Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/90—Details of database functions independent of the retrieved data types
  • G06F16/906—Clustering; Classification
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
  • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• • 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
  • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
• • 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
  • A61B5/1116—Determining posture transitions
• • 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
  • A61B5/1118—Determining activity level
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient ; user input means
  • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
• • 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
  • G06F16/28—Databases characterised by their database models, e.g. relational or object models
  • G06F16/284—Relational databases
  • G06F16/285—Clustering or classification

Definitions

• the present invention relates generally to the field of remote measurement and monitoring of a person's bodily activity.
• the invention relates more particularly to the measurement and remote monitoring of an indicator of autonomy of fragile or isolated people.
• Other methods use motion detection sensors such as, for example, inertial sensors (experimental equipment, smartphones, connected watches) and / or visual sensors (cameras installed in the measurement location) in order to be able to deduce data from these sensors, the subject's movements.
• the identified movement data are then classified by type of movement or posture (sitting, standing, lying, walking, running, etc.) and these methods compare the subject's movements carried out in real time with the classified movement data, in order to identify abnormal behavior and sound an alarm to a third party.
• supervised methods require the implementation of a reference movement database. The data in this database is compared with the data from the sensors, in order to deduce the gesture made by the subject.
• One of the aims of the invention is to remedy the drawbacks of the aforementioned state of the art.
• an object of the present invention relates to a method for evaluating the bodily activity of a user, implemented by a computing device, characterized in that it comprises the following: - on a given date and for a given duration, acquire movement data of said user from a movement sensor,
• the acquired movement data are raw data.
• Raw data is understood to mean data originating essentially from the movements of said user. This data does not include additional data such as data from models representative of movements of other users.
• a distribution of this raw data in different groups of data corresponding respectively to different types of movement amounts to classifying this raw data into at least one class of data, according to at least one unsupervised classification method.
• the data class thus represents an anonymized posture of the person.
• the invention it is possible to evaluate almost in real time (from period to given period) the variations in activity of a user, by comparing the data structures calculated on the given date and for the given duration with at least one other data structure calculated on a date prior to said given date and for a given duration which is the same as the duration on said given date. Taking into account the acquisition, distribution and calculation operations implemented according to the method described above, the invention therefore makes it possible to detect and classify different movement data by unsupervised analysis or classification techniques. of data. These classification techniques are not intended to characterize and name the movement data in gestures, as is achieved by example in the state of the art, but only to group movement data together because they represent a similar gesture without, however, characterizing this gesture.
• unsupervised classification techniques allow non-linear differentiation of the different movement data of the person, which makes it possible to precisely distinguish each movement of the user.
• These unsupervised classification techniques do not require comparing raw data with data models as is the case with supervised classification techniques. Such a fine distinction of the different raw movement data makes it possible to measure different bodily activities of the user and to follow their variations over time.
• This invention does not furthermore allow the raw movement data to be associated with gestures of common life (sitting, standing, lying down, walking, etc.), unless complex analyzes are carried out to characterize these gestures and that, without guarantees of results.
• This difficulty of association is in fact an advantage because it strengthens the preservation of the user's personal data.
• said other data structure is selected from a plurality of data structures representative of the bodily activity of said user, which were calculated on a date prior to said given date and for said given duration.
• the current bodily activity exercised on a given niche, for example on Monday between 8:30 am and 9:30 am
• autonomy is strongly linked to the habits (recurrence of certain bodily activities during equivalent or close time slots) of a user.
• the judicious choice of dates is therefore essential in order to compare periods when the user's bodily activities are similar, in order to detect deviations in activities and therefore indications of loss of autonomy for the user.
• dates chosen do not have to obey a fixed periodic rule (every week at the same time) if the person's habits require choosing dates without logic apparent between them in terms of periodicity.
• This choice of dates to compare can be made by the user, a relative, a practitioner or any other third party who is familiar with the habits of the user.
• said duration is determined with respect to obtaining a desired minimum number N of different groups of data, in which to distribute the acquired movement data.
• the duration is indexed as a function of a minimum number N of movement data and not on a minimum duration to take into account the time slots of inactivity of the user where there may be long periods without particular activity. of the person (example of sleep).
• an autonomy value A is calculated as a function of a plurality of variation evaluations calculated for a plurality of given durations.
• the method performs a comparison of the most recent user activity in a given time slot (example of a full hour preceding the time of comparison) with previous activities of the same duration already saved, for example in dedicated databases. It then generalizes this calculation to other niches (example of the day, week, month or year preceding the comparison) by comparing the most recent activity compatible with the niche analyzes with previous activities on the same slots (example of all Mondays already registered for daily slots, all weeks already recorded for weekly slots etc ).
• This comparison of bodily activity generalized over different periods of the life of a user makes it possible to detect changes in habits as well on well-targeted activities that last around the hour (eating breakfast, playing sports) , watch a movie, have lunch ...) but also on a plurality of activities taking place over longer periods (days, weeks) to very long (months, years).
• This measure of changes in habits is closely linked to the concept of autonomy, because it is known to those skilled in the art that Autonomous people function routinely while changes in habits, and therefore the loss of bearings, are signs of a failure in a person's autonomy.
• the method therefore seeks to give a quantifiable value, objective because based on measurements and calculated by a machine, autonomy, and therefore the loss of autonomy of a user.
• This autonomy value allows the person in charge of monitoring the user (family, doctor, etc.) to be regularly informed of the user's level of autonomy in order to take the actions he deems necessary.
• each of said variation evaluations are respectively assigned a weighting coefficient.
• the weighting of each variation in bodily activities of the user makes it possible to take into account periods that are more relevant than others in the analysis of changes in habits. Indeed, one might think that the measurement of activity changes from previous days is more significant of a change in autonomy than the same measurement made at a more distant period, for example for days of the previous year.
• weighting can also be used to increase the influence of measures of variation in bodily activity in the measurement of autonomy depending on the duration of the time slots used for the measures.
• the method comprises the following:
• the triggering of the action which materializes the loss of autonomy is a function of a single parameter of digital type. It is therefore easy to define an action trigger threshold. In the closest state of the art, no method makes it possible to trigger an alert linked to a loss of autonomy.
• the information generated is communicated to a person authorized by said user.
• this other embodiment it is possible to inform a third party about a possible loss of autonomy of the user. Due to the absence of naming and characterization of the raw movement data transmitted in such information, the action of communication type towards a third party does not in fact disclose the bodily activities of the user to the third party receiving this information and preserves therefore the confidentiality of the user's personal data.
• the invention also relates to a device for evaluating the bodily activity of a user, such a device comprising a processor which is configured to implement the following:
• Such a calculation device is in particular capable of implementing the aforementioned method of evaluating the bodily activity of a user, according to any one of the aforementioned embodiments.
• the invention also relates to a computer program comprising instructions for implementing the method for evaluating bodily activity. of a user, according to any one of the particular embodiments described above, when said program is executed by a processor.
• 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 desirable form.
• the invention also relates to a recording medium or information medium readable by a computer, and comprising instructions of a computer program as mentioned above.
• the recording medium can be any entity or device capable of storing the program.
• the support may include a storage means, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or else a magnetic recording means, for example a USB key or a hard disk.
• the recording medium can be a transmissible medium such as an electrical or optical signal, which can be routed via an electrical or optical cable, by radio or by other means.
• the program according to the invention can in particular be downloaded from a network of the Internet type.
• the recording medium can 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 above-mentioned evaluation method.
• FIG 1 represents the main actions performed by the method of evaluating the bodily activity of a user according to an embodiment of the invention
• FIG 2 shows in more detail one of the actions performed by the method of Figure 1,
• FIG. 4 [Fig 3] - Figure 3 shows in more detail another of the actions performed by the method of Figure 1
• FIGS. 4A, 4B and 4C represent different examples of data structures obtained according to the method of FIG. 1,
• FIG. 1 represents in more detail a calculation of variation in bodily activity executed according to the method of FIG. 1,
• FIG. 6 - Figures 6A and 6B represent examples of visualization of data structures over one or more days
• FIG. 7 represents a device for evaluating the body activity of a user implementing the method for evaluating the body activity of a user of FIG. 1.
• the objective of this invention is the implementation of an automatic metric in order to assess the bodily behavior of an individual as being a situational routine or on the contrary a rare event, and to generate a degree of information to whom law by various means of communication.
• the invention mainly provides a method and a device for calculating a measure of autonomy of a person monitored remotely using raw data from one or more motion sensors carried by the person.
• the objective is not, like many solutions on the market, to launch alerts during a punctual event (fall, malaise ...) but to provide monitoring over time of the bodily activity of the person , in order to inform the medical profession, the family or any interested third party, in the event of a change in the person's habits.
• Such a measure of autonomy takes for example the form of an index of autonomy on a scale of 0 to 1: 0.1 everything is fine, 0.5 one begins to worry, 0.9 there is urgency to intervene.
• the method works by detecting postures specific to the individual by so-called pre-clustering methods (classification of similar raw data into data classes by unsupervised clustering methods by non-linear separator) via the analysis of different flows.
• inertial data acceleration of similar raw data into data classes by unsupervised clustering methods by non-linear separator
• inertial data acceleration of similar raw data into data classes by unsupervised clustering methods by non-linear separator
• the invention proposes to circumvent these problems by proposing an almost unsupervised model capable of isolating postures and performing groups of postures without necessarily characterizing these postures (sitting, lying, standing etc ).
• the postures are therefore anonymized because they cannot be characterized by the process and the process therefore does not make it possible to characterize the postures of said person over time.
• These postures, represented by the data classes in the process may be in large number and characteristic of the individual. To be effective, this method requires the use of only one inertial sensor.
• the method analyzes and identifies the data classes and creates a data structure, such as for example, a list, a matrix, a vector, etc., characterizing an aggregate of the data classes over this duration .
• This data structure is a representation of behaviors or activities such as for example: I get up in the morning, I turn on the coffee maker, I prepare breakfast, I eat lunch, I brush my teeth ...
• sequences of data classes can be analyzed every hour or for different durations (shorter or longer) depending on what you want to analyze and also over time slots specifically defined by the model or by an expert, because they represent particular moments of the day (breakfast, lunch, afternoon stroll, etc.). This analysis is performed in real time on each of the selected time slots.
• these behavior models are compared with other previously saved models to identify similarities in behavior at appropriate times.
• These opportune moments can be the comparison of a time slot with the same time slot of each day of the past week or of each Monday of the past weeks.
• This similarity measure is calculated with neural networks (model which must be trained) and can be performed directly in the terminal integrating the inertial data sensor (example a smartphone or a connected watch) or remote equipment such as a computer or server or a mix of the two equipment.
• a autonomy value A calculated at the end of the method according to the invention is, according to a possible embodiment, compared with a threshold Z. An alarm is then raised if the value A is greater than the threshold Z. This alarm triggers the transmission of information to a third party, such as for example a family member, the attending physician, a service company, a care establishment and even any company or entity able to provide even a partial response to these changes routine (example: supplier of musical content, films, etc.).
• a third party such as for example a family member, the attending physician, a service company, a care establishment and even any company or entity able to provide even a partial response to these changes routine (example: supplier of musical content, films, etc.).
• a raw data sensor CAP records continuously on a transient or non-transient storage medium (shown and named MEM_CAP in Figure 2) the raw digital data describing the movement of a user.
• MEM_CAP transient or non-transient storage medium
• Such a sensor is for example worn by the user. It is for example an accelerometer, gyrometer, magnetometer, etc., commonly installed in a mobile phone, or any portable device such as a connected object.
• the raw data stored is transmitted to a database MVT_DB which is the main knowledge base describing the movements of a person for a long duration of the order of several months to several years (the years 2016, 2017 and part of 2018 in our example).
• the data recorded continuously ACQU in PI are either directly transmitted by the sensor and recorded in the database MVT_DB (dotted arrow), or stored in the memory of the sensor MEM_CAP. All of this raw data collected constitutes a raw database.
• SIM_H_DB represents raw movement data for similar one-hour time slots (10h-l lh) or dissimilar slots (0h-10h-12h-24h)
• SIM_D_DB for similar days (every Monday from database from 2017) and dissimilar (every Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday of the database)
• SIM_W_DB for similar weeks (Week 5 of 2017 and 2018) and dissimilar (the others weeks in the database)
• SIM_M_DB for similar months (the months of January 2017 and 2018) and dissimilar (every month from February to December in the database)
• SIM_Y_DB for similar years (2017) and dissimilar ( 2016 because more distant in time, this year represents less the current situations).
• the signals are sorted in order to keep only the signals to be processed.
• nine signals are picked up by the CAP sensor so as to have values from three sensors (accelerometer, magnetometer and gyrometer) and in the three Euclidean dimensions.
• the process may not want to keep all the raw data.
• the method can decide to keep only six values instead of the nine because the user is for example in the lying position and does not use the magnetometer. In this case, in TRAIT_1, the three values from the magnetometer are not preserved.
• An extraction EXTR_Tr_Dt of the raw data is carried out in the base MVT_DB so as to recover the part MVT_Tr_Dt of the data of the database which were acquired by the sensor on said given date (reference date called Tr) and for said given duration also called reference duration Dt.
• This raw data can be stored in a transient memory or not of the sensor or of the device or in a database located on an external device, such as a computer or a server for example.
• the raw movement data is collected on Tuesday February 6, 2018 from 10 a.m. to 1 a.m. Dt therefore corresponds to a duration of one hour.
• processing is carried out on the collected and segmented interest signals, representative of the MVT_Tr_Dt part of the raw data in the database.
• the processing operations are, in a non-exhaustive manner, low-pass filtering to de-noise the information, normalization to harmonize the raw data, resampling of data to synchronize the sources, etc.
• such a REP classification includes a classification of the raw data, stored in the base MVT_Tr_Dt, which represent similar anonymized postures of said person and grouped into data classes Sri, ..., Sri, ... , SrN.
• the data classes are learned automatically by unsupervised grouping (in English "clustering") of time series from the knowledge base SIM_Dt_DB.
• the method uses recurrent neural networks GRU (from the English “Gated Recurrent Unit”) previously learned on the basis SM_Dt_DB.
• GRU from the English “Gated Recurrent Unit”
• different data classes Sri, ..., Sri, ..., SrN were determined by clustering on the basis SIM_Dt_DB.
• a learning process follows to model a GRU network capable of classifying this raw movement data into classes of data representing anonymized postures of said person.
• the data structure designated by Vr, is a histogram or a vector of Sri data classes recognized in P2. Each value in the histogram represents the number Ci of times a Sri data class has been recognized.
• the data structure Vr is represented by a histogram of the different classes of data and their number during the given duration. On the abscissa, the number N of different data classes is represented and on the ordinate the number Ci of times a given data class has been identified by the method.
• the data structure Vr is represented by a stack of square shapes. Each square represents a different class of data. For each of the data classes, we represent, by a number of black pixels inside the square, the number Ci of times the data class has been identified during the given duration.
• the comparison COMP consists of predictions of similarity of the data structure Vr by comparison with other structures of body activity data VI, ..., Va, ... , VM calculated for data acquisitions in previous periods (previous dates but for a similar duration).
• the goal is to know if the data structure Vr is similar to the other data structures characteristic of its time slot (here in our example every Monday from 10 a.m. to 1 a.m.) in the past of the individual or to one or different dates, but always for the same duration Dt (example of 1 IL ⁇ at 12 noon on Monday February 5 of the year before).
• the Siamese neural networks allow this type of comparison to be made. If two data structures that have been learned by the network represent the same bodily behavior during the period, the Siamese network produces a measurement close to 0. If two data structures are dissimilar (ex: Tuesday's bodily behavior between 10 and 10 lh is different from that of Sunday, between 22h and 23h), the Siamese network produces a measurement close to 1. Then, to calculate the value DELTA_Dt, we will carry out a weighted average of the comparison values between the data structure to be evaluated (Vr on the diagram in Figure 5) and the different data structures already measured at earlier dates (Go to the diagram in Figure 5).
• the goal is to make comparisons with several previous dates when the person should perform similar activities, follow the same routine (example from Monday to Friday at 1:00).
• the difficulty is to choose these earlier dates appropriately to characterize an abnormal activity and not simply a little different from usual.
• This choice of relevance can be made by an expert (doctor, listener) following a prior study of the habits of the person to be followed, but also by the process itself if it has sufficient data concerning the habits of the person to be followed. and therefore opportune moments of comparison. Alternatively, this choice of relevance can be made by both the expert and the process.
• the COMP comparison consists in calculating the similarity DELTA_D between the previous day (Monday) and all the other Mondays of the MVT_DB database.
• the reference day to be considered is the day preceding the given date because all the data are available for this day. Variations in the behavior of the individual on previous Mondays are then considered. If no variation is noted, the user's bodily activity is considered normal. If one or more variations have been noted compared to previous Mondays, the user's bodily activity is considered to be "worsening".
• the method calculates an estimate of the similarity DELTA_W between the previous week (S5 2018) and the other weeks of the base MVT_DB, and, if the base MVT_DB allows it, calculates the measures of similarity of weeks , months and years as follows:
• the characteristic vector of the user's bodily activity over a day is the concatenation of the 24 histograms of data classes.
• the characteristic vector of the user's bodily activity over a day Dl, D2, etc. is the concatenation of the 24 columns of N squares representing respectively 24 time slots. Squares representing a group of data representing similar movements.
• a characteristic vector can be calculated to characterize the bodily activity of the user over a longer period, such as a week, a month or a year.
• the method comprises the calculation of an autonomy value A as a function of the similarities predicted in P4 over the different durations of analyzes carried out.
• an autonomy value A as a function of the similarities predicted in P4 over the different durations of analyzes carried out.
• such a calculation uses the following equation:
• A WH * DELTA_H + WD * DELTA_D + WW * DELTA_W + WM * DELTA_M + WY * DELTA_Y,
• the real weighting numbers can be harmful if the knowledge base MVT_DB does not allow to calculate, for lack of data, the corresponding similarities.
• These weights can be defined manually by an expert (doctor, listener) following an analysis of changes in the behavior of the person to be followed, or automatically by a study of the history of the habits of the person being followed. Alternatively, these weights can be set both manually and automatically.
• the information generated in P7 represents an interpretation of a value corresponding to the difference between the value A and the value Z.
• the value A when communicated by the service described next in P8, makes it possible to give simple and easily understandable information to the competent or family services on the user's level of autonomy.
• This information can be text, graphic or any other means of information.
• Such COM communication can be implemented to inform the individual, his entourage, his relatives, his medical corps, etc. (if agreed by the individual) of his change in bodily habits by transmitting an interpretation of the value A.
• This communication can consist of sending an email, a text message of SMS type, a telephone call, an audible or visual alarm or any other means to alert a third party.
• the process can include the data extracted during this time slot as similar in the base SIM_H_DB ;
• the process can include the data extracted throughout the day as similar in the SIM_D_DB database;
• the process can include the data extracted during the whole week as similar in the base SIM_W_DB;
• the process can include the data extracted during the whole month as similar in the SIM_M_DB database;
• the process can include the data extracted during the whole year as similar in the base SIM_Y_DB.
• the method does not add current data to the SIM_Dt_DB training databases.
• the models of neural networks SIAM_Dt respectively linked to the bases SIM_Dt_DB are updated with the new data to improve their capacity to identify non-routine situations.
• the models must be profoundly modified to correspond to the new routines by being completely re-trained using well-known learning algorithms (ex: fine tuning, transfer leaming, adaptive learning, etc.).
• fine tuning fine tuning, transfer leaming, adaptive learning, etc.
• the actions executed by the evaluation method are implemented by a system comprising an evaluation device D_EVAL and a terminal T_CAP for acquiring data.
• the evaluation device D_EVAL is for example a computer or a server.
• the evaluation device D_EVAL has the classic architecture of a computer and in particular comprises a memory MEM_EVAL, a processing unit UT_EVAL, equipped for example with a processor PROC_EVAL, and controlled by the computer program PG_EVAL stored in memory MEM_EVAL.
• the computer program PG_EVAL includes instructions for implementing the actions of the method for evaluating the bodily activity of a user as described above, when the program is executed by the processor PROC_EVAL.
• the code instructions of the computer program PG_EVAL are for example loaded into a RAM memory (not shown) before being executed by the processor PROC_EVAL.
• the processor PROC_EVAL of the processing unit UT_EVAL implements in particular the actions of the method for evaluating the bodily activity of a user described above, according to the instructions of the computer program PG_EVAL.
• the T_CAP data acquisition terminal is for example a connected object carried by the user.
• the acquisition terminal has the conventional architecture of a computer and notably comprises a memory MEM_CAP, a processing unit UT_CAP, equipped for example with a processor PROC_CAP, and controlled by the computer program PG_CAP stored in MEM_CAP memory. It also includes a CAP movement sensor allowing all kinds of capture of types of movement data (for example: accelerometer, gyroscopic, magnetometric).
• the computer program PG_CAP includes instructions for implementing the actions of the method for evaluating the bodily activity of a user as described above, when the program is executed by the processor PROC_CAP.
• the code instructions of the computer program PG_CAP are for example loaded into a RAM memory (not shown) before being executed by the processor PROC_CAP.
• the processor PROC_CAP of the processing unit UT_CAP implements in particular the actions of the method for evaluating the bodily activity of a user described above, according to the instructions of the computer program PG_CAP.
• the process TRAIT_1 makes it possible to obtain and select signals of interest to be processed.
• the process TRAIT_1 can be carried out by the terminal T_CAP as follows - the movement data acquired by the CAP sensor are stored in memory MEM_CAP in real time and processed by the process TRAIT_1. The processed data are then transmitted to the MEM_EVAL memory to be finally stored in the MVT_DB database.
• the process TRAIT_1 can also be carried out a posteriori, that is to say that the acquisition data not processed by the terminal T_CAP are stored directly in the database MVT_DB and processed a posteriori with the process TRAIT_1 by the device d 'evaluation D_EVAL (cf figure 2 the dotted arrows between TRAIT_1 and MVT_DB).
• the process TRAIT_1 can also be a combination of treatments executed in real time in the memories MEM_CAP and MEM_EVAL, the processed data being stored a posteriori in the database MVT_DB in order to adapt to the storage and calculation performance of the terminal T_CAP.
• the evaluation device D_EVAL and the terminal T_CAP can be interconnected and exchange data on one or more communication link (s), using one or more networks of different types, (a network on the Figure 7), and different protocols.
• networks are a fixed network, a cellular network (for example according to the 2G standard (GSM, GPRS, EDGE), 3G (UMTS), 4G (LTE), LTE-A, LTE-M, WCDMA, CDMA2000, HSPA , 5G, or their variants or evolutions), another type of radio network (eg WiFi® or Bluetooth®), an IP network, a combination of several of these networks, etc.
• GSM 2G standard
• GPRS GPRS
• EDGE 3G
• 4G LTE
• LTE-A LTE-A
• LTE-M Long Term Evolution
• WCDMA Code Division Multiple Access 2000
• HSPA High Speed Packet Access 2000
• 5G Fifth Generation
• IP network a combination of several of these networks, etc.
• the MEM_EVAL memory can include one or all of the databases (MVT_DB, MVT_Tr_Dt, SIM_Dt_DB) of the process but not necessarily. Indeed, these databases could be distinct from the D_EVAL device. Alternatively, for more flexibility, one or more of these bases will be separate from the D_EVAL device, while the other bases of the set will be included in the D_EVAL device.
• each step of calculating the process can be carried out respectively by the device D_EVAL or the terminal T_CAP so as to represent all the possible combinations of configurations of calculations between the device D_EVAL and the terminal T_CAP.

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