U.S. patent application number 14/463630 was filed with the patent office on 2015-02-26 for system, device and method for quantifying motion.
The applicant listed for this patent is Quattriuum Inc.. Invention is credited to Philippe LAVOIE, Olivier TREMBLAY-MUNGER.
Application Number | 20150057111 14/463630 |
Document ID | / |
Family ID | 52480880 |
Filed Date | 2015-02-26 |
United States Patent
Application |
20150057111 |
Kind Code |
A1 |
TREMBLAY-MUNGER; Olivier ;
et al. |
February 26, 2015 |
SYSTEM, DEVICE AND METHOD FOR QUANTIFYING MOTION
Abstract
There is provided a device for motion identification, the device
comprising: an enclosure and a plurality of sensors being provided
with the enclosure and configured to measure acceleration in three
axes and angular motion in three axes. The acceleration may be
measured in a first acceleration range and a second acceleration
range,
Inventors: |
TREMBLAY-MUNGER; Olivier;
(St-Nicolas, CA) ; LAVOIE; Philippe; (Mercier,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Quattriuum Inc. |
Quebec |
|
CA |
|
|
Family ID: |
52480880 |
Appl. No.: |
14/463630 |
Filed: |
August 19, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61867703 |
Aug 20, 2013 |
|
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Current U.S.
Class: |
473/446 |
Current CPC
Class: |
A63B 2220/64 20130101;
A63B 2102/24 20151001; A63B 2230/04 20130101; A63B 2220/76
20130101; A63B 49/08 20130101; A63B 24/0021 20130101; A63B 59/50
20151001; A63B 60/06 20151001; A63B 2220/75 20130101; A63B 53/14
20130101; G06K 9/00342 20130101; A61B 2505/09 20130101; A63B
2220/20 20130101; A63B 2220/801 20130101; G06F 3/0346 20130101;
A63B 24/0003 20130101; A63B 60/10 20151001; A63B 2220/40 20130101;
A63B 60/00 20151001; A63B 60/08 20151001; A63B 2220/18 20130101;
A63B 71/0622 20130101; A63B 2225/54 20130101; A63B 2220/24
20130101; A63B 2220/62 20130101; A63B 2220/836 20130101; A63B
2220/833 20130101; A63B 2102/22 20151001; A63B 2102/18 20151001;
A63B 2220/80 20130101; A63B 69/0026 20130101; A63B 2220/72
20130101; A63B 2230/00 20130101; A63B 2220/30 20130101; A61B 5/1116
20130101; A63B 2225/50 20130101; A63B 2225/74 20200801; A63B 59/70
20151001; A63B 2220/89 20130101; A63B 60/16 20151001; A63B 2220/31
20130101; A61B 5/024 20130101; A63B 2024/0012 20130101; G06F 3/017
20130101; A63B 2024/0071 20130101; A63B 2220/51 20130101; A63B
2071/065 20130101; A63B 2225/15 20130101 |
Class at
Publication: |
473/446 |
International
Class: |
A63B 69/00 20060101
A63B069/00; A63B 59/14 20060101 A63B059/14; A63B 24/00 20060101
A63B024/00 |
Claims
1. A device for motion identification, the device comprising: an
enclosure; and a plurality of sensors being provided with the
enclosure and configured to measure acceleration in three axes in a
first acceleration range, acceleration in three axes in a second
acceleration range, and angular motion in three axes.
2. The device of claim 1, wherein the plurality of sensors
comprises three accelerometers for measuring acceleration in three
axes in the first acceleration range and in the second acceleration
range and at least three gyroscopes for measuring the angular
motion in three axes.
3. The device of claim 1, wherein the plurality of sensor comprises
a first accelerometer group of at least three accelerometers for
measuring acceleration in three axis in the first acceleration
range and a second accelerometer group of at least three
accelerometers for measuring acceleration in three axis in the
second acceleration range.
4. The device of claim 1, wherein the first acceleration range is
between about 0 G to about 10 G and wherein the second acceleration
range is from at least 10 G and above.
5. The device of claim 1, wherein the plurality of sensors
comprises a plurality of accelerometers, a first subset of the
accelerometers measuring at a high measurement rate of more than
1000 measurements per second and a second subset of the
accelerometers measuring at a low measurement rate of less than
1000 measurements per second.
6. The device of claim 1, wherein the plurality of sensor comprises
a magnetometer for measuring the strength and direction of magnetic
fields.
7. The device of claim 1, wherein the enclosure is adapted to be
positioned in the hollow section of a stick handle; and wherein the
enclosure comprises one or more stopper parts each having one or
more tongues for gripping the interior surface of the stick
handle.
8. The device of 1, wherein the plurality of sensors are operable
between a sleep state and a wake state, wherein at least one of the
sensors remains in a wake state while the remainder of the sensors
are operating in a sleep state, and wherein detection of a motion
by the at least one sensor in the wake state triggers activating at
least one of the remainder of the sensors to the wake state.
9. The device of claim 1, further comprising a controller
configured for: receiving one or more measurements taken by the
plurality of sensors during an user-executed movement; and
characterizing the user-executed movement based on the received one
or more measurements, the characterizing comprising: correlating
the received measurements with the sets of predetermined metrics;
and detecting whether the received measurements substantially
matches one of the sets of predetermined metrics.
10. The device of claim 9, wherein the controller is further
configured for: when the received measurements substantially
matches one of the sets of predetermined metrics, identifying the
type of the user-executed movement.
11. The device of claim 10, wherein the controller is further
configured for: quantifying at least one of a duration, speed and
angle of the user-executed movement based on the received
measurements.
12. A system for motion identification, the system comprising: the
device of claim 9; at least one external sensor being external to
the device, and being configured to measure at least one of
vibration, acceleration, rotation, magnetic field, temperature,
humidity, flexion, bend, orientation, distance to an object, sound,
image, heart-beat, blood, wind pressure at the external sensor;
wherein the controller is further configured to receive at least
one measurement from the at least one external sensor; and wherein
characterizing the user-executed movement is further based on the
at least one measurement received from the at least one external
sensor; wherein the external sensor is positioned on one of a user
and a stick used during the user-executed movement.
13. The system of claim 12, wherein the external sensor is a
bending sensor for measuring a mechanical bend of one of a blade
part of a stick and a shaft part of a stick.
14. A method for motion identification, the method comprising:
receiving one or more measurements taken by a plurality of sensors
during an user-executed movement, the measurements being
representative of the user-executed movement and comprising
acceleration measurements in three axes in a first acceleration
range, acceleration measurements in three axis in a second
acceleration range, and angular motion measurements in three axes;
and characterizing the user-executed movement based on the received
one or more measurements, the characterizing comprising:
correlating the received measurements with a plurality of sets of
predetermined metrics; and detecting whether the received
measurements substantially matches one of the sets of predetermined
metrics.
15. The method of claim 14, further comprising: when the received
measurements substantially matches one of the sets of predetermined
metrics, identifying a type of the user-executed movement.
16. The method of claim 15, further comprising: quantifying the
user-executed movement based on the received measurements.
17. The method of claim 16, wherein the plurality of sensor are
fixed to a stick; wherein the received measurements indicate a
motion of the stick, and wherein quantifying the user-executed
movement comprises determining a speed of an object hit by the
stick.
18. The method of claim 17, wherein quantifying the user-executed
movement is further based on at least one additional set of
predetermined metrics chosen swing amplitude, swing duration, stick
speed, stick acceleration, wrist effect, flex analysis, motion
amplitude, and motion behavior, user age, user gender, user
location, user height, user weight, stick length, stick model and
stick flex.
19. The method of claim 14, wherein characterizing the
user-executed movement comprises: detecting a motion starting event
when a first subset of the received measurements substantially
corresponds to one of a plurality of sets of predetermined starting
event metrics, each set of predetermined starting event metrics
being associated with one or more event continuation metrics;
determining a presence of a motion continuation event when a second
subset of the received measurements received after the first subset
substantially corresponds to one of the one or more event
continuation metrics associated to said one of the plurality of
sets of predetermined starting event metrics; said one of the event
continuation metrics being associated with one or more event
completion metrics; and determining the presence of a motion
completion event when a third subset of the received measurements
received after the second subset substantially corresponds to one
of the one or more event completion metrics associated to said one
of the event continuation metrics.
20. The method of claim 19, wherein the motion starting event is a
backswing of a stick, the motion continuation event is a downswing
of a stick and the motion completion event is an impact of the
stick with an object; and wherein detecting the motion starting
event comprises detecting from the received measurements a motion
pattern associated with the backswing of the stick; wherein
determining the presence of the motion continuation event comprises
monitoring received acceleration measurements and detecting that
the received acceleration measurements exceeds a predetermined
acceleration threshold; and wherein determining the presence of a
motion completion event comprises monitoring over an impact
detection period received measurements and detecting that the
received one or more measurements exceeds an impact threshold.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims priority on U.S. 61/867,703
filed on Aug. 20, 2013, that is hereby incorporated by reference in
its entirety.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates generally to systems, devices
and methods for motion capture, motion tracking, motion detection
and motion quantification, especially for sport applications. More
specifically, the present disclosure relates to the automatic
identification, measurement and quantification of a player's
individual performance, such as a hockey player.
BACKGROUND OF THE DISCLOSURE
[0003] Sport practitioners are eager to acquire motion metrics
indicative of their training or gaming performance, for primarily
performance improvement purpose, and secondarily for comparing
their performance to those of their peers or of top professional
athletes.
[0004] Sensor based motion tracking systems are limited in the
complexity of gestures or movements that are able to quantify.
Inertial sensor products, also known as IMU (inertial measurement
units), are confined to motion tracking in sports where the
predictability of actions is easier. Sport applications using IMUs
remain captive of repetitive movements and simple gestures such as
stride, walking or running. A stride, using a single motion sensor
positioned at the foot, is probably the simplest movement to
quantify.
[0005] Current sensor based motion tracking systems suffer from
several limitations. No such motion tracking system today is able
to detect, discriminate and quantify complex gestures in a non
choreographed motion sequence. As an example, IMUs wearable on a
golf glove which can detect the acceleration, velocity, tempo,
position and posture of the sensor and estimate the position and
movement of the golf club, are among the most advanced movement
quantification systems on the market today. To use such a golf
swing quantifier, the player must typically manually activate
and/or configure a sensor prior to a swing, and again at the end of
the swing movement, in order to specify the movement sequence to be
quantified. Manual activation of the sensor prior to each movement
is impractical in most sports where movements are not
choreographed, such as racket sports and team sports.
[0006] Moreover, currently available IMU based motion tracking
systems are limited by at least one of the two following elements:
(a) they lack the capability of blind identification of motion
patterns and/or (b) they lack the capability of blindly identify
complex motions. In (a), by "blind" we refer to the
non-choreographed nature of motion executed in most sports where
the player does not "inform" the sensor of its intention to make a
specific move. As an illustration, IMU-based, golf movement
quantification products cannot discriminate a "golf drive" motion
from a "golf putting" motion. The user must instruct the sensor of
its intentions prior to each specific move. In (b), by "complex" we
refer to motion that requires more than acceleration measurements
to be detected and identified. In other commercial products, run
movement quantifiers use either simple accelerometers or pressure
sensors to detect steps, and then compute the number of steps per
second to evaluate the running speed. Jump movement quantifiers for
basketball use shoe-based pressure sensors to estimate jump height.
Both products focus on simple motion, the "steps", that are easy to
detect and quantify by identifying the zero-velocity states, with
accelerometers, or the zero pressure states with the pressure
sensors. All currently available IMU based products cannot identify
more complex motion hidden in motion noise. Referring to the
examples mentioned above, a run movement quantifier could not
detect a ball kick in a soccer game. A jump quantifier for
basketball could not discriminate a three point shot from a dunk
shot maneuver.
[0007] Consequently, no system of the prior art provides motion
tracking system based on inertial sensors optimized for complex
gestures. No system of the prior art provides motion tracking
system based on inertial sensors which detect, discriminate and
quantify complex gestures in a non-choreographed motion sequence.
No system of the prior art provides motion tracking system based on
inertial sensors which can automatically operate without being
activated. No prior art system provides a motion tracking system
based on inertial sensors that can be operated by a player for long
period of time without the need for external systems.
[0008] Integration of motion sensors on sport equipment requires an
important miniaturization effort to minimize sensors' weight and
avoid obstruction so that ideally the sensors become unnoticed and
do not affect movement in any way. Manufacturing cost
considerations also calls for a diminution in the number of
electronic components involved in the movement sensors.
SUMMARY OF THE DISCLOSURE
[0009] It would thus be highly desirable to be provided with an
apparatus or a method that would at least partially solve one of
the problems previously mentioned or that would be an alternative
to the existing technologies.
[0010] According to one aspect, there is provided device for motion
identification, the device comprising: an enclosure; and a
plurality of sensors being provided with the enclosure and
configured to measure acceleration in three axes and angular motion
in three axes.
[0011] According to another aspect, there is provided a device for
motion identification and at least one external sensor being
external to the device, and being configured to measure at least
one of vibration, acceleration, rotation, magnetic field,
temperature, humidity, flexion, bend, orientation, distance to an
object, sound, image, heart-beat, blood, wind pressure at the
external sensor, wherein the controller is further configured to
receive at least one measurement from the at least one external
sensor, and wherein characterizing the user-executed movement is
further based on the at least one measurement received from the at
least one external sensor.
[0012] According to yet another aspect, therein provided a method
for motion identification, the method comprising: receiving one or
more measurements taken by a plurality of sensors during an
user-executed movement, the measurements being representative of
the user-executed movement and comprising acceleration measurements
in three axes in a first acceleration range, acceleration
measurements in three axis in a second acceleration range, and
angular motion measurements in three axes, and characterizing the
user-executed movement based on the received one or more
measurements.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The following drawings represent examples that are presented
in a non-limitative manner.
[0014] FIG. 1 is a schematic projected view showing the components
of the inertial sensor unit.
[0015] FIG. 2 is a schematic projected view of the enclosure part
of the inertial sensor unit with stopper parts.
[0016] FIG. 3A-B-C are graphs depicting the motion patterns
associated with a hockey slap shot motion.
[0017] FIG. 4A-B are flowcharts exemplifying the functioning of the
motion quantifying algorithm.
[0018] FIG. 5 is an organisation chart showing the network
components used by the motion quantification system and method when
transmitting, using and managing performance data.
[0019] FIG. 6A-D are representations of interfaces using a hand
held device, for the presentation of user profiles, performance
statistics, communication of metrics and comparison of quantified
motion data for a single user and several users.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0020] The following examples are presented in a non-limitative
manner.
[0021] "stick" as used herein refers to any sport instrument such
as a stick or bat used to contact an object such as a ball,
including without limitation a hockey stick, a baseball bat, a
cricket bat, a golf club, a tennis racket, or a badminton
racket.
[0022] "puck" or "ball" as used herein refers indistinctively to
any object that is intended to interact with a stick during sport,
such as without limitation, a hockey puck, a golf ball, a tennis
ball, another hockey stick.
[0023] According to various exemplary embodiments, there is
provided a device for motion identification and quantification,
comprising: An inertial sensor to provide motion data, comprising a
plurality of inertial sensors including at least one but typically
three accelerometers for measuring acceleration in three axis, at
least one but typically three gyroscopes for measuring angular
motion in three axis, a microcontroller executing algorithms to
detect, interpret and quantify motion data, a memory means to store
motion data and quantified performance data, a telecommunication
means to connect to a remote computer, wherein the inertial sensor
measures high acceleration (ex: greater than or equal to about 10
G, greater than or equal to about 15 G, between about 10 G to about
20 G, or tens of G for measuring high impacts) and small
acceleration (ex: less than about 3 G, less than about 10 G, or
less than about 15 G), at different frame rates including a frame
rate higher than 1000 measurements per second and a frame rate of
less than 1000 measurements per second, wherein the device for
motion identification and quantification is located on a single
measurement point located on a stick or on a user.
[0024] For example, the inertial sensor comprises a magnetometer
for measuring the strengths and direction of magnetic fields.
[0025] For example, the device is positioned inside the hollow
section of a stick handle.
[0026] For example, the device is positioned near the extremity of
a stick handle.
[0027] The device comprises an enclosure adapted to fit inside the
hollow section of a stick, using stopper parts located at the
bottom and top sections of said enclosure, each stopper part
consisting of several tongues located on at least two sides of the
stopper part.
[0028] For example, the device is by default in continuous
operation and activates itself automatically by motion, wherein one
sensor of either said accelerometer group or gyroscope group is
kept powered in wake mode at all time while the inertial sensor is
otherwise turned off, and wherein said one sensor can detect a
vertical, lateral or rotational motion, which triggers powering on
of the device.
[0029] For example, the device recognises a stick among a plurality
of sticks identified with barcodes or RFID tags, wherein the device
is provided with a barcode or RFID tag reading means, and wherein
the sensor unit is affixed on the exterior surface of the stick, or
wear by user on a glove or on a wrist
[0030] For example, the device automatically recognises when user
executes a motion.
[0031] For example, the device automatically recognises when user
hits an object with the stick, by detecting the vibrations and
impact associated with said hit.
[0032] According to various exemplary embodiments, there is
provided a system for motion identification and quantification,
comprising: an inertial sensor to provide motion data, comprising a
plurality of inertial sensors including at least one but typically
three accelerometers for measuring acceleration in three axis, at
least one but typically three gyroscopes for measuring angular
motion in three axis, a microcontroller executing algorithms to
detect, interpret and quantify motion data, a memory means to store
motion data and quantified performance data, a telecommunication
means to connect to a remote computer, a remote computer for
presenting and communicating motion data and quantified performance
data, at least one external sensor connected or communicating with
said inertial sensor, from a group of sensors comprising sensors
for sensing vibration, acceleration, rotation, magnetic field,
temperature, humidity, flexion, bend, orientation (such as
magnetometer), distance to an object, sound, image, heart-beat,
blood, wind pressure, wherein raw sensing data from said external
sensor is processed by said microcontroller.
[0033] For example, external sensor is positioned on the stick or
on a user.
[0034] For example, the external sensor is a heterogeneous or
homogeneous combination of several such external sensors.
[0035] For example, the said external sensor is a bending sensor to
recognise mechanical bend of the appropriate part, affixed on or
moulded in the blade part or the shaft part of a stick.
[0036] According to various exemplary embodiments, there is
provided a method for installing and using a sensor device in the
hollow section of a stick handle such as a hockey stick, the sensor
device having an enclosure adapted to fit inside the hollow section
of a stick, using stopper parts located at the bottom and top
sections of said enclosure, each stopper part consisting of several
tongues located on at least two sides of the stopper part, a
comprising the steps of removing the cap that closes the hollow
section at the end of the stick, placing the sensor unit in the
hollow section of the stick, providing a progressive pressure
downward on the sensor unit, until only the top part of enclosure
90 covers the top part of the stick handle, powering on the sensor
device by a motion of the stick.
[0037] According to various exemplary embodiments, there is
provided a method for identifying and quantification motion, using
a sensor unit comprising an inertial sensor for providing
acceleration and rotation data in three axis, a microprocessor
executing algorithms for identifying and quantifying motion and
memory means, located on a user or on a stick, comprising the steps
of: providing raw motion data using the inertial sensor mounted on
a stick or on a user, said raw motion data comprising acceleration
in three axis and gravity data including angular speed variation,
roll, pitch and yaw, for a given duration, detecting by
correlation, from said raw motion data, the presence of motion
patterns similar to motion patterns stored on a memory located on
said inertial sensor, which are representative of the motion
patterns associated with movements typically performed during
practice of a given sport, using algorithms executed on a
microcontroller on said inertial sensor, identifying by
correlation, from the detected motion patterns, the type of
movements performed by user, among a library of typical movements
associated with a given sport stored on said memory, using
algorithms executed on said microcontroller, confirming if a motion
pattern qualifies or not as a typical movement, using algorithms
executed on said microcontroller and, if a motion pattern
qualifies, quantifying said motion, and providing quantified motion
data, using raw sensing data on said memory associated with said
motion processed by a second algorithms executed on said
microcontroller, storing the quantified motion data and its
associated raw in said memory, wherein quantified motion data
provides information on a motion including its duration, its speed
and angle amplitude.
[0038] For example, regarding the motion of a stick, the second
algorithm computes an estimation of the maximum speed of an object
hit by said stick that is representative of the real speed of said
object immediately after impact, using raw data provided by said
inertial sensor and quantified motion data.
[0039] For example, said quantified motion data is correlated by a
second algorithm executed by microcontroller using at least one
value from a group of motion metrics including "swing amplitude",
"swing duration", "stick speed", "stick acceleration", "wrist
effect", "detailed flex analysis", "motion amplitude" and "motion
behaviour".
[0040] For example, said quantified motion data is correlated by
second algorithm executed by microcontroller using at least one
user centric data from a group of user data including "user age",
"user gender", "user location", "user height", "user weight",
"stick length", "stick model" and "stick flex".
[0041] For example, the algorithm, while identifying a motion
pattern, discriminates and quantifies a motion pattern among
motions that share same or equivalent motion patterns stored on
said memory, using a larger set of motion metrics that are
pre-established for each motion pattern.
[0042] For example, the sensor unit comprise a communication means
used to transmit quantified motion data to the remote computer.
[0043] For example, the sensor unit is used in recording mode while
specific motions are executed by user.
[0044] According to various exemplary embodiments, there is
provided a method for identifying and quantification motion, using
a sensing unit comprising an inertial sensor for providing
acceleration and rotation data in three axis, a microprocessor
executing algorithms for identifying and quantifying motion and
memory means, located on a user or on a stick, comprising the steps
of: automatic activation by a motion of the sensing unit, starting
motion detection, wherein said sensor unit is initialized and
starts real-time monitoring and analysis of sensing data from the
inertial sensor, starting a potential motion event, when the
algorithm detects and identifies a motion pattern associated to a
backswing motion, starting a potential motion acceleration event,
when the algorithm detects and identifies a motion pattern that is
greater in value than a predetermined lowest threshold and lower in
value than a higher threshold, starting a potential motion
continuation event, when the algorithm detects and identifies a
motion pattern associated to a potential downswing motion, starting
potential impact detection metric, by which the algorithm monitors
the variation of the sensing data over an impact detection period,
and log the event as a potential impact when the variation reaches
a predetermined level, starting impact detection metric, by which
the algorithm detects and identifies an impact event following a
downswing motion within a predetermined time period, starting shot
detection, by which the algorithm confirms an impact event, and
quantifies the motion metrics associated with the event.
[0045] For example, sensing data from the inertial sensor provides
metric value expressed in m*rad/s.sup.3 as outputs.
[0046] According to various exemplary embodiments, there is
provided a system for motion identification and quantification,
comprising: a sensing unit comprising an inertial sensor for
providing acceleration and rotation data in three axis, a
microprocessor executing algorithms for identifying and quantifying
motion, memory means hosting a database, and communication means,
located on a user or on a stick, a remote computer synchronized
with the sensing unit through the communication means, for updating
data and usage statistics, an external computer with a database,
connecting to the remote computer using a wired or wireless
connection, an online platform accessible form the remote computer
and the external computer, wherein sensing data from the inertial
sensor and quantified motion data provided by the algorithm are
transmitted from the sensing unit to the remote computer using the
communication means.
[0047] For example, the remote computer connected to the online
platform enables user of the remote computer to present and
communicate its sport performance to other users and to compare its
sport performance to those of other users.
[0048] For example, wherein the system comprises hand held devices
connected to the external computer or to the remote computer that
allows third parties to visualize, present, share and compare
quantified motion information and user performance data.
[0049] For example, according to devices of the present disclosure,
the plurality of sensors comprises three accelerometers for
measuring acceleration in three axes in the first acceleration
range and in the second acceleration range.
[0050] For example, according to devices of the present disclosure,
the plurality of sensors comprises at least three gyroscopes for
measuring the angular motion in three axes.
[0051] For example, according to devices of the present disclosure,
the plurality of sensor comprises a first accelerometer group of at
least three accelerometers for measuring acceleration in three axis
in the first acceleration range and a second accelerometer group of
at least three accelerometers for measuring acceleration in three
axis in the second acceleration range.
[0052] For example, according to devices of the present disclosure,
the plurality of sensors comprises at least three gyroscopes for
measuring the angular motion in three axes.
[0053] For example, according to devices of the present disclosure,
the first acceleration range is between about 0 G to about 10 G and
wherein the second acceleration range is from at least 10 G and
above.
[0054] For example, according to devices of the present disclosure,
the second acceleration range is from about 10 G to about 100
G.
[0055] For example, according to devices of the present disclosure,
the plurality of sensors comprises a plurality of accelerometers, a
first subset of the accelerometers measuring at a high measurement
rate of more than 1000 measurements per second and a second subset
of the accelerometers measuring at a low measurement rate of less
than 1000 measurements per second.
[0056] For example, according to devices of the present disclosure,
the plurality of sensors comprises a plurality of gyroscopes, a
first subset of the gyroscopes measuring at a high measurement rate
of more than 1000 measurements per second and a second subset of
the gyroscopes measuring at a low measurement rate of less than
1000 measurements per second.
[0057] For example, according to devices of the present disclosure,
the plurality of sensor comprises a magnetometer for measuring the
strength and direction of magnetic fields.
[0058] For example, according to devices of the present disclosure,
the plurality of sensors comprises a plurality of magnetometers, a
first subset of the magnetometers measuring at a high measurement
rate of more than 1000 measurements per second and a second subset
of the magnetometers measuring at a low measurement rate of less
than 1000 measurements per second.
[0059] For example, according to devices of the present disclosure,
the plurality of sensors are located within the enclosure in close
proximity of one another.
[0060] For example, according to devices of the present disclosure,
the enclosure is adapted to be positioned in the hollow section of
a stick handle.
[0061] For example, according to devices of the present disclosure,
the enclosure is adapted to be positioned proximate the extremity
of a stick handle.
[0062] For example, according to devices of the present disclosure,
the enclosure comprises one or more stopper parts each having one
or more tongues for gripping the interior surface of the stick
handle.
[0063] For example, according to devices of the present disclosure,
the plurality of sensors are operable between a sleep state and a
wake state, wherein at least one of the sensors remains in a wake
state while the remainder of the sensors are operating in a sleep
state, and wherein detection of a motion by the at least one sensor
in the wake state triggers activating at least one of the remainder
of the sensors to the wake state.
[0064] For example, devices of the present disclosure further
comprise a memory to store measurements taken by the plurality of
sensors; and a communication module for transmitting the stored
measurements to an external device.
[0065] For example, devices of the present disclosure further
comprise a controller configured for receiving one or more
measurements taken by the plurality of sensors during an
user-executed movement; and characterizing the user-executed
movement based on the received one or more measurements.
[0066] For example, devices of the present disclosure further
comprise a data storage device having stored thereon a plurality of
sets of predetermined metrics; and wherein characterizing the
received measurements comprises correlating the received
measurements with the sets of predetermined metrics; and detecting
whether the received measurements substantially matches one of the
sets of predetermined metrics.
[0067] For example, according to devices of the present disclosure,
the controller is further configured for when the received
measurements substantially matches one of the sets of predetermined
metrics, identifying the type of the user-executed movement.
[0068] For example, according to devices of the present disclosure,
the controller is further configured for quantifying the
user-executed movement based on the received measurements.
[0069] For example, according to devices of the present disclosure,
quantifying the user-executed movement comprises quantifying a
duration, speed and angle of the user-executed movement.
[0070] For example, according to devices of the present disclosure,
the angle of the user-executed movement comprises an angle in a
first plane and an angle in a second plane.
[0071] For example, devices of the present disclosure further
comprise an identification tag reader.
[0072] For example, a system for motion identification further
comprises a devices as disclosed herein, at least one external
sensor being external to the device, and being configured to
measure at least one of vibration, acceleration, rotation, magnetic
field, temperature, humidity, flexion, bend, orientation, distance
to an object, sound, image, heart-beat, blood, wind pressure at the
external sensor; wherein the controller is further configured to
receive at least one measurement from the at least one external
sensor; and wherein characterizing the user-executed movement is
further based on the at least one measurement received from the at
least one external sensor.
[0073] For example, according to systems of the present disclosure,
the external sensor is positioned on one of a user and a stick used
during the user-executed movement.
[0074] For example, according to systems of the present disclosure,
the external sensor is a bending sensor for measuring a mechanical
bend of one of a blade part of a stick and a shaft part of a
stick.
[0075] For example, according to systems of the present disclosure,
the external sensor takes measurements at a high measurement rate
or more than 1000 measurements per second and at a low measurement
rate of less than 1000 measurements per second.
[0076] For example, according to methods of the present disclosure,
characterizing the received measurements comprises correlating the
received measurements with a plurality of sets of predetermined
metrics; and detecting whether the received measurements
substantially matches one of the sets of predetermined metrics.
[0077] For example, methods of the present disclosure further
comprise when the received measurements substantially matches one
of the sets of predetermined metrics, identifying a type of the
user-executed movement.
[0078] For example, methods of the present disclosure further
comprise quantifying the user-executed movement based on the
received measurements.
[0079] For example, according to methods of the present disclosure,
quantifying the user-executed movement comprises quantifying a
duration, speed and angle of the user-executed movement.
[0080] For example, according to methods of the present disclosure,
the plurality of sensor are fixed to a stick, wherein the received
measurements indicate a motion of the stick, and wherein
quantifying the user-executed movement comprises determining a
speed of an object hit by the stick.
[0081] For example, according to methods of the present disclosure,
quantifying the user-executed movement is further based on at least
one additional set of predetermined motion metrics chosen from
swing amplitude, swing duration, stick speed, stick acceleration,
wrist effect, flex analysis, motion amplitude, and motion
behavior.
[0082] For example, according to methods of the present disclosure,
quantifying the user-executed movement is further based on at least
one additional set of predetermined user metrics chosen from user
age, user gender, user location, user height, user weight, stick
length, stick model and stick flex.
[0083] For example, according to methods of the present disclosure,
the plurality of sets of predetermined metrics are motion
patterns.
[0084] For example, according to methods of the present disclosure,
when the received measurements substantially matches more than one
set of predetermined metrics, identifying the type of the
user-executed movement is based on correlation of the received
measurements with at least one additional set of predetermined
metrics.
[0085] For example, according to methods of the present disclosure,
characterizing the user-executed movement comprises detecting a
motion starting event when a first subset of the received
measurements substantially corresponds to one of a plurality of
sets of predetermined starting event metrics, each set of
predetermined starting event metrics being associated with one or
more event continuation metrics; determining a presence of a motion
continuation event when a second subset of the received
measurements received after the first subset substantially
corresponds to one of the one or more event continuation metrics
associated to said one of the plurality of sets of predetermined
starting event metrics; said one of the event continuation metrics
being associated with one or more event completion metrics; and
determining the presence of a motion completion event when a third
subset of the received measurements received after the second
subset substantially corresponds to one of the one or more event
completion metrics associated to said one of the event continuation
metrics.
[0086] For example, according to methods of the present disclosure,
the motion starting event is a backswing of a stick, the motion
continuation event is a downswing of a stick and the motion
completion event is an impact of the stick with an object.
[0087] For example, according to methods of the present disclosure,
detecting the motion starting event comprises detecting from the
received measurements a motion pattern associated with the
backswing of the stick; determining the presence of the motion
continuation event comprises monitoring received acceleration
measurements and detecting that the received acceleration
measurements exceeds a predetermined acceleration threshold; and
determining the presence of a motion completion event comprises
monitoring over an impact detection period received measurements
and detecting that the received one or more measurements exceeds an
impact threshold.
[0088] For example, according to methods of the present disclosure,
the plurality of sensors are operable to measure acceleration in
three axes in a range of approximately 0 G to 16 G.
[0089] Many human movements associated with sport are particularly
complex, especially when a player use a sport instrument such as a
stick or a racket for contact on a ball or other objects. In
hockey, a slap shot combines fine and precise movements at high
accelerations, especially as the hockey stick hits the playing
surface before hitting the puck. During a slap shot, the two upper
members are in mechanical connection with a first object, the
stick, to interact with a second object, the puck and surface made
of ice, hard floor or turf. Accurately capturing the entire
movement sequence involved in a slap shot therefore requires
measurement of both low accelerations (less than 3 G) movements at
a great precision and measurement of high accelerations (greater
than or equal to 3 G, such as tens of G) requiring less accuracy.
Such characteristics of movement can be found, with necessary
adaptations, in other sports and movements such as golf swing,
baseball batting and tennis, among other.
[0090] Currently, no single inertial sensor or accelerometer on the
market offers a wide dynamic range enabling the concurrent capture
of high and low acceleration movements at high resolution. Such an
inertial sensor would have technical specifications able to provide
an optimized balance between accuracy of motion detection and wide
dynamic range.
[0091] With respect to a sport accessory designed to hit another
object, such as a hockey stick, the ideal positioning for an
inertial sensor, in terms of motion capture, would be close to the
point where the performance metrics are easier to measure. For an
hockey stick, the lower extremity of the stick as close to the
hockey stick blade as possible, where acceleration and movement
amplitude are maximal remains the intuitive placement. However,
positioning a sensor on or near a hockey stick blade is impractical
due to very frequent mechanical shocks affecting the stick that
would damage, temporarily or permanently, the sensor. Most
commercially available inertial sensor units are temporarily
disabled by strong impact, where a sharp "shake" movement induces
unreliable measurements. Implementing a sensor inside the stick
near or on the hockey stick blade would require structural
modification to the stick that would prevent user friendly
retrofitting in existing sticks. Any weight addition located far
from the hand placement tends to unbalance the stick in a way
appreciable to most hockey players.
[0092] Consequently, no prior art system provides a motion tracking
device that can be easily and practically integrated in the handle
section of a sport instrument such as a hockey stick, and therefore
protecting the inertial sensor against mechanical shocks, without
compromising in movement tracking performance. Such a device would
be simple of operation and user friendly, such that a player could
easily and rapidly install the device in any stick having a hollow
section.
[0093] As provided by various exemplary embodiments disclosed
herein, installing the motion sensor inside the handle of existing
sticks such as hockey sticks would maximize user friendliness while
insulating the sensors from most mechanical shocks. A majority of
hockey sticks are made of composite material with a hollow section
where a sensor can be installed. However, positioning motion sensor
near, on, or inside the handle part of the stick is
counter-intuitive in terms of motion capture performance. The
handle follows the angular motion of a stick, at a fraction of the
acceleration felt by the opposite end of the stick. Moreover,
hockey sticks are made of flexible material enabling the stick to
gather important tension when hitting the ice, before liberating
tension as kinetic energy when hitting the puck, for maximal puck
speed and movement efficiency. It may be impractical to quantify
the dynamic behaviour of the stick by only considering acceleration
on the handle part of the stick, where the stick does not suffer
bending.
[0094] After measurement, acceleration data can be processed by
algorithms to first identify and then quantify movements, and
acceleration data can be adapted so as to take optimal advantage of
the accelerometer capabilities.
[0095] It is a challenge to offer a generic movement tracking
devices intended for use by any player of a given sport,
irrespective of their level of practice in such sport, while
providing a consistent accuracy of movement quantification and
easiness of use. Other technical difficulties lay in the nature of
performance metrics to be presented to different types of users of
a given sport. The value of measurement constants specific to each
user (per example, length of arms or legs) must be taken in
consideration. The range of motion variation for a typical movement
performed across a large population of users must be considered.
Yet other technical difficulty lay in the format under which the
captured movement data can be presented to users in order to
provide useful and accurate quantification of movement
representative of the user's performance.
[0096] A need of the market that is not fully addressed by prior
art is to provide a motion tracking system based on inertial
sensors offering a wide dynamic range enabling the concurrent
capture of high and low acceleration movements at high resolution
that is optimized for complex gesture tracking.
[0097] A need of the market that is not fully addressed by prior
art is to provide a motion tracking system based on inertial
sensors which can detect, discriminate, identify and quantify
gestures automatically among a plurality of heterogeneous
non-choreographed movements.
[0098] Another need of the market that is not fully addressed by
prior art is to provide a portable motion sensor which can
automatically operate by recognizing movement, without the need for
prior manual activation, that does not require any intervention
from the player for its operation, and that can be used seamlessly
over a long period of time.
[0099] Another further need of the market that is not fully
addressed by prior art is to provide an inertial sensor unit of
solid construction that can withstand the mechanical stress imposed
on a sport instrument such as a hockey stick, that is easy to
install, to calibrate and to operate with a minimum of operations
while providing a high accuracy of movement quantification.
[0100] The systems, devices and methods for quantifying motion
described herein according to various exemplary embodiments at
least partially address these shortcomings. The systems, devices
and methods for quantifying motion described herein according to
various exemplary embodiments are intended to meet the needs of
sport practitioners to better understand their performance during
practice or competition, through movement tracking, movement
analysis and quantification of movements, especially
non-choreographed movements such as those encountered in racket
sports and team sports.
[0101] The systems, devices and methods for quantifying motion
described herein according to various exemplary embodiments may be
applied for sports involving the use of a stick, a bat or a racket
for contact with another object, such as a ball, a puck or another
object of the same type, such as per example and without limitation
ice hockey, field hockey, baseball, cricket, polo, golf, tennis,
badminton and lacrosse.
[0102] According to various exemplary embodiments, a device for
motion identification includes an enclosure and a plurality of
sensors being provided with the enclosure and configured to measure
acceleration in three axes in a first acceleration range,
acceleration in three axes in a second acceleration range, and
angular motion in three axes.
[0103] Referring now to FIGS. 1-2, a motion sensor unit 10
comprises an inertial sensor 20, a microcontroller 30 which
executes program instructions to detect, interpret and quantify
motion data, a memory means 40 to store data including motion data
and performance data and a telecommunication means 50, a clock 60,
a battery 70, a board 80 that holds the electronic components
mentioned above and a protective enclosure 90.
[0104] The microcontroller 30 described herein may be implemented
in hardware or software, or a combination of both. It may be
implemented on a programmable processing device, such as a
microprocessor or microcontroller, Central Processing Unit (CPU),
Digital Signal Processor (DSP), Field Programmable Gate Array
(FPGA), general purpose processor, and the like. In some
embodiments, the programmable processing device can be coupled to
program memory, which stores instructions used to program the
programmable processing device to execute the controller. The
program memory can include non-transitory storage media, both
volatile and non-volatile, including but not limited to, random
access memory (RAM), dynamic random access memory (DRAM), static
random access memory (SRAM), read-only memory (ROM), programmable
read-only memory (PROM), erasable programmable read-only memory
(EPROM), electrically erasable programmable read-only memory
(EEPROM), flash memory, magnetic media, and optical media.
[0105] The inertial sensor 20 is a sensor arrangement comprising a
plurality of motion sensors, typically an accelerometer group 22
for measuring acceleration in three axis, a gyroscope group 24 for
measuring angular motion in three axis and optionally magnetometers
28 for measuring the strengths and direction of magnetic fields.
The accelerometer group 22 comprise a plurality of accelerometers,
for example in groups of three accelerometers for multi axis
measurements, of different ranges: high acceleration (ex: greater
than or equal to about 10 G, greater than or equal to about 15 G,
about 10 G to about 20 G, between about 10 G and about 20 G, or
tens of G for measuring high impacts), small acceleration (ex: less
than about 3 G, less than about 10 G, or less than about 15 G for
high accuracy measurement of motions), high frame rate (higher than
1000 measurements per second for high resolution motion analysis),
low frame rate (less than 1000 measurements per second, for low
frequency motion detection and reduced power consumption), etc. For
example, one group of three accelerometers may be operable to
measure acceleration in three axes in the high acceleration range
and in the small acceleration range. For example, the one group of
three accelerometers may be operable to measure acceleration in
three axes in an acceleration range of about 0 G to about 16 G.
Technical requirements similar to those of accelerometer group 22
apply, with the necessary adaptation, to gyroscope group 24. The
accelerometer group 22 and the gyroscope group 24 are typically
assembled as closed as possible to one another on a single chip or
on two distinct chips on board 80. Optionally, inertial sensor 20
can comprise one or several magnetometers 28. Since accelerometers
have limited precision in rotational movements at constant speed,
directional information may be obtained from magnetometer 28 or an
electronic compass (not shown), thereby enhancing the precision of
recorded data.
[0106] The raw sensor data provided by the inertial sensor 20 is
received by a data acquisition system integrated to microcontroller
30. The data is then stored in memory means 40 or transmitted to a
remote computer 100 such as a handheld device or a smart phone, or
another external data reception system accessible locally or based
on a remote network, preferably via wireless communication using
telecommunications means 50.
[0107] Remote computer 100 is typically installed with an operation
system, software applications and logical device with multiple
processing cores and/or CPU arrangement. The remote computer 100
described herein may be implemented in computer programs executing
on programmable computers, each comprising at least one processor,
a data storage system (including volatile and non-volatile memory
and/or storage elements), at least one input device, and at least
one output device. For example, and without limitation, the
programmable computer may be a programmable logic unit, a mainframe
computer, server, and personal computer, cloud based program or
system, laptop, personal data assistance, cellular telephone,
smartphone, or tablet device.
[0108] Each program is preferably implemented in a high level
procedural or object oriented programming and/or scripting language
to communicate with a computer system. However, the programs can be
implemented in assembly or machine language, if desired. In any
case, the language may be a compiled or interpreted language. Each
such computer program is preferably stored on a storage media or a
device readable by a general or special purpose programmable
computer for configuring and operating the computer when the
storage media or device is read by the computer to perform the
procedures described herein.
[0109] Memory means 40 is may be a RAM (random-access memory), DRAM
or SRAM memory unit such as a flash memory drive. Communications
means 50 is typically a wireless radio communication modem such as
Bluetooth modem, a WiFi modem, an ANT+, a near-field communication
modem, etc. Optionally, the motion sensor 10 comprise a coprocessor
52 dedicated to wired or wireless communication with a specific
external computer, such as per example an authentication
coprocessor by company Apple, inc. that enable connection between
the sensor unit 10 and an device of the iPhone product line. Clock
60 is a real-time clock (RTC) electronic circuit that keeps time
and allow time-related labelling of motion event quantifying by the
microcontroller 30. Battery 70 is used to power the inertial sensor
20 and other circuits of the motion sensor unit 10, and comprise a
miniaturized battery, such as a Lithium Polymer battery which
capacity is defined by the required operation and standby time.
Exemplary battery capacities range from 100 mAh to 500 mAh and
enable operation time ranging from one to two hours to over thirty
hours. A universal connector 71 on the top face of the enclosure 90
connected to microcontroller 30 allows recharging of the battery
and connection to an external computer or hand held device for
software updates and upgrades, and data transfer.
[0110] The sensor unit 10 may also include a battery status monitor
72 comprising a circuit 73 and a LED indicator 74 that is affixed
on the external top face of the enclosure 90. A display panel 75
and an on/off switch (not shown) are also located on the top face
of enclosure 90. Display panel 75, typically a liquid crystal
display or OLED display is connected to microcontroller 30 and is
used to provide user information such as memory means 40 status,
and performance data such as movement speed, number of movements in
memory, and status of a connexion to an external device using
communications means 50 or the universal connector 71.
[0111] The motion sensor unit 10 is made of standard components
available on the market from manufacturer such as ST
Microelectronics Inc. (Scottsdale, Ariz.) or Analog Device Inc.
(Norwood, Mass.) for the accelerometer 22, ST Microelectronics Inc.
(Scottsdale, Ariz.) or InvenSense (Sunnyvale, Calif.) for the
gyroscope 24, Texas Instruments Inc. (Dallas, Tex.) or Atmel
Corporation (San Jose, Calif.) for the microcontroller 30 and
memory means 40, and Roving Networks, Inc. (Los Gatos, Calif.),
Texas Instruments Inc. (Dallas, Tex.) or Blueradios Inc.
(Englewood, Colo.) for the communication means 50.
[0112] As shown in FIG. 2, the enclosure 90 is adapted to fit
inside the hollow section of a stick (not shown) using stopper
parts 92 located at the bottom and top sections of the enclosure.
Each stopper part 92 comprises several tongues 94 located on each
of the four sides of the stopper part and enable a tight surface
contact with the internal sides of the stick's hollow handle.
Enclosure 90 is made of a material such as plastic or PVC
(Polyvinyl chloride) moulded plastic that is produced easily using,
for example, injection moulding techniques. Installation of the
sensor unit 10 requires removing the cap that closes the hollow
section at the end of the stick, placing the sensor unit in the
hollow section and pressing on the unit, the stopper parts 92
providing a progressive pressure, until only the top part of
enclosure 90 covers the top part of the stick handle.
[0113] According to various exemplary embodiments, locating the
sensor unit 10 in the handle part of a stick permits maximal
user-friendliness and ease of installation, per example easy
retrofitting in existing hockey sticks, allowing any user to
install the sensor unit 10. Positioning the sensors on the stick
handle also offer protection of the sensors against shocks.
[0114] According to various exemplary embodiments, the sensor unit
10 can be connected to and communicate with other, external sensors
(not shown) which data are processed as raw data by the
microcontroller 30. Said external sensors can be positioned on the
stick or on the users' wear or equipment including per example and
without limitation helmet, shoes, skates various protective
equipment pieces, belt, wrist bands, thigh bands, arm bands,
glasses, etc. Said external sensor such as a vibration sensor, an
accelerometer, a gyroscope, temperature sensor, humidity sensor,
flexion sensor, orientation sensor (such as magnetometer), a
microphone, a camera, a video camera, a proximity sensor, any
combination, heterogeneous or homogeneous, of multiple instance of
sensors, etc. Such a sensor can be per example a bending sensor or
a flexion affixed on or moulded on the blade part or on the shaft
part of the stick to recognise mechanical bend of the appropriate
part.
[0115] Motion Data Acquisition
[0116] According to an exemplary method for motion identification,
measurements taken during an user-executed movement by the
plurality of sensor are received and the user-executed movement is
characterized based on the received measurements.
[0117] According to various exemplary embodiments, the
microcontroller 30 is configured to identify and quantify several
movement metrics, using the inertial sensor 20 located on a single
measurement point located in the handle section of a stick or on
another point maximizing the ergonomics of the product implanting
the sensor unit 10. Inertial sensor 20 is configured for
positioning near the handle part of a stick, without reduction in
spatial sensing performance compared with positioning inertial
sensors on the end section of the stick such as a hockey stick
blade or a golf club head. Such arrangement raises technical
difficulties not addressed by prior art, which require specific
motion analysis and processing. Such sensor placement, distant from
the contact point where most accurate speed measurement would be
expected, requires more processing to compensate for the indirect
measurements. For example, the algorithm requires user specific
information, such as information related to the "length", "curve"
and "flex" of the hockey stick, in order to compute accurate
speeds, accelerations and rotation speeds.
[0118] The inertial sensor 20 may provide a more efficient
execution in real-time of detection algorithms (described below)
for the analysis and quantification of complex movements. The
inertial sensor 20 may be calibrated for the quantification of
hockey movements but can be used for other sports requiring the use
of a stick in complex movements such as golf, baseball, lacrosse,
tennis, polo, badminton, etc.
[0119] The method for motion identification may be carried out
wholly on the sensor unit 10, wholly at the remote computer 100 or
partitioned between the sensor unit 10 and remote computer 100. For
example, portioning the carrying out of the method between the
sensor unit 10 and remote computer 100 may improve the amount of
local processing on the sensor unit 10, with respect to the
required data storage capacity, quantity of data to be exchanged
between the sensor and remote computer 100, communication bandwidth
and power consumption. According to the exemplary method,
characterizing the user-executed movement includes correlating the
received measurements with the sets of predetermined metrics and
detecting whether the received measurements substantially matches
one of the sets of predetermined metrics. The sets of predetermined
metrics may be stored on the memory of the sensor unit 10, at the
remote computer 100 or both. The sets of predetermined metrics may
be sets of measurements that are representative of types movements
that the user is expected to execute. For example, each set of
predetermined metrics may be associated with a movement type. For
example, the predetermined metrics may be determined during a
training stage where the different movements are performed while
being measured by a plurality of sensors as described herein. For
example, the predetermined metrics may be motion patterns.
[0120] For example, in a first "motion detection" step, the
algorithm executed by microcontroller 30 processes motion data
acquired by the inertial sensor 20 in real time to detect for the
presence of "known" motion patterns by a correlation process
involving motion patterns (defined below) stored on memory 40. For
example, new motion patterns can be "learnt" or acquired by loading
a pre-existing library of motion patterns, or by using the sensor
unit 10 in recording mode while specific motions are executed by
user.
[0121] A motion pattern includes a string of motion data along time
coordinates. Motion data refers to the raw measurement data
provided by the accelerometer group 22 (accelerations in x, y, z),
the gyroscope group 24 (angular speed variation, roll, pitch yaw)
and optionally the magnetometer 28 (magnetic field values, Bx, By,
Bz). Other sensors including without limitation spatial data,
vibration, temperature, heart beat, blood pressure, or wind speed
sensors, can be implanted in the sensor unit 10 and sensing data
from these sensor can be used as raw data. These raw data are
acquired at a sampling rate that can vary generally from several
hertz (Hz) to several hundred of hertz (Hz). The best performing
sensors can provide data over a thousand hertz (kHz) sampling
rates. The sampling rate is usually configurable for each sport or
application. Raw data is quantified on 8, 12 or 16 bits, varying
with the quality of sensors and MEMS components.
[0122] According one exemplary embodiment, the microcontroller 30
focuses during the motion detection phase on a pre-established
number of metrics that are representative of the motion patterns
associated with movements typically performed during a given sport.
A metric can be part of a motion pattern, such as a spatial
acceleration or movement along x-y or x-y-z plans during a given
time interval, or series of such accelerations or movements. Proper
configuration allows the sensor unit to focus on the reduced number
of different metrics that are representative of a motion pattern.
Processing a lower number of metrics reduces computation
requirements at the microcontroller 30, data storage requirement
and power consumption. A fast motion will require a limited volume
of motion data and small memory 40 requirements, while longer or
slower motions require a greater amount of memory.
[0123] Motion patterns and metrics may be programmed as part of the
executable source code run by the microcontroller 30. Each metric
may be specific to a motion pattern found in a sport
application.
[0124] According to various exemplary embodiments, characterizing
the user-execute movement further comprises identifying the type of
the user-executed movement. For example, this identifying may be
carried only when a match with a set of predetermined metrics has
been identified. For example, the identifying may be carried out
based on the correlation with the set of predetermined metrics used
for the detecting. Alternatively, the identifying may be carried
out based on correlation of the received measurements with at least
one additional set of predetermined metrics. For example, each of
the additional set of predetermined metrics may be associated with
a movement type.
[0125] For example, in a second "motion identification" step, the
algorithm executed by microcontroller 30 runs a gesture detection
routine that characterizes particular movements, in order to
identify the type of movements performed by user, by correlation,
among a library of typical movements associated with a given sport.
The algorithm then confirms if a motion pattern qualifies or not as
a typical movement to be quantified. During the motion
identification step, the algorithm also discriminates and
quantifies motions among motions that share same or equivalent
motion patterns. This second step is facultative as some motions
are de facto identified by the pattern recognition process of the
first phase. Per example, taking ice hockey as an illustration, a
"slap shot" movement can be precisely identified through pattern
recognition during the motion detection phase because of its
distinctive signatures involving high elevation of the stick blade,
high movement amplitude and fast accelerations, as no other hockey
motion shares equivalent characteristics. However, a "snap shot"
motion and a "passing" motion can share several metrics such as
duration, stick angle and acceleration, and may be considered as
similar motion patterns. In such case a more detailed sample
analysis is performed by the algorithm, using a larger set of
motion metrics that are pre-established for each motion pattern.
Hockey movement that can be identified by the system as motion
patterns includes notably "wrist shot", "snap shot", "slap shot",
"dribble", "assist", "passing". For some hockey movements, such as
passes and assists, the system only registers the time of movement
and number of movement occurrence; thus the algorithm increases
value of the motion counter, and return to the motion
identification step. For other hockey movements, a motion
quantification must be perform in order to compute performance
metrics with meaning to the user.
[0126] According to various exemplary embodiments, characterizing
the user-execute movement further comprises quantifying the
user-executed movement based on the received measurements.
[0127] For example, in a third "motion quantification" step,
microcontroller 30 executes a second algorithm that quantifies the
motion executed by user. The second algorithm is executed
automatically when a movement is identified as per the motion
identification step, using raw data stored on memory 40 associated
with the given movement. The second algorithm calculate quantified
movement data including, using a hockey shot as an illustration,
the power of a throw, the duration of a movement, the amplitude of
the angle of the momentum gesture. In addition, the second
algorithm computes an estimate of the maximum speed of the puck.
This estimate provides an approximation of the real speed of the
puck immediately after impact by the stick.
[0128] Once the previous three steps are completed, the
microcontroller 30 stores the computed quantified movement data and
the raw data associated with movements identified and quantified,
in memory 40 or transmit to the remote computer 100 using
telecommunication means 50, in batch or for each individual
movement. In the absence of communication link such as wireless
network, the quantified movement data is stored on memory means 40,
which is typically able to store data representative of several
thousand movements.
[0129] According to various exemplary embodiments, the method
further includes determining a speed of an object hit by a stick
being used by a user in executing the movement. For example, an
algorithm executed on microcontroller 30 can estimate the
information on the movements of a second object that is not
mechanically connected to the stick. The sensor unit can use
estimation algorithms to compute motion information of a second
object. For example, a sensor unit located on a hockey stick can
estimate the speed achieved by the puck once hit by the player. In
a possible embodiment, the sensor unit would compare the motion
metrics from a single puck hit, such as a "slap shot", to pre
recorded data that have been correlated with puck speed measurement
(using per example, a radar unit), in order to identify the most
probable puck speed. In order to yield more accurate puck speed
estimates, correlated data can include, in addition to motion and
speed, player centric variables such as physical measurement, level
of play, age, sex, and other player characteristics which typically
impacts puck speed achieved with slap shots, that otherwise share
similar motion metric when performed by different players. In
another possible embodiment, a formula is used to directly compute
puck speed estimation from pre recorded and correlated data. Said
formula is developed using known linear regression techniques
and/or known genetic algorithms. In order to increase the precision
of the estimation, said linear regression or genetic algorithm can
be computed for smaller reference groups, groups composed from
boundaries or criteria such as player age, player level, player
size and weight, etc. In all cases, the estimations assume an
accurate puck hit and correct puck trajectory. In yet another
embodiment, stick related information is considered by the
algorithm, such as length, material, weight, blade length, blade
angle and flex.
[0130] According to various exemplary embodiments, using the system
in recording mode, the sensor unit 10 records raw sensor data
provided by the inertial sensor 20 while the user executes specific
motions with the stick. Microcontroller 30 then executes algorithms
to process the recorded sensor data to identify key distinctive
elements of the motion patterns. The recorded data can
alternatively be processed by an external processing unit such as
remote computer 100 which executes same algorithm, when the
complexity of the calculation exceeds microcontroller 30 processing
capabilities. The step of identifying key elements of a motion
pattern is provided by a detection process, using key motion
markers for real-time motion pattern recognition, and a
quantification process, using formulas for performance metric
computation. A key motion marker is a measurable value, which can
be computed and identified in real-time, and which corresponds to a
specific posture, motion metric or movement in a motion sequence.
In the identification of slap shot motions, the motions "start",
"downswing start" and "impact", as illustrated in FIGS. 3A-C, are
examples of key motion markers, identified by analysing in real
time the monitoring metric. A performance metric computation
formula is an equation taking input data such as sensors' raw data
or motion metrics, like the angular speed of the stick and
acceleration at specific instants during a slap shot motion, to
provide meaningful outputs in a given sport, such as in ice hockey
the duration of a complete motion, the angle in degrees of the
backswing and the speed of the hockey stick.
[0131] As an example, the recording mode can be used to calibrate
or tune the sensor unit to a user specific motion signature. It can
also provide a means to use the sensor unit in a different sport,
as per example, from hockey to lacrosse, where motions are
different but share similarities.
[0132] Device Activation
[0133] For ergonomic enhancements, or for robustness concerns, the
sensor unit 10 can integrate an automatic start/stop mechanism,
where the "on" command is triggered by a motion, e.g. an impact of
at least 4 G in one or any dimension, while the "off" command can
simply be triggered by a timeout, per example a duration of 30
seconds without movement. Such parameters can also be configured or
turned off by user.
[0134] According to various exemplary embodiments, the plurality of
sensors are operable between a sleep state and a wake state,
wherein at least one of the sensors remains in a wake state while
the remainder of the sensors are operating in a sleep state, and
wherein detection of a motion by the at least one sensor in the
wake state triggers activating at least one of the remainder of the
sensors to the wake state.
[0135] For example, at least one sensor of either the accelerometer
group 22 or the gyroscope group 24 is kept powered in wake mode at
all time, when the sensor unit 10 is otherwise turned off, wherein
said one sensor can detect a short movement such as per example and
without limitation, a "shake" motion vertically, laterally or
rotationally, which movement will trigger a powering on of the
sensor unit 10. A shake movement is perform typically when user
gets hold of or collects a stick that was stored or in an immobile
position. Accordingly, user does not need to notify the motion
sensor unit 10 that it will launch. The sensor unit is "listening"
to all actions of the user at all time.
[0136] According to various exemplary embodiments, the device or
system further includes an identification tag reader operable to
read an identification code, such as a bar code or an
radio-frequency identification tag (RFID tag).
[0137] As an illustration of the device activation and motion
identification and quantification capabilities, a golf user would
not need to configure the motion sensor; the sensor unit 10 would
know which club is used (provided a sensor is installed in each
club or that the sensor unit is provided with a mean of identifying
specific golf clubs like barcodes or RFID tags, such as an infrared
bar code reader or a radio transceiver, in which case the sensor
unit can be affixed on the exterior surface of the stick for
convenience, or wear by the player, typically on a glove or on a
wrist), and exactly when user executes a drive (notably in
detecting the vibrations and impact associated with the club
impacting the ball).
[0138] Improvements provided by various exemplary embodiments
described herein may include (i) faster data acquisition by the use
of an inertial sensor 20 comprising plurality of sensors of high
and low sensitivity and high sampling rate, including
accelerometers 22, gyroscopes 24, magnetometers 28 and other
sensors; (ii) acquisition of higher quality data by the
identification of non-choreographed movements using algorithms
executed by microcontroller 30 that identifies and validates motion
metrics by comparing raw data from inertial sensor 20 to a bank of
motion metrics on memory 40; (iii) richness of the motion data
captured and quantified, by the use of algorithms executed by
microcontroller 30 to quantify identified motion metrics and to
provide usable information such as movement speed, ball or puck
speed estimate, duration of movement, number of movements, etc.
(iv), better ergonomic by positioning the sensor unit in the hollow
section of the stick, where the sensor unit is not obstructing any
user movement and is protected against shocks; (v) simplicity of
installation by the user by simply sliding the sensor unit inside
the hollow section of the stick; (vi) continuous operation where
the sensor unit can operate at all time and active itself
automatically without any operation other that a simple movement
such as lifting the stick; (vii) richness of the motion
quantification experience, by enabling the sensor unit to
communicate with an external device where quantified motion
information can be presented, communicated, compared and exchanged,
notably using applications localised on hand held devices and using
social networks.
[0139] Referring now to FIGS. 3A-B-C, examples of motion patterns
associated with a hockey slap shot or snap shots are provided and
exemplified through graphs where duration or timeline is
represented in the x axis and spatial movement or acceleration is
represented on the y axis. Said graphs are rendition of the
combined raw data provided by the inertial sensor 20 and
interpreted by the algorithms executed on microcontroller 30.
[0140] As represented by FIG. 3A, a shot motion is identified
comprising the following motion patterns successively: "initial
position", "backswing", "downswing", "impact with the puck", follow
"through" and back to an "initial" or neutral position. The motion
detection starts with a backswing of high amplitude identified as
event no 1, as shown on the graph at time 6.65, followed by a sharp
downswing movement starting in event 3 culminating in a impact with
the puck as event no 5 and by movement follow thorough at event no
7, after which the system is idle, until a new event is detected as
illustrated by the second no 1 event at time 6.87+. For example,
the method is carried out by detecting and/or identifying a slap
shot movement by the value of motion metrics typically
characteristic of a slap shot associated with a backswing of events
no 1, followed by a backswing delay identified as lapse no 4, a
downswing start detection of event 3, a downswing delay identified
as lapse no 7, and impact of event no 5, the full duration of the
quantified movement is identified as lapse no 2 and the moving
average is provided as event no 6. The moving average is an average
made on a sliding window of a specific number of samples. For every
new sample, the average is recalculated with the new sample, while
dropping the last one of the window. Such moving average is used to
confirm a strong trend within highly dynamic data stream.
[0141] As represented by FIG. 3B, a hockey shot is often
characterized by an impact with ice and the puck successively, as
shown by the three peaks provided by the monitoring metric graphs,
said peaks being associated with the "ice contact", "puck contact"
and "release" movements and constitutes motion metrics identified
and quantified. Using a stick with a certain flex coefficient, the
stick will accumulated energy during the ice contact and puck
contact movement, referred to as "stick loading" and is
characterized notably by the duration, distance and measurement
period.
[0142] For example, the sensor unit can estimate a ball or puck
speed following a swing movement based on raw data provided by the
inertial sensor using motion metrics from a group including
movements and values such as "swing amplitude", "swing duration",
"stick speed", "stick acceleration", "wrist effect", "detailed flex
analysis", "motion amplitude" and "motion behaviour". The precision
of said puck speed estimate can be increased by the specification
of user centric data using the application located on the remote
computer and transmitted to the microcontroller 30 using the
telecommunications means 50, such user centric data including
without limitation, "user age", "user gender", "user location",
"user height", "user weight", "stick length", "stick model", "stick
flex".
[0143] As represented by FIG. 3C, a hockey shot is often
characterized by motion metrics such as pre impact downswing and
post impact downswing, before a return of the stick to an initial
or idle position.
[0144] According to various exemplary embodiments the user-executed
movement is characterized by detecting a motion starting event when
a first subset of the received measurements substantially
corresponds to one of a plurality of sets of predetermined starting
event metrics. Each set of predetermined starting event metrics may
be further associated with one or more event continuation metrics.
Then the presence of a motion continuation event is determined. For
example, there is presence of a motion continuation event when a
second subset of the received measurements received after the first
subset substantially corresponds to one of the one or more event
continuation metrics associated to said one of the plurality of
sets of predetermined starting event metrics. The sets of event
continuation metrics may be further associated with one or more
event completion metrics. The presence of a motion completion may
be further determined when a third subset of the received
measurements received after the second subset substantially
corresponds to one of the one or more event completion metrics
associated to the one of the event continuation metrics.
[0145] For example, in the context of a hockey movement, the motion
starting motion starting event is a backswing of a stick, the
motion continuation event is a downswing of a stick and the motion
completion event is an impact of the stick with an object.
Accordingly, detecting the motion starting event includes detecting
from the received measurements a motion pattern associated with the
backswing of the stick. Determining the presence of the motion
continuation event comprises monitoring received acceleration
measurements and detecting that the received acceleration
measurements exceeds a predetermined acceleration threshold
associated to the downswing of the stick. Furthermore, determining
the presence of a motion completion event includes monitoring over
an impact detection period received measurements and detecting that
the received one or more measurements exceeds an impact
threshold.
[0146] The method of movement quantification step according to one
example is performed by algorithms executed by the microcontroller
shown graphically in the flowcharts of FIGS. 4A-B, as is
illustrated by the detection, and identification of a shot
movement. The "A" bubble indicates a continuation between FIGS. 4A
and 4B.
[0147] In a first step "Activation Trigger" 210, the sensor unit is
automatically activated by a movement or user action on the stick,
such as an acceleration or rotation.
[0148] In a second step "Shot Start Detection Process" 212,
following activation, the sensor unit initializes metric values and
starts the real-time monitoring and analysis of metrics. The sensor
unit uses as inputs raw data from the inertial sensor, principally
acceleration values Ax, Ay, Az provided by the accelerometer and
angular rate values Mx, My and Mz provided by the gyroscopes, and
provides a metric value expressed in m*rad/s.sup.3 as outputs. Such
value illustrate a rotational speed multiplied by a linear
acceleration, hence the meter*radian numerator and the second 3
denominator. The system returns to an idle status "B" 205, when the
movement stops before qualifying for a third step event.
[0149] A third step "LMIN1" 214, by which the system logs as a
"local minimum #1" event referring to the start of a potential shot
motion, is triggered when the algorithm detects and identify a
motion pattern associated to a "Backswing Motion Start".
[0150] A fourth step "LMAX1" 216, by which the system logs as a
"local maximum #1" event referring to a potential backswing motion
acceleration, is triggered when the algorithm detects and
identifies a motion pattern that is greater in value than a
"backswing low threshold" and lower in value than a "backswing high
threshold". The system loops in this fourth step for as long as the
delay between the detection of LMIN1 and the current time is lower
than the Backswing Delay (BD). If such delay is reached, the system
returns to an idle status 205, when the movement identifies in step
214 fails to qualify as a "local maximum #1" event.
[0151] A fifth step "LMIN2" 218, by which the system logs as a
"local minimum #2" event referring to a potential shot motion
continuation, is triggered when the algorithm detects and
identifies (i) a motion pattern associated to a potential
"Downswing Motion Start" or to (ii) a motion associated with a
potential "Backswing motion start" in which case the motion
replaces the previously logged "local minimum #1". The system loops
in this fourth step for as long as the delay between the detection
of LMIN1 and the current time is lower than the Backswing Delay
(BD). If such delay is reached, the system returns to an idle
status 205, when the movement identifies in step 216 fails to
qualify as a "local minimum #2" event.
[0152] In a sixth step "Impact Detection Metric" 220, by which the
system monitors the variation of the Monitoring Metric over an
impact detection period, and log the event as a potential impact
when the variation reaches a specific level, i.e. the "Impact
Threshold" a "Downswing Motion Start". The impact must follow a
Downswing motion within a specified time period, i.e. the
"Downswing Delay" or "DD" to be considered for the next step. If
the impact is not registered during the specified Downswing Delay,
the system returns to idle mode at step 205.
[0153] In a seventh step "Shot Detection" 222, the first algorithm
confirms an impact event when the Moving Average of the Monitoring
Metric reaches a specific level, i.e. the Shot Threshold, and the
second algorithms quantifies the motion metrics associated with the
event and computes shot statistics from the Low Minimum #2 event to
the impact.
[0154] Various exemplary embodiments described herein rely on the
concept of "software sensor", or "Software Defined Sensor" (SDS).
The SDS can be reprogrammed to enable the identification and
quantification of wide range of movements and gestures. As SDS is
designed to connect to a computer or a smart phone via a wireless
Bluetooth link, it can be reprogrammed remotely by a simple update
of the client application. For example, a SDS based firmware
implemented on a mobile application located on a handheld device
can allow the identification and quantification of "slap shots" and
"short shots", in relation to hockey. Update or upgrade of the
mobile application can be downloaded from a central computer using
internet or a communication network, such newer version of the
mobile application may include a SDS allowing, for example,
identification and quantification of "wrist shots" and of the
amplitude level of the end of the movement (to estimate the speed
reached by the puck after impact). The SDS platform for data
acquisition can be adapted easily allowing the algorithms to
identify and quantify motion metrics associated with any sport. The
sensor unit 10 is typically used in recording mode to record the
raw motion data associated with every pertinent motion of such new
sport. The collected raw data is then processed to identify the key
features of targeted motions. The result is a new detection process
and a new quantification process that can be uploaded in the SDS.
The SDS can therefrom detect and quantify new motions.
[0155] Referring now to FIG. 5, sensor unit 10 is connected to a
remote computer 100 such as a smart phone with a processor, memory,
database and associated software for configuring the sensor unit,
storing data and for organizing, presenting, communicating,
comparing and exchanging quantified motion information and user
information representative of its profile and performance.
[0156] According to one exemplary embodiment, the sensor unit 10
synchronizes with a remote computer 100 such as a smart phone for
updating data and usage statistics. The software application
running on the smart phone synchronises to a localised database on
a memory means connected to the smart phone, or to a remote
database on an external computer 104 using a wired or wireless
connection of the smart phone (cellular, WiFi, Bluetooth). This
synchronization will allow the user to keep a longer history of
statistics, but also to access performance data and other
quantified motion data of members of a wide user community. A
player performance data can be then compared to external similar
data representative of other players' performance. Quantified
motion data can be then presented via ergonomic, intuitive and
engaging interfaces.
[0157] According to one exemplary embodiment, the remote computer
100 connects to an online platform 106 such as a social network
that is accessible from a smart phone or an external computer, for
the purpose of communicating, comparing and exchanging quantified
motion information and user information representative of its
profile and performance. The sensor unit 10 device can also be
connected to interactive applications on external computers 104 or
hand held device 105 such as smart phones, that allow users,
trainers and coaches to analyze, present, share and compare
quantified motion information and player performance data.
[0158] Referring now to FIGS. 6A-B-C-D, a hand held device 102
comprising the processor(s), memory, communication means, display
and graphical interfaces necessary to execute mobile software
applications for the presentation of user profiles, performance
statistics, communication and comparison of quantified motion data
and user information for a single users and several users.
[0159] FIG. 6A provides an example of motion analysis interface
presenting ice hockey "shots" motion events metric, for each
distinct motion metric, such as a "Slapshot", as is shown in the
central window of the hand held device interface. The motion
analysis interface provides quantified motion data with respect to
a shot motion event, such as "shot speed", "duration", "angle",
estimation of "puck speed", "acceleration" and "rotation
speed".
[0160] Interfaces example shown in FIGS. 6A and 6B provides common
features such as the possibility of viewing in "Live" mode, "Shots"
mode or "Events" mode. In Live mode, quantified motion metrics and
events metrics are presented in real time, as illustrated in FIG.
6A. In Shots mode, quantified motion metrics and events metrics are
represented by lists of "shots" events of a similar category, as
classified according to one criteria among a group of criteria
comprising "date", "speed", "duration", "puck speed", "stick
speed", "translational acceleration" and "rotation speed". In Event
mode, quantified motion metrics and events metrics are represented
by lists of "shots events of all categories presented together, and
classified according to one criteria among a group of criteria
comprising "date", "speed", "duration", "puck speed", and
exemplifying in the top windows, the latest or best performing
events of a one or two categories of events such as "SnapShot/Pass"
and "Slapshot" categories, where the top performing event and
average performance are represented, as is illustrated by the
interface example of FIG. 6B.
[0161] User information and user performance information can be
presented using an interface on the dedicated mobile application,
as is illustrated by the interface example of FIG. 6C. Said user
related information comprises without limitation, information such
as name, address, team, position and player's number, number of
events in the system, number of followers from social medias and
number of other users follow by the user. Said user performance
information includes "Recent events" list of events including
events statistics such as speed; for each event categories such as
"Snapshot/pass" and "Slapshot", the total events (shots) and game
performance (number of "goals" or "assist" over number of shot
events), the top performing events defined per example by the top
speed or another criteria, along with the average performance of
all user events of same category.
[0162] Other users of the mobile application have the capabilities
to "Follow" a user, to "Like" an event or a "Comment" an event or
posting. Any user information or performance information can be
shared on an online platform or social media such as Facebook,
using a user Facebook account linked to the mobile application on
user's remote computer or hand held device connected to a sensor
unit 10.
[0163] The mobile application provides a "Leaderboard" listing
comparing user's performance (virtual sport card) using user's
events gathered during game or practice, as illustrated by the
example of interface of FIG. 6D. The Leaderboard interface provides
a listing of users performance information based on at least one
criteria such as "number" of event or "top speed", for a given
event category such as "Slapshots" or "Snapshots/pass" category,
during a given period such as "Last 30 days" period.
[0164] According to one exemplary embodiment, an inertial sensor
unit and a mobile application connected to an online platform such
as a social network, enable users to monitor their sport
performance by identifying and quantifying performance events,
which event information and statistics can be presented, shared,
compared and commented using social medias. Per example, each
"slapshot" made by a ice hockey user will receive a score based on
speed; each user will receive a score based on the quality and
power of its "Slapshot" score launch; this score will rank players
from around the world and stimulate healthy competition. Various
exemplary embodiments described herein may advantageously provide a
motion tracking and quantification system, device and method
optimized for complex gestures in a non choreographed motion
sequence.
[0165] Various exemplary embodiments described herein may
advantageously provide motion tracking system based on inertial
sensor offering a wide dynamic range enabling the concurrent
capture of high and low acceleration movements at high resolution
that is optimized for complex gesture tracking.
[0166] Various exemplary embodiments described herein may
advantageously provide a motion tracking system which can detect,
discriminate and quantify complex gestures automatically among a
plurality of heterogeneous non-choreographed movements.
[0167] Various exemplary embodiments described herein may
advantageously a motion tracking system which can operate without
the need for a specific or prior instruction or action by the user,
that recognizes movements automatically and which can be used
seamlessly and continually over a long period of time without the
need for external systems.
[0168] Further, Various exemplary embodiments described herein may
advantageously provide a motion tracking system of solid
construction that can withstand the mechanical stress imposed on a
sport instrument such as a hockey stick, while not affecting the
behaviour of the sport instrument nor hindering user's movement in
any way, while being easy to install, to calibrate and to operate
with a minimum of operations and while providing a high accuracy of
movement quantification.
[0169] Improvements provided by various exemplary embodiments
described herein may include (i) faster data acquisition by the use
of an inertial sensor comprising plurality of sensors of high and
low sensitivity and high sampling rate, including accelerometers,
gyroscopes, magnetometers and other sensors; (ii) acquisition of
higher quality data by the identification of non-choreographed
movements using algorithms executed by microcontroller that
identifies and validates motion metrics by comparing raw data from
inertial sensor to a bank of motion metrics on memory; (iii)
richness of the motion data captured and quantified, by the use of
algorithms executed by microcontroller to quantify identified
motion metrics and to provide usable information such as movement
speed, ball or puck speed estimate, duration of movement, number of
movements, etc. (iv), better ergonomic by positioning the sensor
unit in the hollow section of the stick, where the sensor unit is
not obstructing any user movement and is protected against shocks;
(v) simplicity of installation by the user by simply sliding the
sensor unit inside the hollow section of the stick; (vi) continuous
operation where the sensor unit can operate at all time and active
itself automatically without any operation other that a simple
movement such as lifting the stick; (vii) richness of the motion
quantification experience, by enabling the sensor unit to
communicate with an external device where quantified motion
information can be presented, communicated, compared and exchanged,
notably using applications localised on hand held devices and using
social networks. The person skilled in the art would understand
that the various properties or features presented in a given
embodiment can be added and/or used, when applicable, to any other
embodiment covered by the general scope of the present
disclosure.
[0170] The present disclosure has been described with regard to
specific examples. The description was intended to help the
understanding of the disclosure, rather than to limit its scope. It
will be apparent to one skilled in the art that various
modifications can be made to the disclosure without departing from
the scope of the disclosure as described herein, and such
modifications are intended to be covered by the present
document.
* * * * *