U.S. patent application number 14/609037 was filed with the patent office on 2016-08-04 for training device for determining timing of next training session.
The applicant listed for this patent is Ambiorun. Invention is credited to Richard Feichtinger, Juergen Loeschinger.
Application Number | 20160220866 14/609037 |
Document ID | / |
Family ID | 55524392 |
Filed Date | 2016-08-04 |
United States Patent
Application |
20160220866 |
Kind Code |
A1 |
Feichtinger; Richard ; et
al. |
August 4, 2016 |
TRAINING DEVICE FOR DETERMINING TIMING OF NEXT TRAINING SESSION
Abstract
A device helps a user to plan the proper timing for setting a
next training session based on the intensity of a training stimulus
of a previous session. The user is shown when there will have been
enough recovery time since the last training session to suggest the
best time for starting the next training session. The timing
recommendations are based on a supercompensation time curve that
depends on a user dependent factor that includes at least the
training load of the last training session and preferably also
depends on the training status of the user. Further parameters like
age and gender and variables like behavior affecting recovery, as
there are for example tiredness from lack of sleep, dehydration
from insufficient drinking, insufficient calories, protein, mineral
or vitamin intake, consumption of alcohol and other drugs, can
additionally be used to influence the recovery timing
calculations.
Inventors: |
Feichtinger; Richard;
(Tuebingen, DE) ; Loeschinger; Juergen;
(Tuebingen, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ambiorun |
Mainz |
|
DE |
|
|
Family ID: |
55524392 |
Appl. No.: |
14/609037 |
Filed: |
January 29, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 19/3481 20130101;
G16H 20/30 20180101; A63B 2220/40 20130101; A63B 24/0075 20130101;
A63B 24/0062 20130101 |
International
Class: |
A63B 24/00 20060101
A63B024/00 |
Claims
1. A training device for determining a timing for a next training
session, comprising: an input device receiving an indication of a
user dependent factor, the input device being at least one of a
user input device and a sensor unit that monitors an activity of
the user; and a processor that defines a time to perform the next
training session, the processor uses an equation that estimates at
least a portion of a supercompensation curve, the equation being a
function of at least one parameter that is varied by the processor
based on the user dependent factor.
2. The training device of claim 1, further comprising a storage
device that stores values of the at least one parameter for at
least two different values of the user dependent factor.
3. The training device of claim 1, wherein the user dependent
factor is a training load of the previous training session based on
a measurement by the sensor unit.
4. The training device of claim 3, wherein the user dependent
factor also includes a training status of the user.
5. The training device of claim 3, wherein the user dependent
factor also includes at least one of age and gender of the
user.
6. The training device of claim 3, wherein the user dependent
factor also includes a further variable affecting recovery selected
from the group including tiredness from a lack of sleep,
dehydration from insufficient drinking, insufficient calories,
protein, mineral, or vitamin intake, consumption of alcohol or
drugs.
7. The training device of claim 1, wherein the user dependent
factor also includes whether the user took a walk after the last
training session, a diet of the user after the last training
session, and whether the user had a massage after the last training
session.
8. The training device of claim 1, wherein the input device
includes the sensor unit, and the sensor unit measures at least one
of acceleration impacts and vibrations correlating to physical
stress.
9. The training device of claim 8, wherein the at least one of
acceleration impacts and vibrations correlate to physical stress on
a runner's legs.
10. The training device of claim 8, wherein the at least one of
acceleration impacts and vibrations correlate to at least one of
foot pronation and tibia rotation.
11. The training device of claim 1, wherein the equation is a
combination of an ascending sigmoid curve equation and a descending
sigmoid curve equation, and defines a section of the
supercompensation curve.
12. The training device of claim 1, wherein the supercompensation
curve is defined by subtracting a descending sigmoid curve from an
ascending sigmoid curve.
13. The training device of claim 11, wherein the ascending sigmoid
curve is defined as gain_a*TAN H(time_a*t-time_constant_a)+offset_a
and the descending sigmoid curve is defined as gain_d*TAN
H(time_d*t-time_constant_d)+offset_d, wherein TAN H is a hyperbolic
tangent function, gain_a, time_a, time_constant_a, offset_a,
gain_d, time_d, time_constant_d, and offset_d are parameters and t
is an elapsed time after the last training session.
14. The training device of claim 13, wherein gain_a, time_a,
offset_a, and time_d vary based on the user dependent factor,
gain_d and offset_d are proportional to gain_a, and time_constant_a
and time_constant_d are constants.
15. The training device of claim 11, further comprising a storage
device that stores values of gain_a, time_a, offset_a, and time_d
for at least two different values of the user dependent factor.
16. The training device of claim 1, further comprising an output
device that outputs the timing for a next training session to the
user.
17. The training device of claim 16, wherein the timing for the
next training session is output as the time associated with the
highest point on the supercompensation curve.
18. The training device of claim 16, wherein the timing for the
next training session is output as the time period associated with
a supercompensation section of the supercompensation curve.
19. The training device of claim 1, further comprising a remote
server with a storage device storing data for a plurality of
users.
20. A method for determining timing of a next training session,
comprising the steps of: obtaining data indicating a training load
of a training session of a user by at least one of sensing the data
using a sensor unit or receiving the data by user input using an
input/output unit; setting, by the input/output unit, parameters of
an equation that estimates at least a portion of a
recovery-supercompensation curve based on the data; and calculating
and displaying the recovery-supercompensation curve to the user on
a display of the input/output unit.
21. The method of claim 20, further comprising the step of
inputting a user's subjective evaluation of the training session,
and said step of setting parameters uses the user's subjective
evaluation.
22. The method of claim 20, further comprising the step of
determining whether the user is in a recovery phase of a previous
training session.
23. The method of claim 20, wherein the data sensed by the sensor
unit is monitored during the training session to determine whether
the data indicates a load level is exceeded.
24. The method of claim 20, wherein data from a plurality of users
is stored in a database, and the parameters of each of the
plurality of users is updated based on the collective data.
25. The method of claim 20, wherein the equation is a combination
of an ascending sigmoid curve equation and a descending sigmoid
curve equation, and defines a section of the supercompensation
curve.
26. The method of claim 25, wherein the ascending sigmoid curve is
defined as gain_a*TAN H(time_a*t-time_constant_a)+offset_a and the
descending sigmoid curve is defined as gain_d*TAN
H(time_d*t-time_constant_d)+offset_d, wherein TAN H is a hyperbolic
tangent function, gain_a, time_a, time_constant_a, offset_a,
gain_d, time_d, time_constant_d, and offset_d are parameters and t
is an elapsed time after the last training session.
Description
BACKGROUND OF THE INVENTION
[0001] The present invention relates to a device and a method
providing a technical aid for physical exercise which instructs the
user on proper timing for setting the next training session on
account of the intensity of the training stimulus of the previous
training session. The best time for performing the next training
session is a time window, which only starts after a proper recovery
time after the previous training session and is based on the
training principle of recovery and supercompensation.
[0002] Training is effective because the body is seeking to adapt
to the stress from its environment. A training stimulus is the
application of stress (the training load) and a training effect is
the body's subsequent adaptation to that stress. In training, the
desired adaptive response, i.e., the ability of the body to better
cope with similar stress situations in future, is described by a
process called supercompensation.
[0003] The supercompensation model illustrating the effect of a
training load is shown in FIGS. 1a and 1b. The first step is the
application of a training load or stress, i.e., exercise, resulting
in fatigue or tiring and the body's subsequent reaction to this
training load (phase a in FIG. 1a). The following step is the
recovery phase (phase b in FIG. 1a). As a result of the recovery
period, the energy stores and performance will return to the
baseline defined as the performance potential just before the
application of the original training stress (the horizontal line,
designated "biological state before stimulus" in FIG. 1b). The next
step is the supercompensation phase (also referred to as
overcompensation). This is the adaptive rebound above the baseline
(phase c in FIG. 1a). The following step in the process is the loss
of the supercompensation effect (phase d in FIG. 1a). If no
training stress is applied for a longer period of time, there will
be a decline in performance potential and the curve will eventually
drop below the baseline (not shown in FIG. 1). This is the
so-called detraining phenomenon. The aim, however, is to apply a
new and proper training load within the proper time window, in
particular within the supercompensation phase, which will increase
the performance potential on the long run.
[0004] The recovery-supercompensation model is a widely used
representation of the training process and graphically represented
in nearly every textbook on training.
[0005] A different explanation is the so-called two-factor theory,
or fitness fatigue model, from Banister and colleagues, with two
exponential decay functions. The premise is that the fatigue effect
of training is of shorter duration but of greater magnitude while
the fitness effect of training is slower changing and longer
lasting (often regarded as three times slower). The basic idea was
to predict the performance potential on a longer time scale, in
particular when scheduling training cycles. However, the predictive
accuracy proved to be insufficient and fitting model parameters to
actual individual training data leads to such a high variation in
time parameters that any physiological meaning, if there was any,
is lost. The same holds true for the other model suggested in the
literature, the performance potential model (Pfeiffer 2014,
International Journal of Computer Science in Sport, Volume 7
Edition 2, 13-32). Moreover, the fitness fatigue model cannot
express overtraining, while the performance potential model has an
overflow term to particularly model breakdown of performance after
overtraining. However, the performance potential model cannot model
the supercompensation effect of a single training load at all,
rather it only models long term effect of continuous training
loads. Brueckner 2008 (E-Journal Bewegung and Training, 2 (2008),
51-65) and Brueckner 2006 (PhD Thesis,
Christian-Albrechts-Universitat zu Kiel) [both in German] developed
a model similar to the performance potential model with flow rates,
but which is able to model the supercompensation effect of a single
training load. However, the supercompensation effect modeled is
proportional to the training load, which is also the case in a
single overload of excessive training, therefore failing to model
the lack of positive training effect after excessive training. He
found that his model's parameter can be derived by fitting to 3
weeks of training data, after which the prognostic quality for one
week of further training is fairly good, but thereafter
mediocre.
[0006] Another model put forward by Mader 1990 (Deutsche
Zeitschrift f. Sportmedizin, 41(2), 40-58 [in German]) "simply"
tries to model the turnover of the underlying biological components
by differential equations. However, many of the regulative
components are still to be discovered and only on very few
components good time course information is available. And even if
successful one day, the resulting calculation model will certainly
not be simple and easy to handle at all.
[0007] The known quantitative models try to explain more or less
the basic training phenomenon, which is still hardly understood in
terms of physiology. Historically, the description of
supercompensation in sports goes back to the Russian physiologist
Jakovlev who observed that the re-synthesis of glycogen stores in
muscle after exercise not only fills up but overshoots for a short
period of time. Jakovlev himself built on work of earlier
researchers, too, in particular the German pathologist Weigert on
wound healing, the famous Russian researcher I. P. Pawlow on
pancreatic function, his student G. V. Folbort on heart recovery
and on the Russian biochemist Engelgard. In fact, Jakovlev
explicitly referred to the phenomenon as Engelgard's principle.
Jakovlev also found it in creatine phosphate, enzymatically and
structural muscle proteins, phospholipids and the quantity of
mitochondria within the muscle fibers and the quantity of muscle
fibers themselves. Jakovlev realized, that the energetic
supercompensation after muscle work has a prominent role for the
re-synthesis for proteins and adaptations to future loads resulting
in a structurally and physiologically stronger muscle.
[0008] Interestingly, it was found more recently that reactive
oxygen species (ROS) actually work as a signal from stressed
mitochondria to induce the repair and adaptation process. For
review see Powers 2009 (Exp Physiol 95 p. 1-9), Powers 2011 (J.
Physiol. 589, 2129-2138). Indeed, it could be shown that taking a
lot of antioxidants actually prevents the training effect of
exercise as well as the health promoting effects of exercise
(Ristow 1990, PNAS 106, p. 321-333).
[0009] There is very limited quantitative data on supercompensation
timing and therefore it is used as an explanatory principle rather
than as a calculation model. The German textbook of Grosser,
Bruggemann and Zintl of 1986 states in the diagram on p. 11 the
recovery phase (to the point of the maximal supercompensation) a
time frame of 2 to 3 days and the beginning of the fading of the
supercompensation effect at latest after 3 days (reproduced on
above mentioned PhD Thesis Brueckner 2006, p. 13). However, the
usefulness of the supercompensation model has also been heavily
criticized, in particular by Mader 1990 (Deutsche Zeitschrift f.
Sportmedizin 41(2), p. 40-58 and Friedrich & Moeller 1999
(Leistungssport 5 p. 52-55) [both in German language]. More
discussion [all in German languages] on the subject can be found in
Tschiene 2006 (Leistungssport January 2006, p. 5-14), Platonov 2008
(Leistungssport February 2008 p. 15-20), Hottenrott & Neumann
2010 (Leistungssport February 2010, p. 13-19).
[0010] One of the issues is that various physiological entities
have different time courses of recovery and supercompensation.
Restoration of glycogen stores is certainly faster than building up
of new mitochondria, with measures on fitness level lying in
between. For an illustration see Heterochronism_of_adaptation.svg
in http://en.wikipedia.org/wiki/Supercompensation.
The most important point, which was originally pointed out by
Jakovlev, however, is to re-build and better over-fill
(supercompensate) the energy stores before the next training in
order that the body has enough energy for the anabolic processes of
the more long term adaptations while at the same time using up
energy during the next training work. Therefore alone the knowledge
of a minimal recovery time, at which all the more short term
recovery processes have been finished and the more long term
effects have at least progressed to some extent, is of a big
benefit.
[0011] To cope with the long term adaptations it is well known in
sports training to alternate a few periods of heavy training load
with a period of little load, and, in longer time frames, use
seasonal alternations in order to allow recovery of longer lasting
anabolic processes. This has been described under the term
periodization in Periodization--Part I (National Strength and
Conditioning Association (NSCA) Journal Volume 15(1), 1993, p.
57-67). Further discussion [all in German language] can be found in
Verchoshanskij 1998 (Leistungssport May 98, p. 14-19), Platonov
1999 (Leistungssport January 99 p. 13-17) and Selujanov 1999
(Leistungssport February 99, p. 13-14).
[0012] The main other criticisms on the supercompensation "model"
were that it is purely descriptive without explaining the
underlying mechanism, that it lacks differentiation on age, on
gender, on training status and on individual training capabilities
Friedrich & Moeller 1999 (Leistungssport 5 p. 52-55). In fact,
from a quantitative point of view, there is no model of
supercompensation, none of the discussion literature referred to
mathematical models of calculating supercompensation.
[0013] In a singular piece of evidence on the influence of age on
the timing of the supercompensation process for an athlete, the
peak of the supercompensation point was estimated on one well
trained person with his increasing age and be found to be 60 hours
(2.5 days) at 47 years of age, 75 hours (3.1 days) at 53 years of
age and 88.3 hours (3.7 days) at 58 years of age (Mitsumune &
Kayashima 2013, Asian J Sports Med 4, 295).
[0014] In view of the above discussion, it can be gathered that
supercompensation is widely used as an explanatory principle to
teach the training effect, but actual training planning is mostly
based on experience rather than on quantitative science. The
existing quantitative training models try to calculate future
performance of an athlete by fitting the model to historic training
data of that athlete, however, the predictive quality is regarded
as insufficient at least for training advice for high-level
athletes. None of the quantitative models can mimic the
supercompensation effect after a training session and dependent on
the particular load of that last training to comply with the figure
of Klavora p. 6 which can be found reprinted in Periodization--Part
I, National Strength and Conditioning Association Journal 15 (1),
1993, p. 57-67 on p. 62 and attached here as FIG. 1b.
[0015] Devices and methods to support the athlete in their training
planning and timing are known.
[0016] WO2013/132141 discloses a system, method and computer
program product for gaining a balanced health and fitness regime,
including a user interface configured for receiving and displaying
information regarding muscle recovery times of workouts of a user.
It provides the user via a device with a method and an interface to
track, analyze and learn post exercise recovery (e.g.
supercompensation) times (paragraph 0029). It further discloses
that the recovery time may depend on the status of the user and
specifically suggests a preset of 5 days for an athlete and 7 days
for a recreational user at 80% of the total workload (paragraph
0042). It further takes into account that the recovery time depends
on the workload of the previous session and discloses a method to
individually calibrate and fine tune the recovery time depending on
the workload of the previous session by specific manual input
screens (paragraphs 0046 and 0047). It also discloses that it
provides the user with tools for entering recovery influencing
behavior like sleep, quality of nutrition or stress level and for
entering the user's subjective feeling e.g. muscle soreness a
couple of days after working out, including Delayed Onset Muscle
Soreness (DOMS), etc. (paragraph 0044). Although WO2013/132141
discloses a system for teaching the user the proper recovery time
before starting the next training session and can be individualized
to a wide extent, it restricts this to a single number of minimal
recovery time and fails to give any method of calculating the
supercompensation curve or to teach the user how the shape of a
supercompensation curve would depend on the workload of the last
training session. Therefore it cannot to teach the user that
excessive training diminishes potential training benefits from
supercompensation, or that a weak training diminishes potential
training benefit from supercompensation, or that a very long
recovery time will lead to a decline and eventually loss of the
supercompensation benefit.
[0017] U.S. Pat. No. 8,348,809B2 describes a device that suggests a
higher or lower training load in the next training session on
account of the measured history of the previous training loads
"thereby stimulating mechanical loadability of the user through
supercompensation" (last feature of claim 1).
[0018] US2014/0134584 discloses the general concept of determining
a timing of a next training session. It discloses a system that is
used to determine quantitatively the effects of training load,
intensity, and duration of each training session (paragraph 0091).
Results from numerous tests can serve as a basis to plot the
subject's actual stress-breakdown-recovery-supercompensation curve
(paragraph 94). FIGS. 11-14 show that tests are performed to
determine when the subject reaches the supercompensation stage
(e.g. FIG. 14). Although US2014/0134584 discloses that a
supercompensation cycle could be drawn on a multitude of test
results on a user, it also fails to disclose, teach or suggest the
calculation of the supercompensation curve with a mathematical
model. Therefore it cannot teach the user that excessive training
diminishes potential training benefits from supercompensation
unless the user actually experiences a decline in performance after
several excessive training loads (paragraph 0133) and measured with
the suggested multitude of performance measurements (e.g. FIG. 8).
Further it cannot teach the user that a weak training diminishes
potential training benefit from supercompensation unless the user
actually fails to experience an increase in performance after
several weak training loads and measured with the suggested
multitude of performance measurements. This leads to a strong
problem: in case the user would not perform the suggested multitude
of performance tests very often, i.e. several time within one
supercompensation cycle and therefore much more frequently than the
training sessions themselves, the user is unable to distinguish
whether the reason for a failure to experience an increase in
performance after several training sessions is actually caused by
not enough recovery time, by excessive training load, by too weak
training load, or by too long a recovery time. A recreational user
doing performance tests only at the beginning of a training session
and not in between two of them would be completely unable to get
enough data points to "define the subject's actual real training
curve" (paragraph 0093). However, typically athletes and even more
so recreational users do performance tests at a low frequency, much
lower than the frequency of training sessions themselves, making
the disclosed method useless for them.
[0019] US 2007/0293371 is pertinent in that it provides a
computation of a regeneration condition (paragraphs 0044-0047),
however it fails to disclose defining a calculation method of
defining the supercompensation curve or any calculation method
indeed.
[0020] For measuring a training load, most prior art is based on
heart rate monitoring. The Polar wristwatches are for example
described in U.S. Pat. No. 7,914,418 B2 and U.S. Pat. No. 8,512,238
B2, the Suunto wristwatches in US 2011/263993 A1, US 2012/215116
A1, US 2013/339409 A1 and US 2014/018945 A1. Firstbeat Technologies
describe their methods in US 2006/004265 B2, US 2006/032315 B2, US
2009/069156 B2, US 2010/216601 B2, WO 2009/133248 A1, WO
2012/140322 A1 and WO 2013/068650 A2 as well as in three white
papers: "An Energy Expenditure Estimation Method Based on Heart
Rate Measurement", "Indirect EPOC Prediction Method Based on Heart
Rate Measurement" and "EPOC Based Training Effect Assessment",
available on the company's website.
[0021] However, the prior art does not disclose, teach, or suggest
a calculation method for the supercompensation curve or presenting
a supercompensation curve depending on individual parameters to
teach the user at which time point of the supercompensation he or
she is according to the recovery time after the last training
session or teach the user how the form of the supercompensation
curve dependent on the load of the last training session will
provide a time window (the supercompensation window) for optimal
timing for a next training session and whether the work load of the
last training session will give a more or less pronounced
supercompensation phase to expect more or less benefit from
training at the optimal time point (the maximum of the
supercompensation curve) or within the supercompensation window
with the ultimate goal to increase the performance potential in the
long run.
SUMMARY OF THE INVENTION
[0022] The present invention allows the user to plan the proper
timing for setting a next training session on account of the
intensity of a training stimulus of a previous session. The
invention shows the user when there will have been enough recovery
time since the last training session to suggest the best time point
and/or time window for starting the next training session. The
invention bases the timing recommendations on a supercompensation
time curve that depends on a user dependent factor that includes at
least the training load of the last training session and preferably
also on the training status of the user. Further parameters like
age and gender and variables like behavior affecting recovery, as
there are for example tiredness from lack of sleep, dehydration
from insufficient drinking, insufficient calories, protein, mineral
or vitamin intake, consumption of alcohol and other drugs, can
additionally be used to influence the recovery timing
calculations.
[0023] As described above, devices and methods for estimating the
training load are available to a person of ordinary skill in the
art based on heart rate monitoring. While the training load, or
parameters like training intensity and training time, of which
training load can be calculated by integration, could be entered
after each training session in a software adopting the inventive
method, in a preferred instance of the invention the training load
will be measured by at least one sensor. Data detected by the
sensor is evaluated and provided as input into software
automatically. Sensors useful to measure the training load are
known to persons skilled in the art, like heart rate monitors,
simple activity monitors for detecting steps, pressure or force
sensors, accelerometer based speed and distance monitors (e.g.,
U.S. Pat. No. 6,301,964B1), more advanced inertial measurement
units including gyroscopes, speed and distance monitors based on
global navigation systems like GPS. The latter systems can detect
changes in height above sea level on a limited accuracy, therefore
often air pressure sensors are used to give more accurate
information on height changes.
[0024] In a preferred embodiment of the invention the sensor(s)
used not only provide information on the amount of training work
(time, speed, distance, height changes, calories burnt) but also on
the impact of the training on the subject, like heart rate changes,
heart rate variability, acidity, lactic acid levels, impacts and/or
vibrations (or similar parameters detected by accelerometry),
pronation and/or tibia rotation (e.g., U.S. Pat. No. 7,912,672B2),
or similar parameters detected by gyroscopes.
[0025] Many studies have addressed the quite high rate of injuries
of leisure runners which is found to be between 1/3 and 2/3 of all
runners participating in such studies during the study within one
season, almost all of them suffering of musculoskeletal pain, with
the most prominent areas being the knee and the Achilles heel. The
exact causes, however, are still unknown to a great extent
(Heiderscheidt J Orthop Sports Phys Ther. 2014 October;
44(10):724-6). Nevertheless, as musculoskeletal injuries are
certainly caused by physical stress, physical load measurements on
the runners' legs are preferred over heart rate based
measurements.
[0026] In a preferred embodiment of the invention measurements
correlating to physical stress on the musculoskeletal lower body
parts (including ligaments and fascia), like acceleration impacts
and/or vibrations, like foot pronation and/or tibia rotation or
similar measurements are evaluated. Such measurements are evaluated
not only for the amount of physical work done, but alternatively or
additionally evaluated to indicate the joint loads, i.e., the
accumulation physical stress on the joints, in particular the foot,
knee and/or hip. This can be achieved for example by integrating a
squared product of the measurements, as individual strong impacts
typically have a more than linear impact on tissues.
[0027] Characteristics influencing the joint load, such as the
suitability of a user's shoes for the individual running style of
the user, the amount of wear of the shoes, the underground
structure, and running speed are thereby monitored. The accumulated
joint load during a training session or for several training
sessions can be used to give a warning feedback (alert) to avoid
overload, for example by suggesting to reduce the load. These
suggestions could be, for example, to return home when 50% of the
individually set load maximum is reached, to reduce speed, or to
discontinue high training load. In a preferred embodiment, the
individually set load maximum is derived by a standardized training
status protocol, as known to persons skilled in the art and which
can be facilitated by evaluating the measurements. In a more
preferred embodiment, said load maximum is automatically derived
and/or adjusted during the training session, e.g., by evaluating
measurements for signs of fatigue, like reduced step length or step
height, or increased stance time (as fraction of total step time)
or decreased impacts or increased pronation velocities or similar
evaluations of the measurements.
[0028] In a preferred embodiment the user's subjective evaluation
of the training session and the recovery process are recorded by
standardized input forms. In particular, any incidences of pain and
their strength and persistence are recorded, both during the
training session and thereafter, including Delayed Onset Muscle
Soreness (DOMS). In case an incidence of pain has been recorded, in
the next training session said warning feedback (alert) is adapted
to occur at a level below the load level, where the first incidence
of pain have occurred. The safety margin, how much below said load
level the warning will occur preferably depends on the strength and
persistence of the pain.
[0029] In a preferred embodiment many users' successive training
loads and training frequency (and thereby recovery timing) are
stored together with his or her objective measurements based on
sensor evaluation as described above and/or subjective evaluations
as just described are stored on a central server and analyzed to
gradually improve the calculation methods, calculation parameters
and eventually the suggestions to the user based on the current
measurement data.
[0030] In a preferred embodiment the system is capable of giving
real-time feedback on certain evaluations to allow the user to
optimize his or her training, for example to optimize the running
style. Parameters which are regarded as important for the running
style are for example the step frequency vs. step length, with
beginners often taking steps too long instead of raising the step
frequency. Similarly in cycling beginners often step too hard
(applying too much torque) instead of raising the pedaling
frequency. Another parameter important for running style is the
kind of foot strike, i.e. heel strike, mid-foot strike or toe
strike. Furthermore, the height of the foot during swing time is
important, as a higher distance from the ground reduces the length
of the leg leverage arm and reduces the necessary energy for
swinging the leg upfront. Further important parameters which are
considered in running are the outward or inward rotation of the
foot at stance, the pronation of the foot during stance and the
rotation of the lower leg (tibia rotation).
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] In the drawings, wherein like reference characters denote
similar elements throughout the several views:
[0032] FIGS. 1a and 1b are prior art depictions of a
supercompensation curve;
[0033] FIG. 2a shows a supercompensation curve with an ascending
sigmoid curve and a descending sigmoid curve that added together
form the supercompensation curve;
[0034] FIG. 2b shows the same supercompensation curve as FIG. 2a
with as presented to the user;
[0035] FIG. 3 shows a supercompensation curve for a better trained
subject, i.e., an athlete;
[0036] FIG. 4 shows a supercompensation curve for the case when the
previous training stimulus was weak;
[0037] FIG. 5 shows a supercompensation curve for the case when the
previous training stimulus was very strong;
[0038] FIG. 6 shows another supercompensation curve with a short
optimal period for a next training session;
[0039] FIG. 7 is a table showing training status;
[0040] FIG. 8 is a table of parameter values for an equation of the
supercompensation curve;
[0041] FIG. 9 is a table of parameter values for an equation of the
supercompensation curve dependent on diverse training loads
[0042] FIG. 10 is a table showing further calculation factors for a
parameter value for the equation of the supercompensation curve
dependent on the user's behavior during the recovery process;
[0043] FIG. 11 is a table showing feedback types to the user based
on values of the supercompensation curve of FIG. 2;
[0044] FIG. 12 shows selected sensor signals and filtering of
running steps used to obtain time points for calculation;
[0045] FIG. 13a shows the peripheral sensor unit (e.g. shoe
sensor), FIG. 13b the input/output device (e.g. smartphone), FIG.
13c the remote server for data storage;
[0046] FIG. 14 is a flow chart according to an embodiment of the
invention in use; and
[0047] FIG. 15 is a flow chart according to another embodiment of
the invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0048] The inventors of the present invention have provided a
device for modeling a recovery-supercompensation curve (hereafter
referred to as SUPERCOMP), which includes the recovery phase,
supercompensation phase, and loss of supercompensation phase, i.e.,
phases b to d in FIG. 1a. The training load phase, i.e., phase a in
FIG. 1a, is typically on a much shorter time scale and is not of
interest for the purpose of guiding the athlete in the recovery and
supercompensation training. In a preferred embodiment, the
SUPERCOMP curve is a combination of at least two curves, an
ascending s-shaped curve (sigmoid) and a second descending s-shaped
curve (sigmoid) which is longer in scale and less pronounced in
amplitude, as exemplified in FIG. 2a. S-shaped, or sigmoid, curves
are often used in biology representing growth and saturation
effects. If growth of N over time t is dN/dt=rN*(K-N)/K, with r
being the growth rate and K the carrying capacity, the solution is
the sigmoid function N=K/(1+e.sup.-r*t+c), with the constant c
locating the process in time. Besides some scaling factors it is
mathematically equivalent to the hyperbolic tangent function (in
short TAN H): 1/(1+e.sup.-t)=1/2*(1+TAN H(t/2)), which is used in
the following for the simpler notation.
[0049] The SUPERCOMP curves in the examples described below are a
function of time t with time given in days since the end of the
last training session. In one specific embodiment of the present
invention, the SUPERCOMP curve is an ascending sigmoid curve, from
which a second sigmoid curve (effectively constituting a descending
sigmoid curve) is subtracted, as follows:
SUPERCOMP=gain_a*TAN
H(time_a*t-time_constant_a)+offset_a-gain_d*TAN
H(time_d*t-time_constant_d)-offset_d.
[0050] There are a total of 8 parameters specifying and linking the
ascending and descending sigmoid functions, respectively. To keep
the calculation model simple, some of them can be fixed at
reasonable values, however, some at least are preferred to vary
with user dependent values, as described below.
[0051] In the following examples of the invention, of the 8
parameters:
[0052] 4 parameters, time_a, gain_a, offset_a and time_d, vary with
user dependent values;
[0053] 2 parameters, gain_d and offset_d vary in a fixed
proportional with gain_a;
[0054] 2 parameters, time_constant_a and time_constant_d are set
constant.
[0055] The time scale for the descending sigmoid curve is generally
longer, for example longer by a factor of 3, in which case time_d
is time_a/3. The constants which locates the process in time,
time_constant_a and time_constant_d, respectively, are for example
2 days in both cases, meaning 2 days as peak ascending speed and 6
days (because of the time scale factor of 3) as the peak descending
speed. The peak ascending speed and the peak descending speeds are
the points of maximum slope values, as shown in FIG. 2a. The point
of the maximum slope values are also the turning points of the
ascending and descending sigmoid functions, respectively.
[0056] The scaling factors are for example 3/4 for the ascending
sigmoid for gain (gain_a) and offset (offset_a), resulting in a
raise from 0 to 150% in the example of FIG. 2a, and -1/2 (which is
-2/3 of the ascending scaling factor) for the descending sigmoid
(gain_d and offset_d) in the example of FIG. 2a, which means it
drops from 0 to -100%. Added together, the resulting curve will
rise above 100% at 2.5 days to a peak of 127% at 3.5 days and falls
below 100% at 6 days. The time window between 0 and the raise above
the 100% reference line is the recovery phase (0 to 2.5 days in
FIG. 2a). The time window between the raise of the
supercompensation curve above the reference line and the subsequent
drop of it below the reference line is the supercompensation phase
(2.5 to 6 days in FIG. 2a). In case the supercompensation curve
does not raise above the reference line, for example when the
previous training load was small, the recovery phase is until the
peak (maximum) of the supercompensation curve and there is no
supercompensation phase (with the ideal timing of the next training
as soon as possible after said peak, for practical purposes within
a day or two).
[0057] The scaling factors and the resulting values are somewhat
arbitrary and chosen for an intuitive representation rather than
representing a physiological measurement like the reduction of a
performance potential (100% being the performance potential before
the last training session, the reference line), which would be a
few percent drop only. Scaling factors could be easily adapted to
represent such physiological values. The main purpose is the proper
combination of the ascending and descending sigmoid functions
(i.e., the relative scaling factors of the two sigmoid functions
are most important) to retrieve a graph which represents the timing
of the supercompensation cycle and derive and visualize helpful
timing information and provide a motivational tool for the user for
his or her training sessions. Therefore the decay after the
supercompensation window is quite strong and for motivational
purposes (to stimulate a new training session soon) rather to
represent a true rapid loss of performance potential after the end
of the supercompensation phase. Scaling factors of the descending
sigmoid of 1/3 instead of 2/3 of the ascending sigmoid would result
in a never ending supercompensation phase (the curve never dropping
from above to below the reference line), failing to visualize the
detraining phenomenon as described in the introduction. Therefore
preferred scaling factors of the descending sigmoid are more than
1/3 of the ascending sigmoid.
[0058] In a preferred embodiment the recovery-supercompensation
curve is visualized to the user in a graphics display. As shown,
for example in FIG. 2b the user can more readily see how the
recovery is predicted to evolve in time, when will be the time
window of the supercompensation phase and at which time point to
best start the next training session, all at a glance. The curve in
FIG. 2b is the same as the curve in FIG. 2a, but is presented so
that the recovery and super-compensation periods are easily
discerned by the user. The time scale can name the days of the week
for easy scheduling (FIG. 2b). The remaining time of recovery to
the optimal time point can be calculated and shown, e.g. in a
balloon, which preferably moves with time on the
recovery-supercompensation curve (FIG. 2b). Additional motivational
texts can be provided by text or speech messages, examples are
given in the table in FIG. 11.
[0059] In a preferred embodiment the graphical display changes its
color with respect to the current status with the
recovery-supercompensation cycle. For example, shortly after a
training session, when still in the recovery process, dun-colored
blue-green colors suggest to the user that he or she should wait to
gain more energy, while hot orange colors during the
supercompensation phase suggest to the user that he or she is full
of energy and should start a new training session soon. Examples
for a color coding scheme are given in the table of FIG. 11. The
specific color schemes described are merely examples of the general
concept of changing display color to match the different phases of
the SUPERCOMP curve.
[0060] In FIG. 2b and in FIGS. 3-6 the labeling of the y axis below
the reference line, is the inverse (1--SUPERCOMP) and drawn with an
inverted scale, as this represents the training stimulus, one of
the parameters to vary with the user's input in a preferred
embodiment.
[0061] In a preferred embodiment the parameters vary with the
training status of the user since one of the desired training
effects is the faster recovery process in better trained subjects.
For example, better trained subjects, i.e., athletes, will have a
shorter time scales, which results in an optimal training period
after 2 to 3 days (FIG. 3).
[0062] Training status can be obtained by user's input or
calculated from measurements recording the training sessions by
technically assisted training monitoring systems like heart rate
monitoring, location based monitoring (like GPS or local timing
infrastructure for triangulation), pressure or force based
monitoring, accelerometry based activity monitoring, camera based
activity monitoring and/or others monitoring principles. An example
of the training status is given in the table of FIG. 7 which simply
depends on the weekly amount of training. In a preferred embodiment
the intensity of the training is taken into account in addition
(e.g. the running speed, the slope). In a preferred embodiment the
development of the training status is monitored and corrected
appropriately. In a further embodiment an increase in training
status is displayed to the user to provide positive feedback for
his or her training effort.
[0063] In another embodiment the parameters vary with the training
stimulus induced by a training load. The training stimulus is given
in a percentage value of the user's individually defined training
load level. For example, 100% training load to be the user's normal
full training mileage at the aerobic/anaerobic threshold in long
distance running. The training stimulus of each training session
can be derived by a user's input (subjective value), by a
measurement value related to a reference training session
(objective value), or a combination of both. Users with a higher
training status will need a higher training load to achieve a
certain training stimulus. A user giving only a weak training
stimulus will have a decreased scaling factor, without or barely
reaching a supercompensation level (compare FIG. 4 to FIG. 3).
However, with a weak training stimulus as in FIG. 4 the amount of
time necessary for recovery is reduced, thereby allowing a new
training session to be started earlier (comparing FIG. 4 to FIG.
3). Also, the new training session can be harder, have a higher
training stimulus, after a weak training stimulus.
[0064] In contrast, a user with an exaggerated or hard training
load will expand the recovery time vastly, missing or almost
missing the supercompensation effect (FIG. 5, compare to FIG.
3).
[0065] In a preferred embodiment, the recovery time will depend on
the user's behavior during the recovery period. Negative effects,
i.e., recovery delaying factors, are for example tiredness from
lack of sleep, dehydration from insufficient drinking, insufficient
calories, protein, mineral or vitamin intake, consumption of
alcohol and other drugs. Further positive effects for recovery
(apart from avoiding negative effects) which can be taken into
account are for example massages, walks. A delayed recovery
prolongs the ascending time scale without affecting the descending
time scale giving the user only a very short optimal period for the
next training session (FIG. 6).
[0066] The parameters for different use cases can be stored in a
table as exemplified in FIG. 8 and the most appropriate taken for a
specific situation.
[0067] More detailed tables can be stored as exemplified in FIG. 9
for the effect of training stimulus on the parameters and the most
appropriate taken for a specific situation. Even more details can
be obtained by interpolating between table entries. The choice for
the parameters can be calculated on a user's input of his or her
training performed or calculated from measurements recording the
training sessions by technically assisted training monitoring
systems like heart rate monitoring, location based monitoring (like
GPS or local timing infrastructure for triangulation), pressure or
force based monitoring, accelerometry based activity monitoring,
camera based activity monitoring and/or others monitoring
principles.
[0068] Training stimulus can raise above 100%, if one is performing
an unusual hard training session.
[0069] In a preferred embodiment the parameters' variation with
training status, training stimulus and other factors are calculated
by specific formulas.
[0070] In a specific example given below, training stimuli below
50% and above 50% have different calculation formulas.
Example Calculation of Parameters for Training Stimuli Below 50%
(Very Weak Training Stimulus)
[0071]
time_a=-1*(training_status+2.5)*training_stimulus+0.8(training_sta-
tus+2.5)
time_d=training_status/15
gain_a=training_stimulus/2
offset_a=1-gain_a
Example Calculation of Parameters for Training Loads Above 50%
[0072] time_a=(0.24*training_status+0.6)*(1-TAN
H(1.6*(training_stimulus-0.5)))+0.08*training_status
time_d=training_status/15
gain_a=training_stimulus-0.25
offset_a=0.75(constant)
[0073] In a preferred embodiment parameters like time_a can be
further individualized by manual input of recovery influencing
behaviors (as listed above) and/or reported injuries. As an example
the user is given a 7 point scale to self-qualify on his or her
recovery process. Based on this settings a correction factor for
time_a is taken, which is multiplied to the previously calculated
time_a (see FIG. 10).
[0074] Alternatively, each of the individual recovery measurements
could be asked for separately and a resulting correction factor
calculated from multiplying the individual correction factors, each
of them for example ranging between 0.85 and 1.05. Negative
influences have a stronger weight than positive influences, so that
even more than one positive influence cannot make up for one strong
negative influence.
[0075] In a preferred embodiment recovery influencing behavior is
in addition to or alternatively to manual input automatically
retrieved by behavior monitoring systems, such as for example
fitness monitors, sleep trackers, systems recognizing the activity
of daily living (ADL), systems measuring the hydration status or
other monitoring systems.
[0076] In a preferred embodiment the recovery process is monitored
for example by systems monitoring heart rate changes, heart rate
variability, redox status or components involved in ROS (reactive
oxygen species) signaling.
[0077] In a preferred embodiment parameters like training_status
can be further individualized for a personal trainability
correction factor that takes into account gender and/or age of the
person. For motivational reasons, reported training_status is not
changed but corrected with a personal trainability correction
factor.
[0078] The correction factor can simply be calculated from
chronological age above a certain age, for example by the formula
1-((age[years]-constant)/scale), with the constant parameter for
example being 25, as at this age the trainability is at the highest
point and the scale parameter for example being 100 to fit the age
over 25 to the scale of the training_status. A person with 55 years
of age would therefore result in a correction factor of
1-((55-25)/100)=0.7, which is multiplied with the training_status
as found above by the activity level, whether by manual input or
found automatically with the help of sensors.
[0079] In a preferred embodiment the evaluations of the training
sessions of multiple users are stored in a central database which
resides on a remote server and used to improve calculation
parameters. For example, the correction factor for age as given
above might be found to be too low (the difference between younger
and older athletes actually still higher) or too high (the
difference between younger and older athletes actually not as
high), and subsequently changed to values better fitting the data
of many comparable users, or many users over a several year's
period of time.
[0080] For another example a possible gender difference is
analyzed. Though it is known that men and women have a difference
in trainability, it is not known to our knowledge whether there is
a difference in the time course of the recovery process. Therefore
the specific formulas disclosed do not have a gender difference.
However, by analyzing many training sessions of many users, of both
men and women, the data might reveal differences, for example that
women have a slightly slower time course than men, in which case
the timing parameters, e.g. time_a, would be slightly lowered for
women.
[0081] In case of two consecutive trainings in short time interval,
when the recovery phase of the first training session was not over
yet, i.e. when the SUPERCOMP calculation did not cross the
reference line yet, the remaining value at the start of the second
training session is in a preferred embodiment added to the training
stimulus revealed by the work load of the second training session.
For example if the recovery process after the first training is
calculated as shown in FIG. 6 and the second training session
already started after 2 days, the value at this time point,
approximately 40%, is added to the training stimulus of the second
training session. In case this is 100%, the resulting sum of 140%
is used for the calculation of the recovery-supercompensation curve
of the second training session, leading to a result as shown in
FIG. 5 rather than in FIG. 3, effectively carrying over another day
for recovery in this example.
[0082] However, even if the suggested recovery periods are all met
in several consecutive training sessions, there is still some carry
over to consider. As described in the introductory part, there are
various physiological recovery processes with different time
courses going on after exercising thus periodization of training
with tapering phases is commonly planned by athletes and trainers.
Therefore another very useful example of evaluating a multitude of
training sessions from multiple users is to reveal proper carry
over parameters for timely scheduled consecutive training
sessions.
[0083] In a preferred embodiment the training status is
automatically adapted by evaluating measurements for signs of
fatigue, as described above. As a specific example the calculation
of the stance time (as fraction of total step time) is used. While
the total step time is fairly easy to measure with accelerometers
since the impact is very pronounced, the end of the stance phase,
the toe-off event, is very difficult to detect with accelerometers.
Therefore the readings of a gyroscope of the sagittal plane
(Gyro-SP, around the left-right axis) are preferred. A simple IIR
low-pass filtered signal of said gyroscope readout by use of:
y(n)=.alpha.*x(n)+(1-.alpha.)*y(n-1), where of the current reading
x(n) only the small fraction a is taken and added to previous
calculation output diminished by the same small fraction a, is
used, which, on the proper choice of a, which is found to be 4/f, f
being the recording frequency in Hz, gives a phase shift of the
right amount to have the maximum of the filtered signal to coincide
with the toe-off event. This is the case for both walking and
running (for the same value of .alpha.) and can therefore be used
to distinguish between those two. In fact the comparison of the
duration of the stance phase (time of impact to time of toe-off) to
the duration of the swing phase (time of toe-off to time of next
impact) is exactly the definition of distinguishing between walking
(longer stance phase) and running (longer swing phase), so this
simple memory saving filter can be used to distinguish between
running and walking steps. An example of two consecutive running
steps is shown in FIG. 12.
[0084] In a preferred embodiment the system is giving real-time
feedback on certain evaluations to allow the user to optimize his
or her training as described above. As a specific example the
feedback to train optimal step frequency is provided. Actual step
time is measured by the time difference of two consecutive impacts,
as detected by an accelerometer and/or a gyroscope (see FIG. 12).
Step length is calculated by calculating orientation in space by
fusing 3-axis accelerometer and 3-axis gyroscope data with or
without 3-axis magnetometer, as known by persons skilled in the
art. Once the orientation in space is known, the vector of earth
gravity can be subtracted from the accelerometer signal. The
remaining accelerometer signals are integrated with setting zero
speed at mid stance phase and any integration drift is subtracted.
The drift corrected speed in direction of running is then
integrated once more to provide step length. Individual correction
factors might be obtained by a reference test of running a known
distance. The optimal step time can for example be calculated as
120-9*step_length [m]/(230-0.5*body_height [cm]). Alternatively to
the body height the leg length might be taken, if known to the
user, with a different multiplication factor (e.g. 1.1). The actual
step time is set in relation to the optimal step time, which
according to the above formula, depends on step length and body
height (or leg length). The amount of deviation is given as
feedback to the user, which feedback can be visual, acoustic or
vibrational, or a combination thereof.
[0085] FIGS. 13a, 13b and 13c are block diagrams of a sensor unit
100, an input/output unit 200, and a remote server 300 according to
an embodiment of the invention. The sensor unit 100 includes a
processor 102 and sensors 104. Although two sensor are shown, the
actual number of sensors may be one or more as required. The
sensors 104, as discussed above, can include at least one of a
heart rate monitor, activity monitors for detecting steps, pressure
or force based monitors, accelerometer based speed and distance
monitors, impact and vibration detectors (especially for pronation
and tibia rotation), gyroscopes, and speed and distance monitors
based on navigation systems like GPS. The sensor unit 100 includes
a local wireless transceiver 110 as is known in the art, i.e.,
Bluetooth or some other known or hereafter developed wireless
connection for communication with the input/output unit 200, as
discussed in more detail below. The sensor unit 100 further
includes a storage 106 for storing data during a training session
and optionally a feedback actuator 108 to provide feedback to the
user during the training session. The sensor unit 100 is a small
device that is mounted on the user, i.e. on a leg or foot of a
runner, or on an exercise machine, i.e., on the crank or pedal of a
bicycle, to sense and save data related to a training session of
the user.
[0086] The input/output unit 200, for example a smartphone,
includes a processor 202, a storage device 204, an input device
206, a display 208, a local wireless transceiver 210, and a
feedback actuator 212. The input/output unit 200 communicates with
the sensor unit 100 to receive the data stored during the training
session, or for multiple training sessions. The data can be
downloaded during the training session if the input/output unit 200
is in communication with the sensor unit 100. If the input/output
unit 200 is not carried by the user during the training session,
the download can occur after the training session when the sensor
unit 100 is in communication with the input/output unit 200. For
example, a runner may wish to minimize what is carried during a
training session and may not carry the input/output device 200.
[0087] The feedback actuators 108 and 212 provide some signal such
as a vibration, audible, or optical signal that a value of one or
more parameter is exceeded or falls short. The parameters may
include frequency or step length or foot strike for running, or
torque for cycling. Other parameters may include foot height during
swing time, rotation of foot at stance, pronation of foot during
stance, and tibia rotation. The signal provided by the feedback
actuators 108 and 212 can serve as the warning feedback or alert
discussed above to avoid overload.
[0088] Based on the equation for SUPERCOMP described above, the
processor 202 determines an optimal time for the next training
session and presents the optimal time to a user on the display 208.
The user is presented with the most optimal time, which is the time
of the high point of the SUPERCOMP curve. As an alternative, or
additionally, the user could also be presented with a time frame in
which the curve is in the supercompensation zone. In a preferred
embodiment the supercompensation curve based on the equation for
SUPERCOMP described above is shown in a graphics display, therefore
the user can see how the recovery is predicted to evolve in time,
when will be the time window of the supercompensation phase and at
which time point to best start the next training session, all at a
glance. The remaining time of recovery to the baseline and/or to
the optimal time point can be calculated and shown.
[0089] Although the input/output unit 200 is described as a
smartphone, the input/output unit may be a standalone device
dedicated to the task of determining a timing of the next training
session. Alternatively, the device may be a tablet, computer, or
other device that includes other functions. The remote server 300
includes a data storage 302 and a processor 304. The data storage
302 receives and stores data from a plurality of users. The remote
server 300 has a wide area network connection 306 and the
input/output device 200 includes a wide area network connection
214.
[0090] FIG. 14 is a flow diagram of a method according to an
embodiment of the invention. When the method is started it is first
determined whether the user is new, step 410. If the user is
determined to be new in step 410, user parameters are input to the
input/output unit 200, step 412. The user parameters include, for
example, training status, age and gender of user. The user then
performs the first training session, step 414. During the training
session, the sensor unit 100 records sensor data, step 416. The
user inputs subjective evaluation data, step 418, such as, for
example, incidences of pain during the training session, the levels
of strength and persistence felt by the user during the training
session, and behaviors affecting recovery. Although steps 416 and
418 are shown in parallel, step 418 can be performed after step
416. As a further alternative, only one of steps 416 and 418 might
be used in certain instances.
[0091] The data recorded by the server unit 100 is then evaluated
by the input/output unit 200 with the user parameters and
subjective evaluation data, step 420. The input/output unit 200
then sets personal parameters based on the collected data, step
422, and calculates a recovery-compensation curve using the
personal parameters and displays the calculated
recovery-compensation curve in the manner described above to the
user, step 424.
[0092] If the user is not new in step 410, a next training session
is started, step 510. The input/output unit 200 determines whether
the recovery phase of the previous training session is over in step
512. If the recovery phase is not over, i.e., if the user is still
in the recovery phase, the user is issued a warning against
overtraining on the input/output unit 200, step 513. The user can
then wait until the recovery period is over or start the next
training session. If the recovery phase is determined to be over, a
next training session is started, step 514. Steps 516, 518 and 520
are the same as steps 416, 418 and 420 described above. While
recording the sensor data, a check is performed to determine
whether a load level value is reached, step 526. If it is
determined that the load level is reached, step 528, a warning is
provided to the user by the feedback actuator or the display to
warn against overload, step 530. After step 520, the input/output
unit 200 updates the user parameter based on the data from the
training session, step 522, and calculates and displays the
recovery-compensation curve to the user, step 524.
[0093] According to a further embodiment shown in FIG. 15, the
results of evaluations of training sessions for a plurality of
users are uploaded to a remote server database 600. In this way,
the training sessions of the plurality of users can be evaluated,
step 602. Based on the evaluation, the formulas, formula parameters
and/or correction factors of the formula parameters can be
fine-tuned, for example, for age, gender, step 604. Based on the
fine-tuning, the various individual parameters can be updated, step
606.
[0094] Thus, while there have been shown and described and pointed
out fundamental novel features of the invention as applied to a
preferred embodiment thereof, it will be understood that various
omissions and substitutions and changes in the form and details of
the devices illustrated and described, and in their operation, may
be made by those skilled in the art without departing from the
scope of the invention. For example, it is expressly intended that
all combinations of those elements and/or method steps which
perform substantially the same function in substantially the same
way to achieve the same results are within the scope of the
invention. Moreover, it should be recognized that structures and/or
elements and/or method steps shown and/or described in connection
with any disclosed form or embodiment of the invention may be
incorporated in any other disclosed or described or suggested form
or embodiment as a general matter of design choice. It is the
intention therefore, to be limited only by the scope of the claims
appended hereto.
* * * * *
References