Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
  • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
  • A61B5/002—Monitoring the patient using a local or closed circuit, e.g. in a room or building
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
  • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
  • A61B5/1123—Discriminating type of movement, e.g. walking or running
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/24—Querying
  • G06F16/245—Query processing
  • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
  • G06F16/2462—Approximate or statistical queries
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F8/00—Arrangements for software engineering
  • G06F8/60—Software deployment
  • G06F8/65—Updates
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/20—Movements or behaviour, e.g. gesture recognition
  • G06V40/23—Recognition of whole body movements, e.g. for sport training
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L67/00—Network arrangements or protocols for supporting network services or applications
  • H04L67/01—Protocols
  • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
  • H04W4/30—Services specially adapted for particular environments, situations or purposes
  • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
  • H04W4/50—Service provisioning or reconfiguring
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
  • A61B2503/10—Athletes
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
  • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
  • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing

Definitions

• the present invention relates to the field of motion recognition implemented by data sensors.
• the invention finds a privileged application in the field of sport to recognize the movements made by an athlete in different disciplines, such as golf, tennis, or skiing.
• Multisports for evaluating the performance of an athlete during his sport, these sensors can be configured according to the sport practiced.
• a data sensor attached to an athlete's wrist should be used to identify how many strokes or backhands were made by that athlete. For this, a reliable recognition of the movements made by the athlete is necessary.
• raw data such as inertial quantities measured by accelerometers and / or gyrometers embedded in multisport data sensors.
• These data are processed directly by the data sensor by means of a dedicated application implementing motion recognition algorithms, these algorithms being designed to recognize the nature or type of movements performed during a sporting practice.
• the dedicated application analyzes the raw data provided by the sensors and produces so-called reduced data which contain the recognized movement and possibly associated measurements.
• the reduced data may contain the information that the recognized movement is a forehand to tennis associated with the speed of the racket at the moment of the typing.
• the recognition of movements by the algorithms implemented by the dedicated application is not optimal. This may come from physical specifics or from the practice of the user for example.
• the present invention aims to remedy the aforementioned drawbacks, by proposing a technical solution to improve the recognition of movements implemented by data sensors, for a wide variety of sports practices.
• a data processing method intended to improve the motion recognition implemented by an application associated with at least one data sensor, said method comprising the following steps:
• the analysis, in particular statistics, raw data from a plurality of sensors used by different users is particularly advantageous for improving the motion recognition performance of a generic algorithm of the application, particularly when such an algorithm is implemented by a large number of sensors.
• the use of statistical analysis methods is all the more advantageous as the number of sensors for feeding the remote database is high.
• the analysis, in particular statistics, raw data from the same sensor is also advantageous for improving the performance of a motion recognition algorithm of the application specifically to this sensor, the algorithm can be Customized based on the raw data provided by this sensor.
• the recognition of movements of an athlete can to be improved.
• the result of the analysis is used to parameterize or update the motion recognition algorithm (s) centrally. This is all the more advantageous as the number of deployed sensors is high.
• the motion recognition applications implemented on the data sensors can be updated or configured centrally, from the raw data obtained from a set of sensors.
• the method further comprises a preliminary test step, during which a criterion of reliability of motion recognition is evaluated, since a movement is recognized by the application, of so that the other steps of the method are initiated according to the value of the reliability criterion with respect to a predetermined threshold.
• the steps of the method according to the invention are executed only in the case where the motion recognition performed by the data sensor has an insufficient degree of reliability.
• the conditional execution of the steps of the method according to the invention is particularly advantageous to avoid unnecessary processing and preserve the electrical resources of the data sensor whose autonomy must be optimized.
• the fact that it is sent on an ad hoc basis is particularly advantageous for limiting the amount of information to be transmitted through the radio communication networks, given the limited transmission capacities of the data carriers.
• an identifier qualifying the type of movement performed by a user of said at least one sensor is associated with said raw data during the collection step.
• the identifier is entered, manually by the user or automatically, according to a sequence of movements to be made predefined, the identifier and the data being transmitted to said database.
• the identifier advantageously makes it possible to classify and group the raw data of the same nature (for example, corresponding to the practice of the same sport under similar conditions) in the remote database.
• the collected raw data are sent by said at least one data sensor to a mobile terminal, this terminal being adapted to transmit said data to the remote database.
• a mobile terminal such as a mobile phone
• transfer raw data from the data sensor to the remote database is particularly well suited in the case where the data sensors do not have means to communicate. with mobile radio networks.
• the raw data collected is transmitted to the remote database by the mobile phone using the subscription linked to this phone.
• the mobile phone uses simplified architecture sensors, whose energy consumption is reduced.
• the raw data comprises acceleration and / or speed inertial data.
• the inertial data is not only used by the motion recognition algorithm but also exploited during the analysis step to adapt the application of the data sensor, in order to improve the recognition capabilities.
• the raw data further comprises at least one of the following parameters: data sensor orientation, ambient pressure, ambient temperature.
• These additional parameters are indicators of the physical conditions in which the data sensor was used during the collection of inertial data. Such indicators may be taken into consideration when analyzing the raw data collected.
• the method further comprises a merging step, during which the raw data collected is merged, so as to provide a calibrated, filtered and more accurate data structure than if considered in isolation.
• the gyro biases are suppressed by calculating a sliding average in the static phases of the data sensor or are removed by more powerful algorithms such as those described by Kalman-Bucy. and known as "Kalman Filters".
• Kalman Filters the data of the gyrometer and the accelerometer are combined, that is to say, merged to achieve greater precision on the calculation of the attitude (orientation in space) of the data sensor. . This merger can be done by Kalman filtering or by using a complementary filter.
• the function of the merging of the collected raw data is to calibrate the quantities measured during data collection, for example, with respect to a common reference value and / or to convert these data so as to express them according to a system of measurement.
• conventional units directly interpretable during the analysis step.
• the calibration makes it possible to compensate for static and dynamic defects by combining complementary data to improve the overall accuracy.
• the parameterization step includes supplying said at least one data sensor with at least one configuration parameter determined according to the results of the analysis.
• the configuration parameter (s) provided to the data sensor (s) are used to customize the recognition algorithm implemented by the application of this data sensor (s).
• the centralized provision of these configuration parameters is particularly advantageous for simultaneously customizing a plurality of deployed sensors, without having to intervene individually on each of the sensors.
• the update step includes providing at least one sensor of an updated application based on the results of the analysis.
• a centralized update of the applications of the sensors can be carried out efficiently.
• This is particularly advantageous for generalizing a recognition algorithm (i.e. improving a generic algorithm) on the basis of raw, preferably merged, data obtained from a large number of data sensors.
• the invention also relates to a data processing method intended to improve the recognition of movements implemented by an application associated with at least one data sensor. Said method comprises the following steps implemented by said at least one data sensor:
• the invention also relates to a data processing method intended to improve the recognition of movements implemented by an application associated with at least one data sensor, said method comprising the following steps implemented by a data server:
• the invention also aims at a data processing system intended to improve the recognition of movements implemented by an application associated with at least one data sensor, said system comprising:
• the system further comprises a mobile terminal adapted to transmit to the remote database collected raw data received from said at least one sensor.
• the invention also relates to a data sensor for improving the recognition of movements, said sensor comprising:
• the invention also aims at a data server intended to improve the recognition of movements implemented by an application associated with at least one data sensor, said server comprising:
• the server is adapted to implement at least one motion recognition algorithm according to an algorithm implemented on said at least one data sensor to be parameterized or updated.
• the algorithm is executed by the server using all or part of the raw data stored in the database.
• the invention also relates to a computer program comprising instructions adapted to the implementation of any of the steps of the methods according to the invention as described above, when said program is executed on a computer.
• This program can use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other form desirable shape.
• the invention also relates to an information storage medium, removable or not, partially or completely readable by a computer or a microprocessor comprising code instructions of a computer program for the execution of any one of the steps methods according to the invention as described above.
• the information carrier may be any entity or device capable of storing the program.
• the medium may comprise storage means, such as a ROM (Read Only Memory), for example a microcircuit ROM, or a magnetic recording means, for example a hard disk, or a memory flash.
• ROM Read Only Memory
• the medium may comprise storage means, such as a ROM (Read Only Memory), for example a microcircuit ROM, or a magnetic recording means, for example a hard disk, or a memory flash.
• the information medium may be a transmissible medium, such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means.
• the program according to the invention may in particular be downloaded to a storage platform of an Internet type network.
• the information medium may be an integrated circuit, in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the method in question.
• Figure 1 illustrates the architecture of a system according to a first embodiment of the invention
• Figure 2 illustrates the architecture of a system according to a second embodiment of the invention
• Figure 3 illustrates an exemplary embodiment of the system according to the first embodiment of the invention comprising a plurality of sensors
• Figure 4 illustrates the steps of the method according to a particular embodiment of the invention
• Figure 5a illustrates an example of representation of two point clouds corresponding respectively to two gestures by illustrating the Hamming distance
• Figure 5b illustrates an example of partitioning to allow motion recognition.
• One of the ideas underlying the invention is to parameterize and / or update the application of one or more data sensors to improve the reliability of motion recognition, depending on the results of an analysis, preferably statistical, raw data stored in a remote database, these data being collected from at least one data sensor.
• the system according to the invention comprises at least one data sensor used by one or more users, a server connected to the remote database.
• the server is adapted to analyze the raw data contained in the database.
• the collected data can be sent to the server via a mobile terminal connected to the data sensor.
• the data sensor is adapted to transmit the collected data directly to the data server.
• the raw data stored in the database is associated with an identifier for qualifying the type of movement made during the collection of corresponding raw data.
• Each set of raw data collected in association with the predefined motion identifier constitutes a complete block of data, all the blocks of data stored in the database that can be exploited by statistical preference analysis methods.
• the results of the raw data analysis are used to determine configuration parameters for improving the configuration of the motion recognition algorithm implemented by the data sensor application.
• the invention advantageously allows a reconfiguration of the application of each data sensor, taking into account all or part of the raw data stored in the remote database.
• the results of the raw data analysis can also be used to develop an update of the application of the data sensor, on which the motion recognition algorithm is implemented.
• the invention makes it possible to update the application of one or more data sensors, taking into account the all or part of the raw data stored in the remote database obtained from the sensors.
• the configuration and / or update of the application of one or more data sensors are performed centrally, by means of the server, taking into account all or part of the raw data obtained from a sensor or a plurality of sensors.
• the invention makes it possible to update and / or configure a generic motion recognition algorithm, this algorithm being able to be common to a plurality of sensors regularly supplying the remote database.
• the purpose of the algorithms deployed on all the sensors is not only to recognize a given movement and categorize it (for example, forehand tennis) in order to create statistics communicated to the user, but also to measure with the greatest possible accuracy a characteristic parameter that will also be communicated to the user, such as the speed of a movement or the height of a jump.
• Figure 1 schematically illustrates the architecture of a system according to a first embodiment of the invention.
• the system includes:
• a mobile terminal 3 constituted, for example, by a mobile phone, - a server 5 and a remote database 7 in a telecommunications network 9.
• the telecommunications network 9 comprises the Internet network in which the server 5 and the remote database 7 appear.
• the mobile terminal 3 is equipped with 3 G or 4 G radio-mobile communications means adapted to access to the Internet according to IP communication protocols.
• the data sensor 1 comprises measurement means Mi, ..., M n for measuring inertial variables, such as speed and / or acceleration, these measuring means being integrated in the data sensor 1.
• the data sensor 1 comprises three measuring means as follows: first measuring means Mi adapted to measure an instantaneous angular velocity, this velocity being represented by a vector ⁇ having three rotational speed components co x , co y , co z around each of the axes of said first measuring means Mi constituted by a gyrometer embedded in said data sensor;
• second measuring means M 2 adapted to measure a local linear acceleration represented by a vector ⁇ having three linear acceleration components ⁇ ⁇ , y y , ⁇ ⁇ along each of the axes of said second measuring means M 2 , these means being constituted by a linear accelerometer embedded in said data sensor;
• third measurement means M3 adapted to calculate a universal acceleration ⁇ (Linear World Acceleration) from the accelerations measured by one or more accelerometers and rotational speeds measured by one or more gyrometers, the universal acceleration being calculated according to known algorithms those skilled in the art (eg rotation matrices using Euler angles or quaternions, data fusion using complementary filters, Kalman filtering), these means creating what can be called "universal accelerometer”.
• ⁇ Linear World Acceleration
• the data sensor 1 may further comprise additional measuring means, such as a pressure sensor and / or a temperature sensor.
• the data sensor 1 further comprises:
• a central processing unit 12 comprising a microprocessor, in particular of ARM type such as Cortex-M4 or other;
• RAM Random Access Memory
• ROM Read Only Memory
• a communication interface 15 for example of the short-range radio frequency type of Bluetooth® type.
• the read-only memory 14 constitutes a recording medium which stores a computer program PG1 conforming to the invention, able to implement, when executed by the central unit 12, the steps of the data processing method performed by the data sensor in accordance with the invention, as illustrated in FIG. 4.
• the mobile phone 3 typically includes:
• COM1 Bluetooth® type short-range radio communication interface
• COM2 type 3 G or 4G radio-mobile communication interface 36
• a central processing unit 32 such as a microprocessor
• HMI human-machine interface 37
• the ROM 34 constitutes a recording medium which stores a computer program PG3 according to the invention, able to implement, when executed by the central unit. 32, the steps of the data processing method performed by the mobile terminal 3 according to the invention, as illustrated in FIG. 4.
• the server 5 is connected to the database 7 on the telecommunications network 9.
• the server 5 typically comprises:
• a communication interface 55 adapted to communicate with the mobile radio communication interface 36 (COM2) of the mobile phone 3;
• a central processing unit 52 such as a microprocessor.
• the read-only memory 54 constitutes a recording medium which stores a computer program PG5 according to the invention, able to implement, when executed by the central unit 52, the steps of the data processing method performed by the server 5 according to the invention, as illustrated in FIG. 4.
• FIG. 2 schematically illustrates the architecture of a system according to a second embodiment of the invention.
• This second embodiment differs essentially from the first embodiment described with reference to FIG. 1, in that the data sensor 1 'is adapted to communicate directly with the server 5. In this case, it is not necessary to have a mobile phone 3, which is the case for autonomous multifunction data sensors.
• the data sensor 1 comprises, for example, a type 3G, 4G and / or wireless type Wi-Fi® communication interface 15' (COM) adapted to communicate directly with the server. 5.
• COM wireless type Wi-Fi® communication interface 15'
• the data sensor further comprises a human-machine interface 17 (IMH) comprising a display device.
• IH human-machine interface 17
• FIG. 3 illustrates an embodiment of the system according to the first embodiment of the invention, in which a plurality of data sensors 1.1, 1.2a, 1.2b,..., 1n are adapted to feed the same remote database 7 via mobile phones 3.1, 3.2, ..., 3.n with which they are respectively associated. It will be noted that several sensors 1.2a, 1.2b can be associated with the same mobile phone 3.2.
• FIG. 4 illustrates an exemplary embodiment of the method according to the invention as implemented by the system illustrated in FIG. 1 according to the first embodiment.
• the method will be described with reference to a single data sensor, but will remain valid for each of the plurality of sensors of the system shown in FIG. 3, where multiple sensors may simultaneously or sequentially transmit raw data to the remote database 7.
• the server 5 and the database 7 are sized appropriately according to the number of sensors and / or mobile phones that can feed the database 7.
• the data sensor 1 is associated with the mobile phone 3 by means of the application PG3 executed on the telephone 3, during a prior association step Eo (pairing mechanism in English).
• the user can select from the application PG3 the type of sport practiced by means of the human-machine interface 37. This information is entered in the sensor 1 which can be configure according to the selected sport.
• the data sensor 1 executes the application PG1 intended to recognize, in real time, movements made by an athlete in action.
• the processor 12 of the data sensor 1 executes a predefined motion recognition algorithm in the application PG1, during a recognition step Ei.
• these raw data comprise the angular velocity values ⁇ measured by the gyrometer Mi, the values of local linear acceleration ⁇ measured by the local linear accelerometer M 2 and the universal linear acceleration values ⁇ provided by the universal linear accelerometer M 3 .
• the application PG1 implements a motion recognition algorithm, this algorithm being predefined during commissioning of the sensor using standard parameters.
• this algorithm can be adapted or updated based on a statistical analysis of the raw data provided by one or more sensors as described below.
• the application PG1 After each iteration of the recognition step Ei, the application PG1 verifies the reliability of this recognition, according to at least one reliability criterion, during a first optional test step E 2 . For this purpose, the application PG1 compares the value of said at least one recognition reliability criterion with respect to a predetermined reference threshold (reliability condition).
• the reliability criterion or indicator used is the Hamming distance.
• the first test step E 2 consists of calculating the Hamming distance between the motion pattern recognized at a given instant and two neighboring patterns previously recognized at the end of the recognition step. If the calculated distance is greater than a predetermined reference threshold, the reliability condition is fulfilled and in this case, the data sensor continues the recognition Ei in real time of the movements made by the sportsman, in his nominal mode of operation.
• a collection step E 4 of the raw data is automatically initiated by the data sensor 1.
• the raw data collection step will be triggered provided that the Hamming distance is less than the reference threshold for more than 70% of the movements detected during a training session.
• the rate of occurrence of this condition is set to 70%. This value can be defined on the basis of previously collected data and analysis of the statistical distribution of the population studied (used to build the motion recognition algorithms).
• the recognition information obtained at the end of the recognition step Ei are reduced data ⁇ e.g. reverses, speed, effect) that can be transmitted to the application PG3 executed by the mobile phone 3 to indicate to the user the recognized movements and their characteristics, by means of the human-machine interface 37.
• the reduced information can not be exploited to improve the motion recognition algorithms because the reduced data is the result of this recognition.
• the collection step E 4 is initiated according to the profile of the user (sportsman) and / or his sports performance, after a second test step E 3 .
• the collection step E 4 can be initiated if at least one of the following conditions is met:
• the user belongs to a predetermined category of athletes (e.g. professional);
• the Hamming distance is too often below a threshold.
• Such conditions are verified by the application PG1 of the data sensor 1 during the second test step E 3 , as a function of profile information and / or performance of the user, all or part of this information that can be provided by the PG3 application executed on the mobile phone 3.
• the application PG3 presents to it by means of the human-machine interface 37 a predetermined sequence of movements to be performed during the data collection step E 4 .
• the PG3 application will provide the user with the instructions to perform the following sequence of movements: five backs, five forehands and three services.
• the collected data can be associated with predetermined movements according to the proposed sequence.
• This sequence comprises a list L of identifiers qualifying the type of movements to be performed.
• this list L is pre-registered in the application PG1 of the sensor 1. It can also be transmitted to the sensor by the application PG3 of the mobile phone 3 when a sport has been selected on the PG3 application.
• an identifier I specifying the type of movement to be performed is automatically associated with the raw data collected according to the motion sequence presented to the user.
• a first identifier Ii denoting, for example, a tennis lapel is associated with the raw data Dl. l and D 1.2a obtained from the data sensors 1.1 and 1.2a following the execution of a setback by each of the two sportsmen.
• This data is transmitted to the server 5 in association with the first identifier Ii by mobile phones 3.1 and 3.2 respectively.
• a second identifier I 2 denoting, for example, a tennis forehand is associated with the raw data D1.2b and Dl .n provided by the data sensors 1.2b and l .n, these data being transmitted to the server 5 by mobile phones 3.2 and 3.n respectively.
• the data Dl. l and D1.2a associated with the first identifier Ii and the data D1.2b and Dl .n associated with the second identifier I2 are stored in the database 7.
• the raw data collected is manually qualified by the user, for example, when the latter selects in the application PG3 a movement from a predefined list of movements before to perform this movement during the collection step E 4 .
• This qualification has the effect of associating the identifier I of the movement made with the raw data collected during the realization of this movement.
• the sequence of movements is presented by means of the human-machine interface 17 included in the data sensor.
• these data are recorded by the data sensor, for example in the form of a local file.
• the local file contains all the data measured by the gyrometer Mi, the local linear accelerometer M 2 , the universal linear accelerometer M 3 and possibly pressure and temperature data obtained by pressure sensors. and temperature during the collection step E 4 .
• Collected raw data is packaged during a packaging step
• the packaging of the data consists in converting the measured data into a raw data structure calibrated with respect to reference values and expressed according to units that can be interpreted for example by a human or by a statistical processing algorithm.
• the packaged data constitutes a raw data structure comprising:
• angular velocity data Di measured by the gyrometer Mi and expressed along three orthogonal axes in a coordinate system linked to the data sensor 1;
• the packaged data may also include: a special data structure called "Quaternion" describing the orientation of the data sensor in the space; and or
• This packaged data constitutes a data structure comprising inertial data and any raw pressure and temperature data, all of which data are calibrated with respect to reference values for each of the quantities concerned.
• the raw data collected and packaged D are sent by the data sensor 1 to the mobile telephone 3 by means of a Bluetooth® communication, during a first transmission step E 7 .
• the packaged raw information D is sent by the communication interface 15, these data being sent in association with the identifier I making it possible to identify the nature or the type of the realized movement.
• the data bundled With associated with the identifier Ii provided by the data sensor 1.1 are transmitted by the mobile phone 3.1. to the data server 5.
• the data Du, Di.2a, Di. n are stored on the mobile phone 3 before being transferred by it to the server 5 via the network 9, during a second transmission step Es.
• the sending of the raw data is automatically proposed to the user on the application PG3 executed on his mobile phone 3.
• the application PG1 executed on the data sensor 1 sends a request to the PG3 application of the mobile phone to ask the user to accept sending the collected raw data to the remote database 7.
• the sending of the raw data is performed without user intervention, for example randomly or automatically when the condition of reliability of recognition is not met.
• Randomness is particularly advantageous for increasing the knowledge of the user base and refining the algorithms accordingly. For example, assuming that in a particular world region or for a category number of people given the recognition performance of the movements are less good, the obtaining of additional raw data is particularly advantageous to achieve a campaign creation and / or modification of motion recognition algorithms.
• the server can contact a dozen users of the category mentioned for the campaign via the PG3 application running on their mobile phone. In this case, a request is sent by the server to these phones. In response to this request, the user informs by means of the PG3 application the identifier I to identify the nature or type of movement made, so that it is transmitted in association with the raw data.
• the user wishing to improve the recognition of movements carried out by his data sensor can initiate of his own volition the steps of collection E 4 , packaging Es and sending E 7 raw data, from the PG3 application executed on its mobile phone 3, without the implementation of the optional test step E 2 .
• the user informs the nature or type of movement made, so that it is transmitted in association with the raw data.
• the collection of raw data is initiated by the user by means of an interface provided on the data sensor, which data is then sent by the data sensor 1 directly to the remote database 7.
• the sensor application includes all or part of the features provided on the PG3 application of the mobile phone described above.
• the raw data packaged is not sent continuously, which limits the amount of data transmitted. This is particularly advantageous since the raw data is bulky.
• the characterization of a motion for a duration of one second requires several kilobytes of data.
• a one-off sending of these data significantly reduces the power consumption of the data sensor compared to the case where the data raw would be sent continuously.
• the energy autonomy of the sensor can be optimized.
• the server 5 On receipt of the raw data, the server 5 stores this data in the remote database 7 during a storage step Eio. Since these raw data are associated with known movements according to the sequence of movements presented to the user during the collection step E 4 , these constitute a set of data that can be used to increase the reliability of the motion recognition. implemented by different data sensors.
• the remote database 7 comprises for each raw data from a sensor an identifier of the type of movement concerned.
• the database may include several sets of characteristic data of the same type of movement for a given sport.
• the raw data D1.2b and D1 .n issued respectively from the sensors 1.2b and 1n are stored in the database 7 in association with the same identifier I 2 designating, for example , a tennis lapel.
• the centralized storage in the database 7 allows all the deployed sensors to contribute to the enrichment of the content of this database during the collection step E 4 . Feeding this database centrally by a large number of sensors makes it possible to constitute a data set sufficiently complete to be analyzed statistically.
• the enrichment of the content of the database makes it possible in particular to constitute an extensive list of possible ways to perform different movements.
• the regular enrichment of the content of this database by raw data from different sensors is particularly advantageous for improving the overall reliability of motion recognition implemented on one or more sensors.
• the server is adapted to implement at least one motion recognition algorithm for each sport practiced.
• the server is adapted to implement at least the following three modules: an activity qualifier (activity qualifier) for identifying the nature of a movement characteristic of a sporting practice, by detecting the different phases of a movement from the measured raw data, a corresponding activity data stream being provided as output of the activity qualifier;
• an activity qualifier activity qualifier
• classifier to classify the activity data from the qualifier
• an analyzer for processing classified activity data from the classifier.
• the activity qualifier provides the classifier with a flow of activity data including qualification values called qualifier values, such as rms and / or average values of acceleration and rotational speed along one or more axes, expressed for example in the local coordinate system at the sensor and / or in the coordinate system of the terrestrial reference.
• qualifier values such as rms and / or average values of acceleration and rotational speed along one or more axes, expressed for example in the local coordinate system at the sensor and / or in the coordinate system of the terrestrial reference.
• qualifier values are made according to each sport considered, for example, following a human analysis. This choice can be made according to tests / errors type tests supported by statistical correlation analyzes.
• the qualifier detects, from the measured raw data, that a punch has been made by the boxer and provides the classifier acceleration data according to the local X axis (component ⁇ ) measured by the local linear accelerometer M 2 .
• qualifying values such as the amplitude of the movement and / or the projection elements of the trajectory of the movement in a reference plane.
• the trajectory detected by the sensor may be projected in a horizontal plane and the two dimensions (length L1 and width L2) of a rectangle in which the projected trajectory is written will be calculated.
• the length L1 and the width L2 of the rectangle are considered qualifier values which prove to be very useful to make the difference between a direct punch and a boxing hook, for example.
• the trajectory may be temporally divided into a plurality of segments, so that qualifier values as described above are calculated on each of these segments.
• the classifier can be adapted to the sport practiced.
• This module allows the recognition of forms (pattern recognition) by the implementation of algorithms of artificial learning (machine learning), from qualifier values provided by the qualifier of activities.
• Such algorithms are based, for example, on C45 decision trees, logistic regression or neural networks.
• This classification method is supervised, in the sense that the nature of the movement is known beforehand to carry out the classification thanks to the flow of activity data provided by the classifier.
• a linear regression algorithm can also be used to classify the different movements.
• Classification rules can be established using decision tree learning methods. The decision tree thus determined makes it possible to classify the movements, that is to say, to recognize them by relying on a large database of already classified sporting gestures.
• the result provided by the classifier is a set of recognized motions which are generally associated with the acceleration and attitude data of the sensor in the terrestrial frame. These data are used by the analyzer implementing algorithms specific to each sport and each movement recognized by the classifier to calculate metrics interesting for the user, such as the speed of the racket just before impact with the ball in the case of tennis, the height of a jump in the case of riding or skiing, or the acceleration at the moment of impact in the case of boxing.
• metrics interesting for the user such as the speed of the racket just before impact with the ball in the case of tennis, the height of a jump in the case of riding or skiing, or the acceleration at the moment of impact in the case of boxing.
• the server 5 performs statistical processing of all or part of the raw data contained in the database 7 to improve motion recognition algorithms.
• the raw data is processed by the server 5 according to statistical analysis methods, so as to provide one or more data sensors:
• the configuration of the motion recognition algorithm implemented by one or more sensors may be such that the classification reliability of the movements is not optimal.
• the raw data provided by the sensor of this sportsman and recorded in the remote database during a training session are used by the data server 5 to adapt the classification algorithm of the classifier and consequently the classifier implemented by the server.
• This classifier makes it possible to improve the recognition reliability of the algorithm according to the statistical analysis of the raw data of a particular sportsman or a group of athletes, the algorithm for recognizing a sensor being derived from the classifier implemented on the server.
• the classification algorithm can be adapted or reconfigured according to specific data of the athlete. This adaptation is performed according to known statistical analysis methods for customizing the recognition algorithm, for example of the C4.5 type.
• Statistical analysis methods advantageously allow the consideration of a very large number of individual data that could not be processed manually by a human being. This is particularly the case for improving the recognition reliability of a generic algorithm for a sport practiced by a large number of users.
• the statistical analysis methods can exploit the Hamming distance criterion to reconfigure or adapt the classification algorithm of the classifier module implemented on the server.
• the motion recognition algorithm is reconfigured or adapted, so that the Hamming distance is as large as possible to limit the risk of confusing movements.
• the adaptation of the classification algorithm of the classifier module according to the Hamming distance by statistical analysis methods constitutes a means for determining configuration parameters of the algorithm making it possible, for example, to maximize the Hamming distance. for an optimal recognition of movements.
• the inventors initially found that the Hamming distance was 0.25 considering the following two Qualifiers: "Variation of the acceleration along the local axis X” and “Variance of the rotational speed around the local axis Z ". Then by performing more complete tests with new types of athletes, this distance is increased to 0 indicating a confusion for the recognition of certain gestures, this being due to a more pronounced movement of the rotation around the local axis X.
• correction or adaptation of the algorithm consists in replacing the
• All or part of the raw data stored in the remote database can be presented as a cloud of measurement points on a two-dimensional diagram, as shown in Figure 5a, where the x-axis represents the variance the mean linear acceleration along the local axis X (Vyx) and the ordinate axis represents the rms value of the rotation speed around the local Z axis (Vooz).
• a first cloud of points Gl is centered on a first coordinate reference point A (Vyx (A), Vcoz (A)) designating the center of gravity relative to a first gesture
• a second point cloud G2 is centered on a second coordinate reference point B ((Vyx (B), Vcoz (B)) designating the center of gravity relative to a second gesture.
• the Hamming distance observed for a population of tested athletes is illustrated by the vector dl.
• the standardized Hamming distance is used.
• the values Va-y are divided along the ordinate axis by the value of the ordinate Vcoz (A) of the first reference point A and the values Vyx are divided along the abscissa axis by the value of the Vyx abscissa (B) of the second reference point B.
• the normalization facilitates the comparison of performances of different algorithms.
• the motion recognition algorithm used by the data sensor (s) that provided the raw data is adapted, so as to maximize the characterizing Hamming distance. the signature difference of each movement.
• the statistical analysis method implemented by the server 7 comprises:
• a partitioning step which consists in defining, on the representation of FIG. 5a, three disjoint zones SI, S2, S3 according to the example illustrated in FIG. 5b: a first zone SI corresponding to the first gesture, a second zone S2 corresponding to the second gesture, and a third intermediate zone S3 located between the first and second point clouds; - A test step which consists in checking whether each of the measuring points belongs to one of the three predefined zones.
• partitioning is defined as follows:
• the first zone SI is defined as the intersection of two straight lines x1, x2 parallel to the ordinate axis and two straight lines y3, y4 parallel to the abscissa axis
• the second zone S2 is defined as the intersection of two straight lines x3, x4 parallel to the ordinate axis and two straight lines y1, y2 parallel to the abscissa axis;
• the third zone S3 is defined by the area between a first line NI tangential to the first cloud of points G1 and passing through the reference point O formed by the intersection of the abscissa axis and the axis ordinates, and secondly a second straight line N2 tangential to the second cloud of points G2 passing through the landmark O, these two lines forming a minimum angle between them.
• the configuration parameters of the motion recognition algorithm are as follows:
• the variance of the acceleration in the terrestrial fixed plane X, Y (Vyx, y ) and the variance of the speed of rotation around the Z axis (Vcoz) being selected as qualifiers (qualifying or qualifying values) adapted, the parameters x1, x2, y3, y4 defining the first area S1, the parameters x3, x4, y1, y2 defining the second area S2, and the two tangent lines N1, N2 defining the third area S3.
• a user with movements whose recognition leads too often to near points i.e. low Hamming distance, for example equal to 0
• a low Hamming distance is an indicator of reduced recognition reliability.
• the statistical analysis of the collected raw data leads to increasing the Hamming distance, which has the effect of making the motion recognition algorithm performed by the motion recognition algorithm more reliable.
• Statistical analysis algorithms may be powered by more qualifier values that potentially increase the Hamming distance to further improve recognition reliability.
• the adaptation of the classifier at the server level can be assisted by a human being who defines in advance the types of movement considered for the statistical analysis. If the movement has been previously classified by the user, the classifier module implemented at the server can operate autonomously and the firmware can be modified or generated automatically. Human intervention is planned to check the correct functioning and to authorize the official diffusion of the modified or generated firmware.
• the application of the data sensor this application containing one or more motion recognition algorithms, and other functions such as battery charge management, display management, will be designated by firmware. , Bluetooth communication.
• the statistical analysis step advantageously makes it possible to extract configuration parameters intended to configure the application executed on the data sensor according to the specificities of the sensor and / or the sportsman concerned.
• the statistical analysis step advantageously makes it possible to extract useful update parameters. to evolve the deployed application on all the sensors concerned.
• an updating step E 14 a new version of firmware is generated for one or more sensors requiring such an update, according to the results of the statistical analysis step E12, this generation being able to be implementation implemented by the server, without or with the intervention of a human being especially for control or verification purposes.
• the value of the Hamming distance resulting from the implementation of the classification algorithm of the classifier module by the server 5 constitutes a result making it possible to decide on a modification of the firmware.
• an update can be performed according to the Hamming distance calculated during the statistical analysis stage relating to all or part of the data stored in the database.
• the Hamming distance is a parameter that potentially triggers a firmware update.
• the new firmware is sent by the server 5 to the mobile phone 3 which transmits it to the (s) sensor (s) of data 1 concerned (s) by this update.
• the installation of the new firmware on the data sensor (s) can be controlled by the user using the application installed on his mobile phone.
• the generation and the transmission of the new firmware by the server and the execution of its installation on the data sensor constitute the update step Ei4 of the application, this application being contained in said firmware.
• an update of the application executed on the data sensor is not essential to improve the recognition of movements.
• the configuration parameters are sent by the server 5 to the data sensor 1 via the mobile phone 3. Upon receipt of these parameters, the data sensor configures the motion recognition application according to the parameters received.
• the obtaining and sending the configuration parameters and the application of these sensor on the application parameters constitute the configuring step E 15.
• the server 5 supplies to one or more data sensors a classification parameter of the movement, said parameter being determined according to analysis parameters, such as the Hamming distance.
• the motion classification parameter constitutes a configuration parameter determined according to the results of the statistical analysis step E 12 .
• the motion recognition algorithm may be executed by the application installed on the mobile phone rather than by the application of the data sensor.

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• 2016
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