined in the
machine setup
data.
Energy
A calculation of the energy used on each stroke is done by using the same
energy
formula E = Fd = f(d2/t), except the values are per-stroke values, such as:
d=distance
moved for stroke(mm); t=time of stroke(s); and E=Energy (mJ) required to move
the
cylinder over distance d in time t. The value of f can be different for
forward and reverse
motion.
Performance Index
The PI on each stroke can be calculated by first calculating the energy rate,
followed by a comparison to the IER already calculated as part of the exercise
profile
data.
The energy rate is the amount of energy used per clock tick and can be
calculated
by taking the energy from above and dividing by the number of clock ticks for
that motion
(or stroke time divided by clock rate). A percentage can then be taken between
this
number and the targeted IER first calculated. This is the percentage of the
Pl. The actual
PI achieved on each stroke can then be calculated by taking the above
percentage and
multiplying by the target set in the exercise profile.
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Fatigue and Variance
A measure of the person's fatigue over the exercise period can be determined
by
calculating the slope of the PI% though a linear regression. A negative slope
indicates the
person is fatiguing. A simple variance to indicate consistency between strokes
can be
calculated by looking at successive PI figures and subtracting. This can be
expressed as
a % to show variation.
Calories
Because energy is being measured, this can be converted to calories using the
following formula:
C = E/4190
where E = measured energy (mJ), and C = Calories. It is possible to relate
this
value to the calories burned by the exerciser by applying the following
factors: inefficiency
in the machine mechanism; inefficiency in body movement; and body mass index.
System Implementation
As mentioned earlier, the methods described herein are advantageously
employed in the context of a computerized physical performance measurement
system. A
description of such a system now will be provided, in order to provide a
suitable context
for the application of methods and other aspects of embodiments of the present
invention.
Fig. 3 is a block and flow diagram of a computerized exercise system 200. The
system 200 includes an identification device 202 for each user, and a storage
means 204
to store a plurality of user profiles and performance data. The identification
device 202,
such as an RFID tag, can be a microchip device worn by the user on wrist or
chest. It
preferably integrates and communicates with an optional Heart Rate detection
system.
The identification device, or user identifier, 202 actuates, or activates,
each exercise
station's processor, in particular a data acquisition intelligence system.
Alternatively,
=each user can have a unique personal identification number (PIN) to enter at
each
exercise station.
The storage means 204 can be implemented as a central database or databank,
in which case it acts as the main system of data collection and data
management. In a
preferred embodiment, data stored in the storage means 204 is centrally
accessible, even
in the case where the storage means comprises a plurality of physical storage
devices.
This provides an option of distributed storage. The terms "central database"
and
"databank" in this description are used interchangeably with "storage means",
and
represent a means for storage of data, from which the data is centrally
accessible.
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Information can be collected and stored in the databank and managed by, or on
behalf of,
each user as needed. The databank can collect information from as many local
PCs as
deployed. The databank can contain, for example, the following information:
historical
workout results, exercise programs, human performance physical profiles,
training
activity, achieved results, dietary information and various predictive
analysis and
algorithms. Other information can additionally be included, such as exercise
machine
profiles. This databank can also contain proprietary, scientific and
mathematical formulas
for calculating the various performance intensity factors for each member.
The system 200 tracks individual performance of each user. Each user is
preferably automatically identified as they commence use of an exercise
device. The
computer system can also recall a program that has been previously established
for that
user and appropriately adjust the visual or other output display of the system
to allow the
user to monitor his own progress and perform at a desired personal level. In
this way,
each piece of exercise equipment is effectively customized for the individual
user and the
system tracks the individual's performance.
Referring back to Fig. 3, a plurality of exercise machine modules 208 are each
in
communication with the central database 204 via a communications network 206
to
receive a stored user profile from the central database.
In this example, the
communications network can be implemented as an Ethernet link. While only one
exercise machine module 208 is shown in Fig. 3 for simplicity of illustration,
in practice
the system 200 includes a plurality of exercise machine modules 208, as will
be
described and illustrated later. Each of the exercise machine modules 208
includes: a
sensor system, or physical performance detection system, 210; and an
electronic
controller 212.
The sensor system 210 preferably includes an exercise machine sensor, for
coupling to an exercise machine, preferably to a resistance element thereof,
to measure
performance data. For example, the sensor system 210 can include a sensor,
such as a
load cell mounted onto an exercise station weight stack, to continuously
measure the
force used for each repetition for each exercise. The sensor system can also
include an
encoder or potentiometer to be mounted on the exercise station and used to
measure
distance moved for each repetition. By adding various sensors to exercise
devices such
as bikes, treadmills, weight lifting machines, the system of the present
invention is able to
measure, calculate and provide feedback to users based on their degree of
effort and
desired goals.
The electronic controller 212 is coupled to the sensor system 210, to
determine or
calculate exercise intensity in response to the measured user performance
data, to
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compare the calculated exercise intensity with the user profile; and to
provide feedback to
the user regarding exercise intensity based on a comparison of measured
performance
data and the stored user profile. The exercise controller 212 can
alternatively be
described as being coupled to the sensor system 210 to calculate exercise
intensity in
response to the measured user performance data, to determine an exercise
intensity
indication, or parameter, based on a comparison between the calculated
exercise
intensity and the user profile, and to provide the exercise intensity
indication as feedback
to the user.
The electronic controller 212 can include a user identification unit 214 to
receive a
user profile from a central database 204. The user identification unit 214, or
data
acquisition intelligence system, can read or otherwise receive a user
identifier, such as
from an identification device 202, e.g. an RFID tag. As such, the system of
the invention
can be described as including an identification module to receive a user
profile from a
storage means in response to a received user identification. The electronic
controller 212
can also include a processor 216, in communication with the user
identification unit 214
and with the sensor system 210 associated with the exercise machine, to
calculate
exercise intensity in response to received user performance data, and to
determine an
exercise intensity indication (such as PI) based on a comparison between the
calculated
exercise intensity and the user profile. The electronic controller 212 can
also include a
feedback module 218, such as a display, in communication with the processor
216, to
provide the exercise intensity parameter to the user. In other words, this
device includes
the intelligence to identify and communicate to the feedback system, while
measuring the
physiological and physical function of the body.
In the processor 216, a data acquisition intelligence system can be
implemented
as a card that tracks all performance data including force, distance, time,
heart rate, etc.
The processor 216 preferably includes a memory with firmware or software
comprising
sequences and instructions to determine exercise intensity and workload. The
processor
can control various LED lights on the feedback module 218, which can be
implemented
as a digital feedback unit, or display unit. The processor 216 can also
communicate data
to a computer 220. The computer 220 can be implemented as a central computer,
or in a
distributed manner where the functions of the computer can be considered as
centrally
controlled, or centrally available. The processor 216 can also track and
communicate
heart rate data.
Although the system 200 has particular application for retrofitting with
existing
equipment, it can also be used with (or alternatively integrated in) new
equipment.
Furthermore, although the system is described with respect to the addition of
sensors to
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existing resistance elements on the exercise equipment, new resistance
elements can be
added which include the sensors as part thereof. Obviously, the retrofit
application
provides a cost advantage.
In an example of implementation, a user would wear a heart rate belt and scan
an
RFID tag, or user identifier 202, in front of the electronic controller 212.
Based on the
downloaded user profile, the electronic controller 212 downloads a unique set
of data
tables specifically designed for that user, and provides an indication based
on the data
tables of how much weight the user should be lifting. While the user is
exercising, the
electronic controller can show the user's range of motion for the muscles,
calculate how
much energy the user has exerted, and can count down the repetitions based on
the
number of repetitions that have already been done.
The processor 216 comprises the necessary intelligence to vary the prescribed
programming to continuously challenge the user to perform at their unique
maximum
capability while ensuring safety. At the end of each exercise session, the
electronic
controller 212 preferably automatically sends all tracked and collected
outcome data to
the central computer 220 for immediate reporting.
Automatic update of goals based on performance
The system can include dynamic, or automatic, modification or updating of a
goal
or target based on measured performance results. The system can include a
measurement module 234 for extracting measured user performance data from the
storage means or central database 204 and for comparing the measured data with
the
stored target data. For example, the data in question can be a Performance
Index (PI). If
the measured data shows that the measured PI value meets a target PI value,
the target
PI value can be increased slightly so that the user is set to improve when
coming for the
next workout. The update or modification of the goal, or target, can be
performed by an
automatic goal update module 236. The amount by which the goal, such as a PI
value, is
increased is determined by a stored progression index, which is preferably
stored in a
memory accessible by, or within, the automatic goal update module 236. The
progression
index can be a percentage by which the PI value, or any other value, is
increased if the
user reaches a target, or is decreased if the user fails to reach the target.
Although any
suitable value can be used and modified by the user, a presently preferred
progression
index value is about 10%.
Besides providing automatic updating of goals, the system can also provide the
user the ability to manually modify parameters of an exercise, such as:
weight,
repetitions, and target performance index for that particular exercise.
This user
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modification can be performed a user-accessible profile edits module 238,
which can be
accessed via a web management module 240. The profile edits module, or
training
module, can include relevant information on exercise programs as developed and
managed. It can include personal information on the user as well as desired
goals and
objectives. The section can include critical data forming the cornerstone of
health
management information. Access to the module can be provided to various users
based
on security privileges, as defined by the system or by the member. A global
target, such
as a global performance index, is then preferably automatically updated based
on any
manual user modification or change to specific exercise parameters. The global
PI
preferably cannot be changed directly by the user.
The web management module, or web site, 240 can preferably be the interface
used to manage all information gathered from all the various systems and
users. It can
feature various user interfaces for members, personal trainers, physicians,
and other
professionals based on the assigned security privileges. From this main
system, users
can be prompted to and provided with the ability to manage, accept, and modify
various
health information gathered and tracked by the system.
Integration of caloric intake with exercise program
Known systems do not provide a way for a user to easily integrate dietary
intake
with an exercise program. The system can provide a caloric intake module 242,
which
can include modules to receive and store information relating to diet and/or
intake. Meal
consumption and caloric intake information can thus be entered into the same
system
that tracks fitness. As a result, the user performance targets, or fitness
targets, for the
individual can be dynamically modified based on meal consumption. The modified
profile
based on the updated caloric intake can then be sent to the health club or the
communication module system and the user profile is updated accordingly.
Therefore, tomorrow's workout can be customized based on food consumed today
and the workout can even be customized within the same day, given that the
updated
information can be transferred almost immediately and the user profile will be
updated
accordingly. As a result, the fitness program can always ensure that the user
is burning
off more calories than are being taken in. The total caloric intake can
preferably be
updated on a periodic basis, such as at the end of each day, and can
preferably be based
on knowledge of the user's caloric burn rate. A caloric value database that
interacts with
the web module of the system of the present invention preferably includes
caloric values
relating to different types of food, which the user can select when entering
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consumption, such as by a drop-down menu. That way, the user does not need to
have
knowledge of calories associated with particular food types and amounts.
From home or office, the user can connect to a website module and enter their
dietary intake. Software in, or in communication with, the caloric intake
module calculates
the caloric impact on the overall training program and analyzes the impact on
weight
gain/weight loss based on tracked training proficiency. On subsequent
workouts, the
software utilizes this revised profile and only activates the LED light
feedback system
based on the new modified work intensity requirement. This cycle of
objectively
documenting intake and associating it to measured output will not only enhance
user
compliance, but substantially improve the ability for the user to achieve
their fitness goals.
The software can provide predictive analysis on various weight gain and weight
loss scenarios for 30 days, 90 days, 6 months, one year and longer, based on
observed
dietary intake and activity intensity. The software recommends changes to
future dietary
intake and training intensity. Having access to dietary intake information can
allow the
predictive engine to forecast both the short and long-term impact on the users
physical
condition and associated physical risks. The predictive engine can be
implemented as a
predictive profiles module 244, as shown in Fig. 3.
The predictive profiles module, or prediction system module, can be used by
the
member to analyze and determine weight gain/ loss scenarios based on measured
and
observed outcomes. Various algorithms and scientific principles can be
utilized in
determining the validity and effectiveness of various exercise training
programs and to
make the necessary recommendations to the user for change. A key prediction
component can be determined by the measure of physical activity in comparison
to the
desired goals and objectives of the training program. Based on such
measurements, the
system can advise the user of the anticipated time of progress to achieve the
desired
goals.
Fatigue & Variance
When exercising, a user typically experiences fatigue. However, known systems
do not provide the user with any indication of whether the amount of fatigue
being
experienced is normal. There is similarly no indication of whether the user is
being
consistent with respect to energy expended during a workout. Inconsistency and
unusual
fatigue are signs that a user's exercise program is not suitable and needs to
be changed.
The system can provide the ability to dynamically modify a user-specific
exercise program
based on measured fatigue and/or variance.
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A fatigue and variance module 246 is shown in Fig. 3 as being in communication
with the storage means 204, and having access to the measured user performance
data.
While this module is shown as a single module, the two functions can be
implemented
separately. With respect to fatigue calculation, this can be determined in
relation to the
calculated energy per repetition and any variation there has been between
energy per
stroke by observing the slope of a line showing the energy expended. This
shows
whether the person is exerting more energy continuously or losing more energy
continuously. In a normal healthy individual training at the full intensity, a
strength loss
rate of about 10 % is expected.
A coefficient of variance, which is a measure of consistency, illustrates how
consistently the repetitions were performed. If energy is increasing or
decreasing but the
consistency is not there, the user is not trying their best. The system looks
at the
relationship between consistency and fatigue, with ideal values being a
fatigue of about
10 % and a consistency variation of about 0 %. Within each set, the system can
collect
data relating to each individual stroke. Each stroke in an exercise (or
individual exercise
movement) can be summarized, with its distance, position, range of motion,
energy,
fatigue, heart rate, and performance. At the end of each stroke, an intensity
parameter,
such as a performance index (PI), is calculated. A summary PI is also
calculated for each
set. A personal trainer can use this data to communicate with the user and
identify areas
that need to be worked on. The system can preferably automatically update a
user profile
based on stored settings relating to fatigue and variance.
A reports module 248 can also be provided in communication with the web
management module 240. The reports module can generate user-specific reports
based
on information from the central database 204, as well as the measurement
module 234,
the caloric intake module 242, and the fatigue/variance module 246. Additional
modules
can include the ability to create custom statistics and measured outcome for
published
research. The reports module 248 will now be described in further detail.
Reports Module / Kiosk
The system preferably includes a reports module, or kiosk, 248 to generate and
provide access to user-specific reports based on measured user performance. In
physical implementation, the reports module, or kiosk, 248 can be provided at
the
computer 220. In the following description, the kiosk will be described
separately, partly
since the system can include a plurality of kiosks. The computer 220 centrally
manages
all data and communicates with all ECs in the system, preferably using
wireless
technology. The user has the option to login at the kiosk 248 before starting
an exercise
routine and accept the workout program modified by the system based on results
from
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the previous workout, the amount of consumed calories and the desired goals
and
objectives. Once completed, the kiosk 248 sends the revised exercise profile
to the
various EC units for each exercise. At the end of the workout the individual
approaches
the kiosk, activating the reporting system for outcome summary of measured
performance in comparison to their pre-established goals and objectives. A one-
page
graphical report can be generated, so that the user can evaluate their
performance and
make the necessary modifications to their exercise routines.
The kiosk software can include various equipment setup parameters and can be
used for organizing the various equipment inventories in any club and
associating them to
various software parameters. All stations are preferably initially
characterized prior to first
use. The section can include, for example: Equipment calibration screens;
Product
registration screens; Equipment ID and Data Acquisition Units association
screen; Facility
setup screen; and Protocol and communication setup screens.
The kiosk software can also include various personal setup screens for
entering
all personal data including, for example: User personal setup screens; Medical
clearance
questionnaires and signoff; Various security log-in privileges for other
users; Customized
exercise program screens; Baseline testing and goal setting screen,
anticipated trends;
and Battery of standardized templates for creating training programs.
At the beginning and/or completion of any workout, the user or the individual
responsible for the user can print various progress reports, manage and create
training
programs, and enter dietary information. At the end of each workout, the user
can have
the option to print various progress reports to review effectiveness of the
workout.
Reports can be summarized in relation to established baseline and planned
goals and
objectives and can include, for example: One page summary of current workout
results
(prints automatically at end of workout); and two to three page summary of any
number of
workouts (user defined data range).
The report module, or reporting engine, 248 can automatically provide these
results on-
line or to other communication devices including personal digital assistant
(PDA), Cell
Phone or by email, such as in PDF (portable document format) file format, or
any other
suitable data format for any other device capable of data communications.
Suitable
protocols can be used to enable communication between the network module, the
identification module, and the performance data and feedback module.
Software/Hardware Implementation
As will be understood by those of skill in the art, the methods of the present
invention can generally be embodied as software residing on a general purpose,
or other
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suitable, computer having a modem or internet connection to a communications
network.
The application software embodying the methods of the present invention can be
provided on any suitable computer-useable medium for execution by the
computer, such
as CD-ROM, hard disk, read-only memory, or random access memory. In a
presently
preferred embodiment, the application software is written in a suitable
programming
language, such as C++ or Matlab, and can be organized, into software modules
to
perform the method steps. The methods could be implemented in a digital signal
processor (DSP) or other similar hardware-related implementation.
As such, embodiments of the present invention can be provided as a computer-
readable medium including statements and instructions which, when executed by
a
computer, cause the computer to perform the steps of the method of measuring
physical
workload for a task, or the method of characterizing a physical performance
device, as
described above.
Similarly, embodiments of the present invention can be provided as a computer
readable medium comprising a data structure. The data structure can include a
physical
performance index value generated by the method of measuring physical workload
for a
task, as described above. In another aspect, the data structure can include a
physical
performance device profile generated by the method of characterizing a
physical
performance device, as described above.
The above-described embodiments of the present invention are intended to be
examples only. Alterations, modifications and variations may be effected to
the particular
embodiments by those of skill in the art without departing from the scope of
the invention,
which is defined solely by the claims appended hereto.
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