U.S. patent application number 17/393865 was filed with the patent office on 2022-05-19 for system and method for multi-sensor combination for indirect sport assessment and classification.
The applicant listed for this patent is MindMaze Holding SA. Invention is credited to Farzin DADASHI, Beno t MARIANI.
Application Number | 20220155164 17/393865 |
Document ID | / |
Family ID | |
Filed Date | 2022-05-19 |
United States Patent
Application |
20220155164 |
Kind Code |
A1 |
MARIANI; Beno t ; et
al. |
May 19, 2022 |
SYSTEM AND METHOD FOR MULTI-SENSOR COMBINATION FOR INDIRECT SPORT
ASSESSMENT AND CLASSIFICATION
Abstract
A system for measuring power output of a runner is disclosed. In
some embodiments the system comprises a first sensor component
including a first sensor, microprocessor, and a signal transceiver;
a second sensor component including a second sensor and a signal
transmitter; wherein the first sensor is configured to measure a
vertical velocity and horizontal velocity, the second sensor is
configured to measure the slope angle of a foot of the runner
during a stance phase of the foot, the signal transmitter
configured to send slope angle data, the signal transceiver
configured to receive the slope angle data from the signal
transmitter, and the microprocessor has computing instructions
configured to calculate a power output based on the vertical
velocity, horizontal velocity, and slope angle data.
Inventors: |
MARIANI; Beno t; (Lausanne,
CH) ; DADASHI; Farzin; (Lausanne, CH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MindMaze Holding SA |
Lausanne |
|
CH |
|
|
Appl. No.: |
17/393865 |
Filed: |
August 4, 2021 |
Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
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16411156 |
May 14, 2019 |
11105696 |
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17393865 |
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62671204 |
May 14, 2018 |
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International
Class: |
G01L 5/00 20060101
G01L005/00; A43B 3/34 20060101 A43B003/34 |
Claims
1. A method for measuring the power output of a runner, comprising:
sampling, during a contact phase of a foot of the user during an
activity of the user, data from a first sensor configured to
measure data indicating the inclination of the foot; sampling, data
from a second sensor configured to measure data indicating a change
in elevation; buffering the data from the first and second sensors
for a predetermined number of sampling cycles; calculating, during
the activity of the user, a slope trajectory from the buffered
data; and estimating a power based in part on the slope
trajectory.
2. The method of claim 1, wherein the first sensor comprises a
first accelerometer mounted at the foot of the user such that the
first accelerometer measures a vertical acceleration when the foot
is inclined that is lesser than the vertical acceleration when the
foot is not inclined.
3. The method of claim 2, wherein the slope trajectory is
calculated at least in part from a vertical acceleration sampled
from the first accelerometer.
4. The method of claim 2, wherein the first sensor is integrated as
part of a shoe.
5. The method of claim 1, wherein the second sensor comprises a
second accelerometer and the slope trajectory is calculated at
least in part from vertical velocity data sampled from the second
accelerometer and vertical acceleration data sampled from the first
accelerometer.
6. The method of claim 1, wherein the second sensor comprises a
barometer, and the slope trajectory is calculated at least in part
from barometric pressure sampled from the barometer and vertical
acceleration data sampled from the first accelerometer.
7. The method of claim 6, further comprising sampling, from a third
sensor comprising a second accelerometer, vertical velocity data
and the slope trajectory is calculated at least in part from the
vertical velocity sampled from the second accelerometer and
vertical acceleration data sampled from the first
accelerometer.
8. The method of claim 2, wherein a slope trajectory greater than 0
degrees is calculated where each vertical acceleration sampled from
the first accelerometer over a predetermined number of gait cycles
indicates the foot is not inclined.
9. The method of claim 2, wherein a slope trajectory less than a
predetermined slope trajectory threshold is calculated where one or
more vertical accelerations sampled from the first accelerometer
over a predetermined number of gait cycles indicates the foot is
inclined more than the predetermined slope trajectory
threshold.
10. The method of claim 9, wherein the predetermined slope
trajectory threshold is 2 degrees.
11. A system for measuring the power output of a runner,
comprising: a first sensor configured to measure data indicating
the inclination of the foot; a second sensor configured to measure
data indicating a change in elevation; and a computing device
having a processor and a memory for storing buffered data from the
first and second sensors for a predetermined number of sampling
cycles and having stored thereon instructions for execution by a
processor to cause the computational device to receive data sampled
from the first sensor and from the second sensor, to buffer the
data from the first and second sensors for a predetermined number
of sampling cycles, to calculate, during an activity of the user, a
slope trajectory from the buffered data, and to estimate a power
based in part on the slope trajectory.
12. The system of claim 11, wherein the first sensor comprises a
first accelerometer mounted at the foot of the user such that the
first accelerometer measures a vertical acceleration when the foot
is inclined that is lesser than the vertical acceleration when the
foot is not inclined.
13. The system of claim 12, wherein the instructions are further
configured to cause the computational device to calculate the slope
trajectory at least in part from a vertical acceleration sampled
from the first accelerometer.
14. The system of claim 12, wherein the first sensor is integrated
as part of a shoe.
15. The system of claim 11, wherein the second sensor comprises a
second accelerometer and the instructions are further configured to
cause the computational device to calculate the slope trajectory at
least in part from vertical velocity data sampled from the second
accelerometer and vertical acceleration data sampled from the first
accelerometer.
16. The system of claim 11, wherein the second sensor comprises a
barometer, and the instructions are configured to cause the
computational device to calculate the slope trajectory at least in
part from barometric pressure sampled from the barometer and
vertical acceleration data sampled from the first
accelerometer.
17. The system of claim 16, comprising a second accelerometer and
wherein the instructions are further configured to cause the
computational device to sample, from the second accelerometer,
vertical velocity data and to calculate the slope trajectory at
least in part from the vertical velocity sampled from the second
accelerometer and vertical acceleration data sampled from the first
accelerometer.
18. The system of claim 12, wherein the instructions are further
configured to cause the computational device to calculate a slope
trajectory greater than 0 degrees where each vertical acceleration
sampled from the first accelerometer over a predetermined number of
gait cycles indicates the foot is not inclined.
19. The system of claim 12, wherein the instructions are further
configured to cause the computational device to calculate a slope
trajectory less than a predetermined slope trajectory threshold
where one or more vertical accelerations sampled from the first
accelerometer over a predetermined number of gait cycles indicates
the foot is inclined more than the predetermined slope trajectory
threshold.
20. The system of claim 19, wherein the predetermined slope
trajectory threshold is 2 degrees.
Description
FIELD OF THE DISCLOSURE
[0001] The present invention relates to systems and methods for
measuring and communicating power and efficiency metrics during
bipedal motion, including running, using sensor technology and
feedback mechanisms.
BACKGROUND
[0002] With 65 million annual joggers and runners in the US (more
than 20% of total population), running is the most practiced sport
worldwide. Despite the recent adoption of GPS watches and
heart-rate monitors, most runners do not get proper feedback to
manage their runs, thus leading to drop-outs due to injuries,
frustration, fatigue, or overreaching.
[0003] Power output measurement allows the monitoring of changes in
performance and, thus, enhancement of performance. In cycling for
example, power output (PO) has completely revolutionized training.
With this new metric, the cyclist can train at the appropriate
intensity zones and monitor performance changes and progress,
taking into account the environment, slope, and speed. However,
systems and methods that estimate PO for cycling can directly
measure PO because of a bicycle's stable framework on which to
mount sensors that directly measure force (e.g., crank arm, pedals)
and angular velocity (e.g., spindle, freehub, sprocket). This type
of direct measurement is not possible in activities involving other
activities like running, walking, skiing and the like. These other
activities lack a stable framework and involve irregular movements,
independent movement of sections of the body and, to some extent,
unpredictable movements.
[0004] Nevertheless, monitoring performance is important in running
and other non-cycling endurance activities. It is known that in
endurance activities, both for recreational or elite athlete, a
large part of the training volume/duration should be in the
moderate intensity domain (i.e., below the first lactic or
ventilatory threshold). This "polarized training" (Munoz et al.,
"Does polarized training improve performance in recreational
runners?" Int'l J. Sports Physiol. Perform., 2014) leads to larger
aerobic capacity enhancement (Stoggl & Sperlich, "Polarized
training has greater impact on key endurance variables than
threshold, high intensity, or high volume training," Front
Physiol., 2014) and reduces the risks of overreaching or
overtraining (Seiler, Haugen & Kuffel, "Autonomic recovery
after exercise in trained athletes: intensity and duration
effects," Med. Sci. Sports Exerc., 2007) and subsequently of
injuries (Kibler, Chandler & Stracener, "Musculoskeletal
adaptations and injuries due to overtraining," Exerc. Sport Sci.
Rev., 1992) and illnesses (Gabriel et al., "Overtraining and immune
system: a prospective longitudinal study in endurance athletes,"
Med. Sci. Sports Exerc., 1998).
[0005] In running, the mechanical work notion dates back to the
early work of Fenn ("Frictional and kinetic factors in the work of
sprint running," Am. J. Physiol. 92, 1930) where the author
attempted to explain the metabolic cost of running by quantifying
the mechanical work. (Cavagna et al., "Mechanical Work in Running,"
J. Appl. Physiol., 1964) later extended the calculation of Fenn
(1930b) (Fenn, "Work against gravity and work due to velocity
changes in running," Am. J. Physiol. 93, 1930) to a larger range of
running speeds. In level running, different methods have been
proposed to measure the total amount of work produced by the body
and to derive the power. However, a clear and universally valid
approach has not yet been established (Arampatzis et al.,
"Mechanical power in running: a comparison of different
approaches,"J. Biomed., 2000).
[0006] Two distinct type of work have been identified: the external
work which sustains the motion of the center of mass (CoM) of the
body relative to the surrounding and the internal work which
sustains the motion of the limbs relative to the CoM. The total
work is defined as the absolute values of the external and internal
work (Cavagna & Kaneko, "Mechanical work and efficiency in
level walking and running," J. Physiol., 1977).
[0007] The instrumentation used to estimate power is mainly based
on force plate and camera-based motion tracking system to estimate
ground reaction force and body kinematics and CoM speed. When
compared at similar speeds, existing approaches resulted in
different findings (Williams & Cavanagh, "A model for the
calculation of mechanical power during distance running," J
Biomech., 1983; Arampatzis et al., 2000).
[0008] Some studies also have investigated the relationship between
power and metabolic energy (Williams & Cavanagh, "Relationship
between distance running mechanics, running economy, and
performance," J. Appl. Physio., 1987; Shorten et al., "Mechanical
Energy Changes and the Oxygen Cost of Running," Engineering in
Medicine, 1981; Luhtanen et al., "Mechanical work and efficiency in
treadmill running at aerobic and anaerobic thresholds," Acta
Physiologica Scandinavica, 1990).
[0009] Fukunaga et al. ("Effect of running velocity on external
mechanical power output," Ergonomics, 1980) showed that mechanical
power increases as the running speed increases. Although the
approaches to measure the mechanical work seemed to relate to the
running speed and therefore, also on the net VO.sub.2 changes,
Cavagna et al. (1964) observed that produced efficiency ratio
(mechanical power produced divided by the metabolic energy
consumed) far exceeded the maximal efficiency of muscles. A
spring-mass model (where the leg acts like a linear spring) was
proposed to explain this difference (Cavagna et al., "The sources
of external work in level walking and running," J Physiol. 1976;
McMahon et al., "Groucho running," J. Appl. Physio., 1987;
Blickhan, "The spring-mass model for running and hopping," J.
Biomech., 1989). The stiffness of the spring was shown to be
relatively constant over a wide range of speeds (He et al.,
"Mechanics of running under simulated low gravity," J. Appl.
Physiol., 1991; Farley et al., "Running springs: speed and animal
size," J Exp. Biol. 185, 1993) and provide a better modelling of
speed and CoM vertical displacement which is of interest for power
estimation. The measurement of leg and vertical stiffness was
mainly done using force plate and/or video motion analysis
(Brughelli & Cronin, "Preventing Hamstring Injuries in Sports,"
Strength & Conditioningl, 2008). Considering that the equipment
required for this type of analysis is costly and not practical for
in-field measurements, Morin et al. ("A Simple Method for Measuring
Stiffness during Running," J Appl. Biomech., 2005) proposed and
validated a simple calculation method for assessing leg and
vertical stiffness based on anatomical data and a few mechanical
spatio-temporal parameters (e.g., contact and flight times, forward
speed). This model was shown to be more robust when considering the
different types of foot-strike patterns.
[0010] Udofa et al. ("A general relationship links gait mechanics
and running ground reaction forces," J. Exp. Biol., 2016) also
evaluated the ability of an anatomically-based, two-mass model of
the human body to predict vertical impact and peak forces during
running from spatio-temporal parameters, concluding with the
suitability of wearable sensor to predict vertical force.
[0011] Slope running is rarely studied. In one study, Dewolf et al.
("The rebound of the body during uphill and downhill running at
different speeds," J. Exp. Biol., 2016) proposed a modification of
the spring-mass model, including an actuator in parallel with the
spring that produces (uphill) or absorbs (downhill) energy
according to the slope. The spring-mass model commonly used in
running mechanics allows modelling the vertical and leg stiffness
which are two important components in controlling running speed and
ground reaction force and producing power. Stiffness was mainly
investigated using 3D force plates.
[0012] The inventors have found that stiffness parameters are
affected by fatigue through investigation using foot worn IMUs in
real marathon race conditions. Significant differences (p<0.05)
can be found on the vertical stiffness during the first half of the
race as force decreases followed by stable values until the end of
the race with no significant differences in leg stiffness can be
found. The fast degradation of the vertical stiffness during the
first half of the race can be explained by a decrease of peak
vertical reaction force. It suggests that fatigue evolves quickly
and attains a plateau at the middle of the marathon. The results
allow a better understanding of fatigue mechanism during running as
their fluctuation allowing to better guide the athlete to keep
power efficient running.
[0013] Inertial Measurement Units (IMUs) consisting of 3D
gyroscopes and 3D accelerometers, have been used in different
analyses of running. In some studies, spatio-temporal parameters
have been extracted from IMU signals obtained in different body
locations. Some studies have used acceleration and/or angular
velocity of the shank/tibia to detect temporal events and
investigate peak tibial acceleration after impact (Crowell et al.,
"Reducing impact loading during running with the use of real-time
visual feedback," J. Orthop. Sports. Phys. Ther., 2010; Mercer et
al., "Characteristics of shock attenuation during fatigued
running," J. Sports Sci., 2003). Other studies have used IMU on the
trunk, the lower back, the head for spatio-temporal analysis of
running, or a combination of those locations (Norris, Anderson
& Kenny, "Method analysis of accelerometers and gyroscopes in
running gait: A systematic review," J. Sports Eng. & Tech.,
2014; Bergamini et al., "Sprint running temporal parameters with
IMU," 2012). Other studies have used foot-worn IMUs to estimate
temporal parameters (Strohrmann et al., "Out of the lab and into
the woods: kinematic analysis in running using wearable sensors,"
UbiComp, 2011; Chapman et al., "Identification of Cross-Country
Skiing Movement Patterns Using Micro-Sensors," 2012; Brahms, "The
assessment of fatigue-related changes in stride mechanics,
variability and long-range correlations in recreational and elite
distance runners using foot-mounted inertial sensors," 2017;
Reenalda et al., "Continuous three dimensional analysis of running
mechanics during a marathon by means of inertial magnetic
measurement units to objectify changes in running mechanics," J.
Biomech., 2016) and estimate the running speed (de Ruiter et al.,
"Running Speed Can Be Predicted from Foot Contact Time during
Outdoor over Ground Running," PLoS One, 2016).
[0014] Recently, Reenalda et al. (2016) have shown that IMUs can be
used to perform a continuous 3D kinematic analysis of running
technique during a marathon to objectify changes in running
mechanics. Few studies have investigated the effect of feedback on
the running technique. Messier et al. ("Effects of a verbal and
visual feedback system on running technique, perceived exertion and
running economy in female novice runners," J. Sports Sci., 1989)
showed the benefit of 5 weeks training with visual and verbal
feedback on running technique. Later, Crowell et al. (2010) used
real-time visual feedback to reduce impact load, measured using
accelerometers fixed on tibias. Finally, Eriksson et al.
("Immediate effect of visual and auditory feedback to control the
running mechanics of well-trained athletes," J. Sports Sci., 2011)
showed the usefulness of giving visual and auditory feedback to
elite athletes to reduce step frequency and CoM vertical
displacement.
[0015] Others in the field have not developed the high-performance
biomechanics signal processing techniques required to extract
running power from various inertial sensor locations. There are
currently 3 types of existing systems that measure power output
from biomechanical signals. However, none of them sufficiently
measure power output or satisfy customer requirements. Current
devices include GPS watches, heart-rate monitors, and
consumer-grade power meter devices. GPS and heart-rate-based
devices are technologically limited to assess power in real-time
and thus not usable for proper running pace management as soon as
there are slopes during a run. In particular, GPS devices are
unable to assess power on slopes in real-time. Heart-rate-based
devices are unable to assess power in real-time during slope
transitions where previous conditions still affect the heart rate
and the body has not adjusted to current conditions. Some
conditions that affect heart rate include the altitude, weather,
dehydration, and the like. Current consumer-grade power meter
devices similarly cannot effectively measure power because of the
latency between terrain changes (e.g., slope) and heart rate or
other biometric data. When a runner moves from flat terrain to an
uphill slope, the heart takes some time to adapt its frequency to
the new condition. Thus, for current devices, power output
measurements are accurate only to the extent they are used on
terrain with few or only gradual slope changes.
[0016] Measuring power output during running or other like activity
that involves some irregular movement (e.g., skiing) can be done
only indirectly. That is, current devices can measure
spatio-temporal parameters, force at specific places and times,
acceleration of different portions of the body, and the like and
then piece together an estimate of power from the measurements. As
a result, current devices are inaccurate despite their complexity.
Indeed, current systems incorporate overly complex designs to
overcome inaccuracy. They include sensor systems that group sensors
in a single housing to be mounted on the clothing or another device
of a runner. These prior systems use combinations of IMUs,
accelerometers, gyroscopes, and pressure and temperature sensors in
concert within the device to derive power estimates. However, these
systems suffer from the complexity required to reconcile all of the
different sensor measurements from the device which may give
different results across different sessions for various reasons,
including inconsistent placement of the device on the user.
Additionally, these devices suffer from inaccurate readings because
of the limited types of measurements they can take. Indeed,
runners' experience and providers of prior devices and methods
suggests that the results are unreliable.
[0017] Current devices rely on sensor assemblies that transmit
measurement data to some other device like a smartphone or fitness
watch for processing. Sensor assemblies are separated from the
processing and display device in part to save battery life of
sensor assemblies and other devices. Separating devices is
necessary to conserve battery power but contributes to the
inaccuracy of results.
[0018] One such prior device, Stryd described in U.S. Publ. No.
20170189752A1, is a foot-worn device which claims to measure power
and to synchronize with some of the latest generation of
smartwatches. However, Stryd's accuracy and precision are
questionable in spite of its complex grouping of sensors to gather
a multitude of metrics. Further, it is doubtful that a single
sensing position is sufficient to estimate accurate running power
output. Additionally, the Stryd, along with other similar devices,
despite their already high expense, require pairing with other
expensive devices such as smartphones and smart watches to provide
feedback from the user.
[0019] Power output estimation for skiing similarly has been
attempted without much success or accuracy. For example, estimating
methods have used analysis of film of skiers for rough comparisons
(Norman & Komi, "Mechanical Energetics of World Class
Cross-Country Skiing," Intl J Sports Biomech Vol. 3, 1987) and
later estimations of just upper body power output.
[0020] Current devices and systems measure slope, which is used to
estimate power, using barometric pressure changes or vertical
velocity or acceleration of the runner. Such measurements can be
inaccurate or lag such that on varied terrain, power estimates can
be significantly unreliable.
[0021] Given the above shortcomings of the current art, a need
still exists for a power meter device that can provide runners and
other athletes the accuracy of power output measuring that cyclists
enjoy. In particular, a need still exists to provide proper and
accurate power estimations to runners to manage their runs and
avoid drop-outs because of injuries, frustration, fatigue, or
overreaching through advanced signal processing and machine
learning methods to estimate the external mechanical power of the
runners the kinematic signals from more effective use of sensors
and sensor placement. Furthermore, a need exists for a device that
effectively estimates PO regardless of terrain, whether even or
uneven, road or trail, and the like to assist athletes improve
daily practice and performance regardless of fitness and skill
level, whether recreational or elite.
SUMMARY OF SOME OF THE EMBODIMENTS
[0022] Preferred embodiments assess spatio-temporal parameters
(alternatively, spatio-temporal parameters may be referred to as
temporal parameters herein) of the running gait (alternatively,
spatio-temporal parameters may be referred to as temporal or
spatio-temporal phases herein), using foot-worn inertial
measurement units (IMUs). Preferred embodiments additionally
include a plug-and-play method to remove the influence of the
sensor's initial orientation on the system's accuracy and
precision. Spatio-temporal parameters measured include ground
contact time, flight phase duration, swing phase duration, and
cadence. Methods for accurately estimating power output measure, at
each step, both initial contact and terminal contact events.
Spatial parameters related to the 3D orientation of the foot,
namely foot pronation angle and foot strike angle are also measured
in preferred embodiments. Preferred embodiments also apply machine
learning techniques to estimate the essentially instantaneous
running speed, in real-time, using some of the aforementioned
temporal parameters as input of the model.
[0023] Preferred embodiments of the present invention include
advanced signal processing and machine learning methods to estimate
the external mechanical power provided by a runner and different
sensors, including inertial measurement unit (IMU) sensors, and
sensor placements. Unique combinations of kinematic signals from
the sensors provide relevant and accurate power output
monitoring.
[0024] Preferred embodiments include an apparatus for measuring
power output of a runner having only one sensor position (either
foot, wrist or head for instance) on a runner and measuring power
output according to slope by detecting slope changes and
retroactively applying slope measurement to spatio-temporal data.
In preferred single-sensor position embodiments, no other devices
are required for measuring power output.
[0025] Some preferred embodiments include a system for measuring
power output of a runner having a plurality of IMU mounted on the
body of the runner resulting in accurate power measurement
estimates.
[0026] Some preferred embodiments include foot-worn IMUs and signal
processing algorithms to assess spatio-temporal parameters of the
running gait measured at the foot using the IMUs. Additionally,
such embodiments incorporate an initialization method to remove the
influence of the sensor's initial orientation on the system's
accuracy and precision and estimate temporal parameters, such as:
ground contact time, flight phase duration, swing phase duration,
and cadence. An initialization method detects initial contact and
terminal contact of the foot with the ground. Initialization
measurements also can be taken of the 3D orientation of the foot,
namely foot pronation angle and foot strike angle. Foot orientation
measurements can also be taken periodically as power is estimated
and not just initially. Finally, we used a machine learning
approach to estimate the instantaneous running speed, using some of
the aforementioned temporal parameters as input of the model.
[0027] Some preferred embodiments can measure leg stiffness
parameters using foot worn IMUs for analyzing the effect of fatigue
on the stiffness parameters. For example, GPS speed, contact time
and flight time can be measured over the course of a session.
Vertical and leg stiffness can be calculated using Morin's
equations (Morin 2005). Leg stiffness fluctuations during a session
can be measured and combined with power output estimations for more
robust feedback to a runner to maintain power efficient
running.
[0028] Some preferred embodiments combine a minimal number of
sensors to measure velocity and incline to calculate a power output
estimation. In particular, incline can be measured by detecting the
angle of the foot or shoe during a stance phase (also referred to
as contact phase) of the runner's gait.
[0029] Other preferred embodiments combine a plurality of sensors
mounted on the body, clothing, or wearables of the runner to
measure velocity, incline/decline, and force. Various signal
processing algorithms, machine learning techniques, or some
combination thereof can be applied to sensor measurements and other
measurements (e.g., initialization measurements) to estimate power
output and provide runner feedback for maintaining efficiency or
attaining higher efficiency.
[0030] Various embodiments of the methods, systems and apparatuses
of the present disclosure can be implemented by hardware, software
or a combination thereof. For example, as hardware, selected steps
of a methodology according to some embodiments can be implemented
as a chip and/or a circuit. As software, selected steps of a
methodology according to some embodiments of the disclosure can be
implemented as a plurality of software instructions being executed
by a computer (e.g., using a suitable operating system).
Accordingly, in some embodiments, selected method steps, systems
and/or apparatuses of the present disclosure can be performed by or
implemented in a processor (e.g., executing an application and/or a
plurality of instructions).
[0031] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this disclosure belongs. The
materials, methods, and examples provided herein are illustrative
only and not intended to be limiting.
[0032] Various embodiments of the methods, systems and apparatuses
of the present disclosure can be implemented by hardware and/or by
software or a combination thereof. For example, as hardware,
selected steps of methodology according to some embodiments can be
implemented as a chip and/or a circuit. As software, selected steps
of the methodology (e.g., according to some embodiments of the
disclosure) can be implemented as a plurality of software
instructions being executed by a computer (e.g., using any suitable
operating system). Accordingly, in some embodiments, selected steps
of methods, systems and/or apparatuses of the present disclosure
can be performed by a processor (e.g., executing an application
and/or a plurality of instructions).
[0033] Although embodiments of the present disclosure are described
with regard to a "computer," "computing device," and/or with
respect to a "computer network," it should be noted that optionally
any device featuring a processor and the ability to execute one or
more instructions is within the scope of the disclosure, such as
may be referred to herein as simply a computer or a computational
device and which includes (but not limited to) any type of personal
computer (PC), a server, a cellular telephone, an IP telephone, a
smartphone or other type of mobile computational device, a PDA
(personal digital assistant), a thin client, a smartwatch, head
mounted display or other wearable that is able to communicate wired
or wirelessly with a local or remote device. To this end, any two
or more of such devices in communication with each other may
comprise a "computer network."
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Embodiments of the disclosure are herein described, by way
of example only, with reference to the accompanying drawings. With
specific reference now to the drawings in detail, it is stressed
that particulars shown are by way of example and for purposes of
illustrative discussion of the various embodiments of the present
disclosure only and are presented in order to provide what is
believed to be a useful and readily understood description of the
principles and conceptual aspects of the various embodiments of
inventions disclosed therein.
[0035] FIG. 1 illustrates a schematic of a sensor system according
to preferred embodiments.
[0036] FIG. 2 illustrates a non-limiting, exemplary method for
measuring and displaying power output metrics and feedback in
accordance with preferred embodiments.
[0037] FIG. 3A illustrates a flowchart of a non-limiting, exemplary
method for estimating power output for running in accordance with
preferred embodiments.
[0038] FIG. 3B illustrates a flowchart of a non-limiting, exemplary
method for calibrating a sensor or sensor assembly to measure power
output for running in accordance with preferred embodiments.
[0039] FIG. 3C illustrates a flowchart of a non-limiting, exemplary
method for detecting and determining slop for power output
measurement for running in accordance with preferred
embodiments.
[0040] FIG. 4 illustrates a schematic of a non-limiting, exemplary
configuration of sensors on the body of a runner in accordance with
preferred embodiments.
[0041] FIG. 5 illustrates a flowchart of a non-limiting, exemplary
method for estimating and displaying feedback for power output for
running in accordance with preferred embodiments.
[0042] FIG. 6 illustrates a schematic of a non-limiting, exemplary
configuration of sensors on the body of a runner in accordance with
preferred embodiments.
[0043] FIG. 7 illustrates a flowchart of a non-limiting, exemplary
method for estimating and displaying feedback for power output for
running in accordance with preferred embodiments.
[0044] FIG. 8 illustrates a chart of slope changes and accuracy of
power estimation among different classes of power estimation
devices including preferred embodiments that include foot
orientation measurements.
DETAILED DESCRIPTION OF SOME OF THE EMBODIMENTS
[0045] Preferred embodiments include a research-grade power meter
software library customized for running, to be embedded in wearable
devices of various kinds (earphones, smartwatches, smartshoes,
smartphones, etc.). Its innovation comes from its scientific
approach and validity, and the use of multiple sensors at different
body locations to derive the most accurate power estimation of
running. Preferred embodiments can also include be used with
existing sensor systems or assemblies or more simplified sensor
systems or assemblies.
[0046] Preferred embodiments can estimate running power in every
condition, such as indoor and outdoor running, on road, hill,
trails, etc. using a complete data collection protocol covering the
various situations and providing sufficient data as input to power
output estimation models. Some preferred embodiments use quality
reference systems and meta-data about the user such as age and
gender to properly estimate physiological responses.
[0047] Additionally, preferred embodiments include real-time
on-board processing to provide useful feedback during running and
create value propositions for consumer wearables. Sensor systems in
accordance with preferred embodiments can estimate power output in
real-time with a minimal latency. In addition, preferred
embodiments include on-board, real-time feature processing at the
sensor level and minimal buffers and processing to limit battery
leakage.
[0048] Preferred embodiments model and validate the instantaneous
power measured using inertial sensors. The signal processing in
preferred embodiments is performed in real-time and the ground
reaction forces acting on the runner's body is modelled using the
acceleration and angular velocity obtained from body-worn sensors.
Moreover, spatio-temporal parameters are used for real-time
estimation of mechanical power, but also to give direct feedback
with parameters that could be modified by the athlete (e.g.,
changing cadence, stride length, etc.).
[0049] Referring now to the figures, FIG. 1 illustrates a schematic
of a non-limiting, exemplary system 100 of sensor assemblies 105,
140, 160 for measuring the power output of a runner. The sensor
system can include a main sensor assembly 105 that includes a
computing device 110, a sensor array 125, and a power data storage
device 130. In other embodiments a computing device can be remote
and not located in a sensor assembly device. Sensor assembly 105
preferably has a small form factor and is configured as part of a
wearable or to attach to a wearable. Sensor array 125 can include
one or more sensors that measure velocity, acceleration, and the
like. In a preferred embodiment, sensor array 125 includes one IMU
sensor for measuring the vertical velocity and one IMU for
measuring horizontal velocity. Other sensors that measure velocity
can be used and sensors that measure other aspects of movement can
be included in sensor array. For example, sensor array 125 can
include a gyroscope or other type of accelerometer.
[0050] Computing device 110 includes machine learning engine and
feedback engine. Machine learning engine can include an embedded
machine learning library. The machine learning library can be
implemented according to different AI techniques known in the art.
For example, some non-limiting suitable techniques include LSTM
networks; various types of RNN (recurrent neural network) such as a
Siamese RNN; a CNN (convoluted neural network); and an MNN (modular
neural network).
[0051] Feedback engine 120 can include machine instructions for
creating feedback messages to the runner based on the computed
power output from machine learning engine or changes in the rate of
power output as determined in the machine learning engine.
[0052] Power data storage device 130 can store raw velocity, slope,
and other measurements from sensor arrays as well as power
computations from machine learning engine 115. Computing device 110
can be implemented using standard micro-controller processors such
as the family of ARM M4 Cortex architectures.
[0053] Sensor assemblies 140, 160 include sensor arrays 145, 165.
Sensor 145, 165 arrays can each be adapted to placement on
different body parts of the runner. For example, a sensor array can
include only IMUs to measure velocity, can include only a force
sensor, accelerometer, or barometric pressure sensor to measure
gait phase from a location on or near the runner's foot. Each of
sensor assemblies include a wireless transceiver to send
measurement data signals to sensor assembly. Sensor assembly
includes a wireless transceiver to receive the measurement data
signals from sensor assemblies for processing by machine learning
engine 115.
[0054] Other preferred embodiments include machine instructions
without a machine learning engine 115 for computing power output
directly from measurements data signals received from sensor
arrays.
[0055] FIG. 2 illustrates a preferred method 200 for measuring and
displaying power output metrics and feedback in accordance with
embodiments. At step 202, an inertial measurement is received from
a first sensor. At step 204, an angular velocity is received from
the first sensor. At step 206, an inertial measurement is received
from a second sensor. At step 208, an angular velocity is received
from the second sensor. At step 210, a total power is calculated.
At step 212, a feedback indicator is determined. A feedback
indicator can comprise a number representing the total power, some
graphical element (e.g., graph, geometric representation, color
coding, and the like) that indicates the total power, audio
feedback, or some combination thereof. In some instances, feedback
can be relative based on the user's history or relative to a
database of total power for multiple users or (how are they
determined) (what determines them) (when are they determined/do
they ever change) At step 214, the feedback indicator is displayed.
(How) (where) (anything else displayed as a result) (how many) (any
special characteristics?)
[0056] FIG. 3A illustrates a preferred method 300 for estimating
slope and slope changes. At step 302, sensors are calibrated for
slope. Sensor calibration is discussed further in connection with
FIG. 3B. The user may or may not be on a flat surface but during
calibration the slope of that surface will serve as the baseline
slope for the calibration process. The calibration process is
preferably performed a plurality of times to more accurately
determine a baseline slope. At step 304, the current slope is
estimated. In preferred embodiments, barometric pressure and foot
orientation are measured to determine slope. Estimation of slope is
discussed further in connection FIG. 3C. At step 306, a check is
made whether the user is on stairs. In some preferred embodiments,
this is done by comparing the slope estimation and the orientation
of the foot. For example, if the slope estimation is within a stair
slope range and the foot orientation is within a range that would
indicate a flat surface, then a determination is made that the
terrain is stairs. Preferably, the stair slope range is between
.+-.20 and 60 degrees and more preferably between .+-.30 and 50
degrees. To increase the accuracy of the determination, some
preferred embodiments will buffer the sensor data used to determine
slope and the determination of stair terrain will be based on an
essentially constant or smoothly changing slope estimation. That
is, where the sensor data indicates an essentially constant change
in elevation over the course of several gait cycles a determination
can be made that the terrain is stairs but if the slope estimation
varies beyond a threshold over the course of several gait cycles, a
determination is not made that the terrain is stairs. The number of
gait cycles buffered is more than one but, in some preferred
embodiments can be adjusted. Preferably, the variance threshold is
3 degrees and, more preferably, can be adjusted. The slope
estimation itself is determined as discussed further herein.
Preferably, the flat surface range for stairs is between 0 and -12
degrees. In some preferable embodiments, the flat surface range for
stairs can be adjusted for the user based on the user's kinematics.
This determination can be important as the slope of the foot during
a stance phase will indicate the that user is on a flat surface but
is actually on a sloped plane.
[0057] If the user is on stairs, then at step 308, the slope data
is adjusted for stair terrain. Additionally, in some embodiments,
heel strike, heel off, and toe strike data are removed from the
sensor data. Heal strike, heal off, and toe strike data are not as
relevant when the user is on stairs because of how gait is adjusted
to accommodate the flat surface of a stair. If those temporal
phases are included, then gait analysis and timing could be
inaccurate because those phases do not exist on stair terrain in
the same way as for non-stair terrain. If the user is not on
stairs, then at step 310, the slope data is adjusted for non-stair
terrain. At step 312, the stride length and velocity are calculated
based on the slope trajectory or slope estimation for later input
into a power output estimation. Inertial data typically indicates
gait phases inaccurately. Consequently, in some preferred
embodiments, gait phase data (e.g., stride length) and velocity
data are adjusted by calculating the interrupt of inertial data
through the slope trajectory. At step 314, abrupt slope change
determination data is buffered. Slope change data can be derived
from pressure sensor data changes (e.g., barometric pressure),
topographical data, infrared time-of-flight sensor data, and the
like, as well as foot orientation. The data can indicate a slope
change when there is only a temporary change in the terrain and not
necessarily from an actual slope change. For example, a user can be
running over a protruding rock, log, or bump which causes the foot
to be inclined at a greater angle than the overall current terrain
would suggest. Additionally, because the runner can be elevated
according to the terrain protrusion, barometric data can also
suggest a rising slope. However, over just a few cycles the data
will revert back to indicating a lack of change in the overall
slope or only slight changes in the slope. Therefore, in preferred
embodiments, abrupt slope changes are buffered for a number of
cycles for comparison with slope data from later cycles. An abrupt
slope change can be a change in slope that results in a change to
the power output but a change in heart rate or other biosignals
lags. In some embodiments, a parameter indicating whether a
particular slope change is abrupt can be user-defined. If slope
data from later cycles confirms the new slope, then an abrupt
change can be determined and be used as an input to the power
output estimation. Buffered abrupt slope change determination data
is maintained preferably for 3-5 cycles. It should be understood
that the embodiments other than a whole-body kinetics embodiment
can use methods similar to the method exemplified in FIG. 3A.
[0058] Buffering abrupt slope change data is important to achieve
superior power estimation results. As discussed herein, biometric
data that would indicate slope change and, thus, increased power
output, typically lags actual slope change. Additionally, sensor
data typically used to determine slope changes (e.g., barometer,
topographic information, etc.) may present inaccuracy or similarly
lag. To increase accuracy in determining slope changes and
particularly, abrupt slope changes, foot orientation is included in
the determination. Foot orientation angles change immediately upon
a slope change. However, foot orientation can present a false
positive when the user's foot is on a temporary protrusion or a
false negative when the user is on stairs. To account for the false
positives and negatives, preferred embodiments will sample both
traditional types of sensor data and foot orientation and buffer
them both over a number of cycles as discussed herein so that
proper and more accurate power output estimates can be calculated
in the event the user remains on a similar slope and encountered a
protrusion (e.g., pressure change rate is steady but foot
orientation indicates abrupt change--false positive), is on stairs
(e.g., pressure change rate increases but foot orientation
indicates essentially flat terrain--false negative), or has
encountered an abrupt slope change (e.g., both pressure change rate
increases and foot orientation indicates abrupt change).
[0059] Thus, preferred embodiments estimate power while accounting
for abrupt slope changes without the lag inherent in previous power
estimation devices and systems. This is important for assisting
users to maintain a proper pace during the slope changes to
maximize power efficiency, as illustrated in FIG. 8. In current
devices, the best results are derived when changes in elevation are
detected, but during and shortly after abrupt changes, event the
most accurate current devices lag in their ability to estimate
power (see the second and third row of FIG. 8).
[0060] FIG. 3B illustrates a preferred method 320 for calibrating
sensors for slope calculation. At step 322, an activation signal is
received by the sensor assembly. Each sensor assembly worn by the
user receives the activation signal. At step 324, sampling
synchronization is initialized. Different types of sensors will
sample data at different frequencies. For example, a barometer is
typically sampled four times for every accelerometer sampling.
Therefore, in preferred embodiments, the sample rates are
synchronized so that data is collected from the sensors at
essentially the same time. At step 326, the slope during a static
period is calculated. A static period is one in which the user is
stationary or near stationary. Here, slope can be calculated using
foot orientation. A baseline barometric pressure can also be taken
for comparison with later changes in pressure to help determine
slope. At step 328, a temporal phase of the movement is detected.
In a preferred embodiment, precision is not required at this step.
That is, some preferred embodiments require only a rough
segmentation of the temporal phases (e.g., is the user in a contact
phase or not). At step 330, it is determined whether the user is on
a flat surface and stopped. At step 332 the slope is recalculated
from new slope data obtained and a mean of the slope data from the
previous cycles. At step 334, the foot orientation is calculated.
At step 336, the slope based on the current cycle calculations is
adjusted. If a baseline slope is determined so that an offset angle
of the current slope from the 0.degree. plane is known within a
margin of error, then the calibration cycle is not repeated. In
preferred embodiments, steps 328-334 of the calibration cycle are
repeated at least a predetermined number of times and until a
sufficiently accurate baseline slope is determined. It should be
understood that the embodiments other than a whole-body kinetics
embodiment can use methods similar to the method exemplified in
FIG. 3B.
[0061] FIG. 3C illustrates an exemplary method 350 for measuring
slope changes for input into a power output estimation calculation
in accordance with preferred embodiments. At step 352, acceleration
data is received from one or more sensors. In preferred
embodiments, acceleration data includes both horizontal and
vertical acceleration data and includes acceleration from gravity.
Velocity data is also collected. Acceleration and velocity data are
important to collect because at higher speeds, barometric pressure
measurements will less reliable. At step 354, barometric pressure
measured by one or more barometers is received. At step 356, a
slope error is calculated from the barometric pressure change from
the previous cycle. At step 358, it is determined whether the
current terrain is flat based on foot angle during a stance phase
and a change in the barometric pressure. In some preferred
embodiments, foot inclination (also referred to as foot
orientation) can be determined from acceleration data from an
accelerometer mounted at the foot. In some instances, the
accelerometer can be integrated as part of a shoe or other
footwear, clothing or gear at the foot in a location of the foot
that experiences an inclination when the foot is in a contact phase
on sloped terrain. Thus, when the foot is inclined or declined
during a portion of the contact phase, the accelerometer will
generate data showing, for example, a lesser vertical acceleration
relative to the accelerometer and a greater horizontal acceleration
relative of the accelerometer. If the terrain is considered flat,
then at step 360 the current terrain is classified as flat and no
change to slope metrics is made. If the terrain is not considered
flat, then at step 362, it is determined whether the slope angle of
the current terrain is greater than 0.degree.. If it is, then at
step 364, it is determined whether the change in slope is greater
than a slope change threshold. A slope change threshold is
preferably 2 degrees. More preferably, a slope change threshold can
be at a user-defined predetermined level in some embodiments. In
other embodiments, slope change thresholds can be based on the
measurements taken from slope measurements and compared against
biosignal(s) where the slope measurements indicate a change but the
biosignal(s) do not. If the slope change is beyond the slope change
threshold, then at step 366, the terrain is classified as "up
stair" and a slope counter is incremented. In a preferred
embodiment, the slope counter is incremented by a predetermined
amount. If the slope change is not beyond the slope change
threshold, then at step 368, the terrain is classified as "up
slope" and incremented. In a preferred embodiment, the slope
counter is incremented by a predetermined amount.
[0062] If the slope angle is not greater than 0.degree., then at
step 370 it is determined whether the change in slope is greater
than the slope change threshold. In preferred embodiments, the
slope change threshold used in step 370 is the same as the slope
change threshold used in step 364. If the slope change is beyond
the slope change threshold, then at step 372, the terrain is
classified as "down stair" and the slope counter is decremented. In
a preferred embodiment, the slope counter is decremented by a
predetermined amount. If the slope change is not beyond the slope
change threshold, then at step 374, the terrain is classified as
"down slope" and decremented. In a preferred embodiment, the slope
counter is decremented by a predetermined amount. It should be
understood that the embodiments other than a whole-body kinetics
embodiment can use methods similar to the method exemplified in
FIG. 3C.
[0063] Different methods have been proposed for calculation of
mechanical power during running. Power estimation is based on the
estimation of the product of force-velocity or moment-angular
velocity. The common reference method to estimate power is based on
force plate. The ground reaction force during stance phase
F.sub.GRF is used as the force acting on CoM and the power
(P.sub.GRF) is estimated from the integration of CoM acceleration
obtained through F.sub.GRF as follow:
P G .times. R .times. F = F G .times. R .times. F .times. ( v 0 +
.intg. .times. F G .times. R .times. F M .times. dt ) Eq . .times.
( 1 ) ##EQU00001##
[0064] With v.sub.0 an integration constant corresponding to the
mean velocity of the runner of mass M. Skilled artisans can
appreciate that as compared to other methods, e.g., using
multi-segment kinematics, a whole-body kinetics method shows good
behavior and correspondence with oxygen uptake.
[0065] Whole-Body Kinetic Embodiment
[0066] FIG. 4 illustrates an arrangement of sensor assemblies,
sensors, or a combination thereof on a runner for the measurement
of total power. In the arrangement shown, sensors or sensor
assemblies 410, 415 are placed at the waist near the center of mass
of the user and at the foot. Spatio-temporal measurements are taken
at each location. One or more of the sensors 410, 415 can be
mounted to a runner's body as part of an assembly of components, in
a packaging or case, as part of the runner's clothing, to another
item mounted to the runner's body, or some combination thereof.
Preferably, such a sensor assembly is located on the torso of the
runner and most preferably at or within a few inches of the waist.
Different runners can have a different center of mass and the
sensor assembly should be place near that vertical center of mass.
One or more sensors of the sensor assembly located near the center
of mass measure acceleration and velocity in at least 4 directions
(2 vertical dimension directions and 2 horizontal dimension
directions). In some preferred embodiments, one or more sensors
also measure acceleration and velocity in 2 other horizontal
directions orthogonal to the other horizontal directions. Sensors
here can also include a barometer or barometric pressure
sensor.
[0067] At least one sensor assembly (for example, sensor 415) is
located on the body so that the ground inclination during a run can
be measured. Preferably, such a sensor assembly is located on the
shoe or at the foot and includes an IMU, accelerometer, barometric
pressure sensor, some other sensor, or a combination thereof
configured to measure the inclination of the slope during the
stance phase of the runner's gait as described herein. One or more
of the sensors can be used to measure foot orientation for
estimating slope. It is important, particularly for more advanced
users, that the weight and size of a sensor assembly at the foot be
as small and light as possible. Thus, in preferred embodiments, the
architecture of a sensor assembly mounted at the foot is simplified
and efficient by using a smaller microprocessor and memory, only
essential sensors, and less PCB material. For example, in one
preferred embodiment, a sensor assembly can include a single IMU, a
microcontroller, and a wireless communications device to send data
to another assembly or device. To further reduce mass of the sensor
assembly, the components or packaging of components can be
integrated or be made as part of footwear or clothing at the foot
or area of the foot. Placing a sensor at the foot as opposed to
elsewhere on the body can allow for drift correction and for
measuring the foot-ground interface (e.g., ground slope and other
interface characteristics). Sensor arrangements similar to the
arrangement of FIG. 4 can be used with methods similar to the
exemplary method discussed in connection with FIG. 5.
[0068] FIG. 5 illustrates a preferred method for estimating total
power according to preferred embodiments. At step 510, a horizontal
velocity of the center of mass is received from a first sensor or
sensor assembly. The first sensor or sensor assembly is ideally
placed at or near the center of mass of the runner. At step 520, a
vertical velocity of the center of mass is received from the first
sensor or sensor assembly. In some embodiments, a different sensor
or sensor assembly than the first sensor or sensor assembly can
measure the horizontal velocity. Again, this sensor or sensor
assembly is ideally placed at or near the center of mass of the
runner. At step 530, a measurement of horizontal force of the
center of mass is received from a second sensor or sensor assembly.
In some embodiments, the same sensor assembly that measures
velocity of the center of mass can include a sensor to measure the
horizontal acceleration of the center of mass from which a
horizontal force vector can be derived. At step 540, a measurement
of the inclination slope of the foot is received from a third
sensor or sensor assembly. At step 550, the total power (P.sub.ToT)
is estimated from the kinetics relations of the horizontal force
(F.sub.H), horizontal (c.sub.H) and vertical velocity (v.sub.v)
acting on center of mass as well as the slope angle of ground
(a):
P.sub.ToT=F.sub.Hv.sub.H+Mgv.sub.v sin sin .alpha. Eq. (2)
[0069] Force and velocity can be estimated from body acceleration
measured with a sensor placed on waist (close to CoM):
F.sub.H=MA.sub.H Eq. (3)
v.sub.H=v.sub.0+.intg.A.sub.Hdt Eq. (4)
and
v.sub.v=v'.sub.0+.intg.A.sub.vdt Eq. (5)
and the slope can be estimated from the foot inclination during
stance phase using shoe accelerometer or barometric pressure
sensor. Force can be estimated at the sensor assembly measuring
acceleration or at another sensor assembly or computing device.
[0070] Total power estimations are computed at predetermined
intervals. In some embodiments, total power estimations can be
calculated when some triggering event occurs such as when a
velocity, acceleration, or force changes or reaches a threshold.
According to preferred embodiments, total power estimates can be
calculated and changes in total power can be determined. At step
560, a feedback indicator is determined. At step 570, a feedback
indicator is displayed to the runner. Such a feedback indicator can
be based on the total power estimate or a change in the rate of the
total power estimate to indicate to the runner increasing or
decreasing power output during a session. Additionally, according
to preferred embodiments a total power estimate that exceeds or
falls below a predetermined threshold can trigger a feedback
indicator. In some embodiments, a feedback indicator can take the
form or a display element on a smartwatch, smartphone, smart
glasses, and the like. In other embodiments, a feedback indicator
can be audial or pressure indicators.
[0071] According to some preferred embodiments, an initialization
step is included to calibrate the sensors and test their alignment
according to the anatomical frame of the runner. Calibration of the
sensors can be performed at each use and alignment initialization
can be performed once to determine the best or a sufficient
location of a sensor or at each use.
[0072] FIG. 6 illustrates an arrangement of sensor assemblies,
sensors, or a combination thereof on a runner for the measurement
of total power. A plurality of sensors 610-650, preferably IMUs,
are attached to a runner's body. In the embodiment shown, sensors
610-650 are placed at the head, trunk, thigh, shank, foot, and
wrist to estimate the total power at each of those body segments.
According to preferred embodiments, a plurality of sensors or
sensor assemblies are placed at different segments of the body and
sufficiently spaced apart to obtain distinct measurements. Sensors
or sensor assemblies measure inertia and angular velocity of the
respective body segment. The embodiment shown also includes a
sensor or sensor assembly 660 similar to sensor or sensor assembly
415.
[0073] FIG. 7 illustrates an exemplary method 700 in accordance
with embodiments for estimating total power from inertia and
angular velocity from embodiments consistent with FIG. 6. At step
710, an inertial measurement from a first sensor is received. At
step 720, an angular velocity from the first sensor is received. At
step 730, an inertial measurement from a second sensor is received.
At step 740, an angular velocity from the second sensor is
received. According to some embodiments, additional inertial and
angular velocity measurements from additional sensors are received.
In preferred embodiments, the measurements are synchronized so that
an inertia and angular velocity are measured at substantially the
same time and the measurements from the different sensors are taken
substantially at the same time. At step 750, the total power
estimate is calculated. Here we consider the mechanical power of
individual segment of inertia I to estimate the whole power from
the angular velocity (w.sub.j).
P.sub.ToT=>.SIGMA.l.sub.jw.sub.1 Eq. (6)
[0074] At step 760, a feedback indicator is determined. At step
770, the feedback indicator is presented to the runner or some
other user.
[0075] In some embodiments, estimating power output can use machine
learning in accordance with preferred embodiments. Here we consider
also several body segments but instead of using the above equation
we will use machine learning techniques to estimate P.sub.ToT.
Embodiments can use reference data (from a force plate, for
example) for a learning phase. Here, a sensor is located on the
foot, the sensor including a force plate that measures the forces
on the foot as it strikes and releases from the fly through the
stance gait phases.
[0076] Preferred embodiments, including embodiments described
herein, can be used to detect, measure, and report a user's
training or activity progress, fatigue, or injury risks.
Embodiments can be used to provide real-time feedback on
performance and economy (i.e., feedback during an activity).
Embodiments can also be used to measure and follow speed and pace
changes of racers. Such embodiments can be useful during broadcasts
of races. Embodiments of the present invention can obtain data at a
greater granularity than current devices, including: [0077] Data at
each stride (.about.11'000) [0078] Various parameters (VO2,
kinematics) [0079] Real-time data [0080] 3 ms Accuracy
[0081] Embodiments can be used to understand runner profiles for
better experience of purchasing equipment, shoes in particular, and
generate more brand loyalty.
[0082] Machine learning could overcome some drawbacks of a
biomechanical model where many segments are necessary to estimate
accurately the P.sub.ToT. Here we exploit some
correlation/association between segments to minimize the number of
segments and consequently the number of sensors. In embodiments
implementing a machine learning technique, one of the sensors or
another wearable device is a self-learning power meter equipped
with a microprocessor and an embedded machine learning library. The
machine learning library can be implemented according to different
AI techniques known in the art. For example, some non-limiting
suitable techniques include LSTM networks; various types of RNN
(recurrent neural network) such as a Siamese RNN; a CNN (convoluted
neural network); and an MNN (modular neural network).
[0083] In preferred embodiments, the machine learning library would
be incorporated in a wearable device.
[0084] Any and all references to publications or other documents,
including but not limited to, patents, patent applications,
articles, webpages, books, etc., presented in the present
application, are herein incorporated by reference in their
entirety.
[0085] Example embodiments of the devices, systems and methods have
been described herein. As noted elsewhere, these embodiments have
been described for illustrative purposes only and are not limiting.
Other embodiments are possible and are covered by the disclosure,
which will be apparent from the teachings contained herein. Thus,
the breadth and scope of the disclosure should not be limited by
any of the above-described embodiments but should be defined only
in accordance with claims supported by the present disclosure and
their equivalents. Moreover, embodiments of the subject disclosure
may include methods, systems and apparatuses which may further
include any and all elements from any other disclosed methods,
systems, and apparatuses, including any and all elements
corresponding to target particle separation,
focusing/concentration. In other words, elements from one or
another disclosed embodiment may be interchangeable with elements
from other disclosed embodiments. In addition, one or more
features/elements of disclosed embodiments may be removed and still
result in patentable subject matter (and thus, resulting in yet
more embodiments of the subject disclosure). Correspondingly, some
embodiments of the present disclosure may be patentably distinct
from one and/or another reference by specifically lacking one or
more elements/features. In other words, claims to certain
embodiments may contain negative limitation to specifically exclude
one or more elements/features resulting in embodiments which are
patentably distinct from the prior art which include such
features/elements.
* * * * *