Device and method for tracking the posture of a subject

Abstract

Device and method for tracking the posture of a subject The invention relates to a method and a processing device (T1) for tracking the posture of a subject (UR1). The method comprises: obtaining sensor data (DT1) representative of the subject (UR1); generating, from the sensor data, a model (ML1) defining anatomical points (PT1) of the subject over time; determining, from the model (ML1), kinematic variables (V1) representative of the evolution of the posture of the subject; determining, from the kinematic variables (V1), at least one non-linear variability indicator (V2) representative of a temporal structure of the kinematic variables (V1); analyzing (S10) said at least one non-linear variability indicator (V2) by comparison with a respective reference value (REF2); and generating a posture setpoint (CMD1) as a function of a result of the analysis. Figure for abstract: Fig. 1.

Description

Title of the invention: Device and method for tracking the posture of a subject Technical field

[0001] The invention relates to the field of analysis and monitoring of a subject's posture and relates more particularly to a processing device and method for monitoring a subject's posture, in particular with a view to evaluating and / or correcting the subject's posture. Prior art

[0002] It is now generally accepted that moving is widely beneficial for people, whether on a physical level but also psychologically, cognitively, immune-related, etc., and this at any age. Physical activity, and more generally movement, therefore constitute major public health issues and today occupy a central place in supporting individuals, particularly for athletes and people with specific needs, or for the management of injuries, pathologies, post-operative follow-ups, etc.

[0003] In the field of sport, both professional and amateur, injury is a major issue for athletes and practitioners. There is therefore a significant need today in the care of athletes to avoid injuries (preparation, re-athleticization, etc.), assess their physical and psychological condition, or optimize their performance. Prevention and management of injuries constitute a major issue in the monitoring and care of athletes, at all levels.

[0004] In addition to supporting athletes, movement is a key element in supporting individuals, particularly in a preventive or curative context (after injury, for example). Health care offerings are therefore increasingly moving towards an active approach to health. Many public programs therefore aim to promote physical activity in all its forms (sport, play, work, etc.).

[0005] Monitoring a person's posture, i.e. the way they stand or move in an environment, can provide a great deal of information about the person's health or condition (e.g. in the case of injury, pathology, etc.) or about their functioning (e.g. their performance, habits, etc.). There is therefore a growing need today, particularly among professionals but also among the general public, to evaluate the quality of people's movement, in various contexts, particularly for the purposes of assisting with physical activity, gaming, performance monitoring, rehabilitation or even re-athleticization. In this context, movement analysis techniques have grown significantly in recent years, but current solutions currently have limitations and constraints. which remain a brake on the development of this sector.

[0006] Thus, current posture (or movement) analysis techniques generally do not offer satisfactory results, particularly in terms of reliability and precision. For example, applications currently available on smartphones for posture tracking do not allow for a detailed analysis of a posture, whether static or dynamic. These solutions often induce errors in the positioning of the anatomical parts of the subject and are generally limited to comparing the captured movements to reference models without being able to assess the quality of the movement in all its complexity.

[0007] In addition to the limitations in terms of precision and reliability, some current solutions require complex, expensive and / or tedious equipment to implement. Some current techniques thus require, for example, the deployment of a significant number of sensors, or the installation of reflective sights or markers which are positioned on the target subject itself. The complexity of installation, adjustment and use of the necessary equipment can then prove problematic, or even prohibitive, in practice. The installation, for example, of markers on the subject can thus prove tedious and presents the risk of leading to measurement errors in the event of unexpected movements of the markers. The economic, human and / or time cost for the implementation of these techniques as well as the training required for users limit the deployment of these solutions. Presentation of the invention

[0008] One of the objects of the present invention is to solve at least one of the problems or deficiencies of the technological background described above.

[0009] Another object of the present invention is to enable efficient monitoring of the posture of a subject in terms of precision and reliability, in particular with a view to optimizing the health and / or performance of individuals, for example for the purposes of assisting with physical activity, physical preparation, gaming, performance monitoring, rehabilitation and / or re-athleticization.

[0010] Another object of the present invention is to enable a precise and reliable analysis of the posture, static or dynamic, of a subject and this with limited cost and complexity of implementation.

[0011] Another object of the present invention is to effectively assist in correcting the posture of a subject.

[0012] To this end, a first aspect of the invention relates to a processing method (or tracking method), implemented by a processing device, for tracking the posture of a subject, said method comprising: (a) obtaining sensor data representative of the subject; b) generation, from the sensor data, of a model defining anatomical points of the subject over time; c) determination, from the model, of kinematic variables representative of the evolution of the subject's posture over time; (d) determination, from the kinematic variables, of at least one non-linear indicator of variability representative of a temporal structure of the kinematic variables; e) analyzing said at least one non-linear indicator of variability by comparison with a respective reference value; and f) generation of a posture instruction based on a result of the analysis.

[0013] The method according to the invention may include other characteristics which may be taken separately or in combination, in particular among the following embodiments which are presented for illustration purposes only and may be combined or associated unless otherwise stipulated.

[0014] According to a particular example, the sensor data obtained in a) comprises image data captured by a plurality of image capture devices, the image data from each image capture device defining a respective 2D view of the subject over time; generation b) including: - estimation of a position of the anatomical points of the subject over time in the 2D view from each image acquisition device; and - generation of the 3D model by triangularization of the estimated positions of the anatomical points in each 2D view.

[0015] According to a particular example, generation b) comprises: - attribution, to each estimated position of the anatomical points of the subject in the 2D views, of a respective confidence score representative of a level of confidence in said estimated position; the model being generated from the estimated positions of the anatomical points from which are excluded, by a first filtering, each estimated position of an anatomical point whose confidence score does not reach at least a first threshold value.

[0016] According to a particular example, generation b) of the model comprises: - comparison of first and second confidence scores associated respectively with first and second estimated positions for the same anatomical point in 2D views from image acquisition devices; the model being generated from the estimated positions of the anatomical points to which a second filtering is applied according to which, if the difference between said first and second confidence scores reaches at least a second threshold value, the one among the first and second estimated positions with the lowest confidence score is excluded.

[0017] According to a particular example, said at least one non-linear indicator determined in d) comprises a first sequence of values ​​over time; the analysis e) comprising: - comparison of the first sequence of values ​​of said at least one non-linear indicator over time with a second sequence of reference values; the result of analysis e) being a function of a result of said comparison of the first and second sequences.

[0018] According to a particular example, generation f) comprises at least one of: - restitution, as a first posture instruction, by means of a user interface, of posture information specifying how to correct the subject's posture; and - sending, as a second posture instruction, a command causing a setting of a movement assistance device to correct the subject's posture.

[0019] According to a particular example, analysis e) comprises: - verification of whether said at least one non-linear variability indicator meets a conformity criterion with respect to the respective reference value; wherein, if the compliance criterion is not met, the posture instruction generated in f) is configured to correct the subject's posture.

[0020] According to a particular example, if a non-linear indicator of variability - called a non-compliant indicator - does not meet the compliance criterion, analysis e) includes: - determination of at least one kinematic variable, called target kinematic variable, associated with the non-compliant indicator and of a correction to be applied to said at least one target kinematic variable to improve the non-compliant indicator compared to the reference value according to the compliance criterion; said posture instruction being generated in f) as a function of the correction to be applied to said at least one target kinematic variable to correct the posture of the subject.

[0021] According to a particular example, the determination of said at least one target kinematic variable and the correction to be applied is carried out by an optimization method taking as input: - a non-compliant indicator trend objective; - a function defining a relationship of the non-compliant indicator with a plurality of associated kinematic variables; and - constraint data defining movement constraints that the model must respect; the optimization method delivering as output setpoint data defining said at least one target kinematic variable and the correction to be applied to modify the non-compliant indicator according to the trend objective; the posture instruction being generated in f) from the instruction data.

[0022] According to a particular example, the method comprises, prior to calculation c): - recognition, from the model, of a movement carried out by the subject; and - selection, as a function of the recognized movement, of the kinematic variables to be calculated in c).

[0023] In a particular embodiment, the different steps of the method according to the first aspect of the invention are determined by computer program instructions.

[0024] Consequently, a second aspect of the invention relates to a computer program on an information medium (or recording medium), this program being capable of being implemented in a processing device, or more generally in a computer, this program comprising instructions adapted to the implementation of the steps of the processing method according to the first aspect of the invention. In particular, the second aspect of the invention relates to a computer program comprising instructions for the execution of the steps of the method according to the first aspect of the invention when said program is executed by a computer.

[0025] Thus, the method of the invention can be implemented by means of a non-volatile memory storing computer program instructions and by means of a processor executing these instructions.

[0026] This program may use any programming language, and be in the form of source code, object code, or code intermediate between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0027] A third aspect of the invention relates to an information medium (or recording medium) readable by a computer, and comprising instructions of the computer program according to the second aspect of the invention.

[0028] The information medium may be any entity or device capable of storing the program. For example, the medium may comprise a storage means, such as a rewritable non-volatile memory or ROM, for example a CD ROM or a microelectronic circuit ROM, or a magnetic recording means, for example a hard disk or a memory in the form of a USB key or the like.

[0029] On the other hand, 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 from an Internet-type network.

[0030] Alternatively, the information carrier 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.

[0031] A fourth aspect of the invention relates to a processing device (or tracking device) configured to implement the method according to the first aspect of the invention. The fourth aspect of the invention relates in particular to a processing device configured to carry out posture tracking of a subject, said device comprising a memory associated with a processor configured to implement the steps of the processing method according to the first aspect of the present invention.

[0032] According to one example, the invention according to the fourth aspect provides a treatment device for assisting a subject in performing a posture, said device comprising: - obtaining module configured to obtain sensor data representative of the subject; - first generation module configured to generate, from the sensor data, a model defining anatomical points of the subject over time; - first determination module configured to calculate, from the model, kinematic variables representative of the evolution of the subject's posture over time; - second determination module configured to determine, from the kinematic variables, at least one non-linear indicator of variability representative of a temporal structure of the kinematic variables; - analysis module configured to analyze said at least one non-linear indicator of variability by comparison with a respective reference value; and - second generation module configured to generate a posture instruction based on an analysis result.

[0033] It should be noted that the various embodiments mentioned above (as well as those described below) in relation to the treatment method of the invention as well as the associated advantages apply in a similar manner to the treatment device of the invention.

[0034] In particular, for each step of the processing method, the processing device of the invention may comprise a corresponding module configured to carry out said step.

[0035] According to one embodiment, the invention is implemented by means of software and / or hardware components. In this regard, the term "module" may correspond in this document to a software component, a hardware component or a set of hardware and software components.

[0036] A software component corresponds to one or more computer programs, one or several subroutines of a program, or more generally to any element of a program or software capable of implementing a function or a set of functions, as described below for the module concerned. Such a software component can be executed by a data processor of a physical entity (terminal, server, gateway, router, etc.) and is likely to access the hardware resources of this physical entity (memories, recording media, communication buses, electronic input / output cards, user interfaces, etc.).

[0037] Similarly, a hardware component corresponds to any element of a hardware assembly capable of implementing a function or a set of functions, as described below for the module concerned. It may be a programmable hardware component or one with an integrated processor for executing software, for example an integrated circuit, an electronic card, etc.

[0038] The present invention as defined above and described below in particular examples advantageously makes it possible to accurately and reliably monitor the posture of a subject. In particular, it is possible to evaluate the quality of a posture, static or dynamic, of a subject over time and to guide or improve this posture by generating a posture instruction adapted to the behavior of the subject. Such monitoring makes it possible in particular to evaluate and improve the health of the subject and / or his performance, and this for various purposes depending on the case, such as for example for assistance with physical activity, physical preparation, play, physical rehabilitation and / or re-athleticization. The invention further allows accurate and reliable monitoring of the posture, static or dynamic, of a subject and this with limited cost and complexity of implementation. The present invention can also advantageously offer diagnostic assistance.

[0039] In particular, the analysis of at least one non-linear indicator of variability determined from kinematic variables advantageously makes it possible to finely evaluate the quality of the movement of a subject in order to take into account complex aspects not perceptible on the sole basis of kinematic variables. Depending on the result of this analysis, a posture instruction can be advantageously generated, for example to adapt, improve and / or correct the posture of the subject and thus help to improve the health of the subject and / or his performance.

[0040] The invention therefore offers a high-performance tool, both to movement professionals such as health and / or sports professionals (doctors, therapists, physiotherapists, physical trainers, sports coaches / trainers, etc.) and to the general public, for analyzing and improving the posture of a subject. Brief description of the drawings

[0041] Other characteristics and advantages of the present invention will emerge from the description of the particular and non-limiting exemplary embodiments of the present invention below, with reference to the appended figures 1 to 18, in which:

[0042] [Fig-1] [Fig.l] schematically represents a cooperating processing device with at least one associated device, according to at least one embodiment of the invention;

[0043] [Fig.2] [Fig.2] represents an example of implementation of the processing device of [Fig.l] according to at least one embodiment of the invention;

[0044] [Fig.3] [Fig.3] schematically represents the processing device (or monitoring device) of [Fig.l], according to at least one embodiment of the invention;

[0045] [Fig.4] [Fig.4] represents, in the form of a diagram, the steps of a treatment method (or monitoring method), according to at least one embodiment of the invention;

[0046] [Fig.5] [Fig.5] schematically represents the obtaining of sensor data (image data) during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0047] [Fig.6] [Fig.6] schematically represents the obtaining of sensor data (inertial data) during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0048] [Fig.7] [Fig.7] schematically represents the obtaining of sensor data (EMG data) during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0049] [Fig.8] [Fig.8] schematically represents the generation of a model defining anatomical points during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0050] [Fig.9] [Fig.9] schematically represents the generation of a model defining anatomical points during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0051] [Fig. 10] [Fig. 10] schematically represents the calculation of kinematic variables during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0052] [Fig. 11] [Fig. 11] schematically represents the calculation of kinematic variables during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0053] [Fig. 12] [Fig. 12] schematically illustrates different degrees of posture complexity that can be represented by a non-linear indicator of variability determined during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0054] [Fig. 13] [Fig. 13] schematically illustrates different degrees of complexity of posture likely to be represented by a non-linear indicator of variability determined during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0055] [Fig. 14] [Fig. 14] schematically illustrates different degrees of posture complexity that can be represented by a non-linear indicator of variability determined during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0056] [Fig. 15] [Fig. 15] schematically represents the determination of at least one non-linear indicator of variability (approximation entropic indicator) during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0057] [Fig. 16] [Fig. 16] schematically represents the determination of at least one non-linear variability indicator (GEM type indicator for “Goal Equivalent Manifold” in English) during the processing method of [Fig.4], according to at least one embodiment of the invention;

[0058] [Fig. 17] [Fig. 17] schematically represents the generation of a posture instruction during the processing method of [Fig.4], according to at least one embodiment of the invention; and

[0059] [Fig. 18] [Fig. 18] schematically represents the generation of a posture instruction during the processing method of [Fig.4], according to at least one embodiment of the invention. Description of embodiments

[0060] Examples of implementations of the invention will now be described in the following with reference to Figures 1-18. Unless otherwise indicated, elements common or similar to several figures bear the same reference signs and have identical or similar characteristics, so that these common elements are generally not described again for the sake of simplicity.

[0061] The terms "first(s)", "second(s)", etc.) are used in this document by arbitrary convention to enable different elements (such as operations, threshold values, etc.) implemented in the embodiments described below to be identified and distinguished.

[0062] The invention proposes to track and adapt the posture of a subject. In this context, a posture designates in the present disclosure a positioning or a movement of the subject considered, or more precisely of all or part of the subject's body. In other words, the posture being tracked may be a static posture (position of the body at a given moment) or a dynamic posture (movement of the body). Generally speaking, depending on the precision of the motion sensor(s) used, a posture may therefore in certain cases appear to be static or dynamic. A posture may provide information on the position of one or more anatomical points of a subject at a given moment or over time. To the extent that a given individual is normally subject to variations, even tiny ones, in the positioning of the different parts of his body due to at least natural or involuntary movements (muscle tension, breathing, tremors, joint repositioning, etc.), a posture can generally be considered dynamic over time provided that the sensor data used is sufficiently precise to represent the movements in question.

[0063] The quality of a subject's posture can be influenced by various factors, including physical, psychological, and environmental. It has been found that a good understanding and therefore good monitoring of posture is desirable to effectively guide a subject in performing a posture, movement, or physical activity.

[0064] In the present disclosure, the subject object of the posture monitoring can be any person who may have various physical conditions or states (child, adult, elderly person, injured person, person with a disability, etc.), or even more generally a living being, including an animal, such as a dog or a horse for example.

[0065] The invention relates in particular to a processing method (or tracking method), and a corresponding processing device (or tracking device), for tracking the posture of a subject. The method is based in particular on the determination of kinematic variables from a model defining anatomical points of the subject, and on the analysis of at least one non-linear indicator of variability determined from the kinematic variables. A posture instruction can thus be generated based on a result of this analysis, in order to adapt, correct and / or improve the posture of the subject.

[0066] The invention, according to its various embodiments, thus implements a processing method, implemented by a processing device, for tracking the posture of a subject, said method comprising: (a) obtaining sensor data representative of the subject; b) generation, from the sensor data, of a model defining anatomical points of the subject over time; c) determination, from the model, of kinematic variables representative of the evolution of the subject's posture over time; (d) determination, from the kinematic variables, of at least one non-linear indicator of variability representative of a temporal structure of the kinematic variables; e) analysis of said at least one non-linear indicator of variability by comparison with a respective reference value; and f) generation of a posture instruction based on a result of the analysis.

[0067] The invention, according to its various embodiments, also relates to a computer program, and an information medium containing such a program, for executing the steps of the processing method according to the invention.

[0068] Other aspects and advantages of the present invention will emerge from the exemplary embodiments described below with reference to the drawings mentioned above.

[0069] [Fig.l] schematically represents a processing device T1 (also called a tracking device or device) capable of cooperating with at least one associated device 20, also called a control device, to carry out posture tracking of a subject denoted UR1 of the invention according to particular embodiments. The processing device T1 and said at least one control device 20 together form a processing system (or tracking system) denoted SY1.

[0070] It is assumed by way of example that the associated device(s) 20 are distinct from the processing device T1 although variants are possible in which the associated device(s) are part of the processing device TL.

[0071] By way of example, it is subsequently assumed that the processing device T1, also called device, is configured to monitor the posture of the user UR1 in a context which may vary depending on the case. The subject UR1 may be, for example, an athlete, an injured or convalescing person, or more generally any person whose posture (or movement) is to be evaluated and improved, for the purposes, for example, of assisting with physical maintenance, improving health, preventing injuries, playing and / or performance.

[0072] As illustrated in [Fig.l], the processing device T1 may use at least one posture sensor (or motion sensor) 12 to obtain DTI sensor data representative of the subject UR1, or more precisely, of the PSI posture of all or part of the subject URL. The sensor(s) 12 may be distinct from the device T1 or be part of the latter as the case may be. The nature of the DTI sensor data recovered by the processing device T1 may vary as the case may be, depending in particular on the type of sensor 12 used. Examples of such sensors, denoted 12a, 12b and 12c, are described below.

[0073] According to one example, the processing device T1 is configured to receive, from at least one image acquisition device 12a, image data DT1a representative of the posture PSI of the subject URL

[0074] According to one example, the processing device T1 is configured to receive, from at least one inertial sensor 12b positioned on the subject UR1, inertial data DTlb representative of the posture PSI of the subject URL.

[0075] According to one example, the processing device T1 is configured to receive, from at least one 12c EMG type sensor (for “electromyogram”) positioned on the subject UR1, EMG DTlc data representative of the PSI posture of the subject UR1.

[0076] The processing device T1 may thus be configured to receive, as DTI sensor data, at least one of the image data DT1a, the inertial data DT1b and the EMG data DT1c as previously described, or any combination of at least two of these types of data. The configuration and use of these sensors 12a-12c, as well as the associated sensor data, are described in more detail in the following description of the tracking method according to various embodiments. The processing device T1 may be configured to receive and process in real time all or part of the DTI sensor data received from the sensor(s) 12.

[0077] According to one example, the processing device T1 is configured to obtain all or part of the DTI sensor data by accessing a memory (for example the memory 6) in which the data is stored. It is for example possible to record DTI sensor data and then configure the processing device T1 to retrieve and process this data later.

[0078] In the following, it is assumed that the DTI sensor data defines a PSI posture of the subject UR1 over time, which makes it possible in particular to analyze a movement or an evolution of the PSI posture over time, although variants are possible where the DTI sensor data only defines the PSI posture of the user UR1 at a given instant, for example to track the spatial positioning (or disposition) of the subject at a given instant.

[0079] As illustrated, the processing device T1 comprises in this example at least one processor 4 and a memory 6. This memory 6 may comprise various types of memory, in particular a volatile memory (of the RAM type) and a non-volatile memory. The non-volatile memory may comprise a read-only memory (of the ROM type) and / or a rewritable non-volatile memory. This memory 6 may comprise in particular an operating system 10 executable by the processor 4 to operate the processing device TL.

[0080] The memory 6 constitutes a recording medium (or information medium) according to particular embodiments, readable by the processing device T1, and on which is recorded a computer program PG1 conforming to various particular embodiments. This computer program PG1 comprises instructions for executing the steps of the tracking method of the invention according to particular embodiments. The steps of this method are represented, in a particular embodiment, in [Fig.4] described later.

[0081] Thus, the processor 4 is configured to execute the instructions of the computer program PG1 in order to carry out steps of the tracking method of the invention according to particular embodiments. For this purpose, the processor 4 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. In particular, the processor 4 may use a volatile memory internal to the processing device T1 (this memory may be part of the memory 6 or be a separate memory) to carry out the various operations and functions necessary for the operation of the processing device T1, including for executing the computer program PG1 when implementing the processing method of the invention.

[0082] As shown in [Fig.l], the memory 6 is capable of storing various data that may be used during the execution of the tracking method. Thus, the memory 6 can store a model ML1 defining anatomical points PT1 of the subject UR1. Positions PN1 can be predicted (or estimated) and stored for each anatomical point PT1 of the model ML1, possibly in association with respective SCI confidence scores. The memory 6 can also be used to store kinematic variables VI, one or more non-linear indicators of variability V2, and a reference value REF2. According to a particular example, the memory 6 can also be used to store a linear indicator of variability V3 and a reference value REF3. The nature and use of the aforementioned data are described in more detail later in particular examples.

[0083] The processing device T1 may have various forms depending on the case, and may in particular be a computer, a server, a smartphone, a tablet, or more generally a processing device comprising the means configured to carry out the steps of the monitoring method of the invention.

[0084] As shown in [Fig.l], the processing device T1 is further configured to cooperate with, or control, at least one control device 20. In particular, the processing device T1 is able to send a posture instruction CMD1 to one or more control devices 20. The number and type of control devices 20 used may vary depending on the case. A posture instruction CMD1 is a command (or instruction) intended to control a control device 20 in order to guide or assist the subject UR1 in the execution of a posture, a movement or a physical activity. By way of example, this posture instruction CMD1 makes it possible to adapt, correct and / or improve the posture PSI of the subject UR1 through the control device(s) 20.

[0085] The type of each posture instruction CMD1 depends in particular on the type of the control device 20 concerned as well as the objective sought. Examples of such control devices, denoted 20a, 20b, 20c and 20d ([Fig.l]), are described below.

[0086] According to an example, the processing device T1 is configured to send a first posture instruction CMD1 to a user interface 20a-20c to cause the rendering posture information IF1 specifying how to adapt, correct and / or improve the PSI posture of the subject UR1. As illustrated, this user interface 20 can take various forms, the manner in which the information IF1 is rendered depending on the type of user interface used.

[0087] According to one example, the processing device T1 is configured to send at least one first posture instruction, more precisely denoted CMD1a, to a display device 20a to cause the display of posture information IF1 (in visual form). According to one example, the processing device T1 can send at least one first posture instruction CMD1a to a sound device 20b to cause the emission of posture information IF1 in sound form. According to one example, the processing device T1 can send at least one first posture instruction CMD1a to a light device 20c to cause the emission of posture information IF1 in light form (for example by activating a light indicator or by adapting the color of an emitted light). Several user interfaces 20 among those described can also be used in combination.

[0088] By way of example, a control device 20 cooperating with the processing device T1 may be a smartphone, tablet, computer or equivalent, comprising a display device 20a as indicated above.

[0089] According to one example, the processing device T1 is configured to send at least one second posture instruction, more precisely denoted CMDlb, to a control device 20 taking the form of a movement assistance device 20d (also called a physical conditioning device). This second posture instruction CMDlb then causes a parameterization (or a configuration) of the movement assistance device 20d to adapt, correct and / or improve the posture PSI of the subject UR1.

[0090] The assistance device 20d may be any device configured, under the control of the processing device T1, to facilitate, guide and / or intensify the performance of postures, movements and / or physical activities by a subject or individual. For example, the assistance device 20d may be or include a weight training machine, a treadmill, an elliptical trainer, an exercise bike, a rowing machine, a multi-gym station, etc. This type of device is intended, for example, for improving cardiovascular fitness, muscle strengthening, weight loss, developing proprioception, or more generally for physical preparation or training or re-athleticization.

[0091] The assistance device 20d may be more or less complex depending on the case. In particular, it may be configurable according to at least one operating parameter (for example, a running speed of the belt for a treadmill, a resistance force for an exercise bike or an elliptical bike, a duration of a training program, etc.). The processing device T1 can thus control the confi configuration of such an assistance device 20d by sending a second posture instruction CMDlb.

[0092] According to one example, the posture sensor(s) 12 used to obtain the DTI sensor data are part of one or more control devices 20.

[0093] According to one example, the processing device T1 cooperates with at least one sensor 12 and at least one control device 20, these elements T1, 12 and 20 all being part of (or forming) the same device or system. By way of example, a terminal such as a smartphone or other may comprise the processing device T1, at least one sensor 12 and a display device 20a as previously described.

[0094] [Fig. 2] represents an exemplary embodiment of the system SY1 as described with reference to [Fig. 1]. In the example of [Fig. 2], a plurality of cameras 12a are arranged around the subject UR1 to capture the latter's posture PSI over time. In this case, the processing device T1 obtains or recovers image data DT1a comprising images representing the subject UR1 over time.

[0095] The processing device T1 can thus be configured to send at least one of a first posture instruction CMD1a and a second posture instruction CMD1b, or any combination of the different examples of posture instructions envisaged.

[0096] According to one example, the movement assistance apparatus 20d may comprise at least one of the user interfaces 20a-20c. In this case, a first posture instruction CMD1a and a second posture instruction CMD1b may be sent by the processing device T1 to cause both a restitution of information IF1 by the assistance apparatus 20d and the configuration of said assistance apparatus 20d.

[0097] The processing device T1 can be configured to generate and send one or more posture instructions CMD1 in real time to control the control device(s) 20. Alternatively, the posture instruction(s) CMD1 can be generated and sent during a phase subsequent to that of capturing the posture of the subject URL

[0098] It should be noted that the processing device T1 shown in [Fig.1] constitutes only an exemplary embodiment, other implementations being possible within the framework of the invention. Those skilled in the art will further understand that certain elements of the processing device T1 are described here only to facilitate understanding of the invention, implementations of the invention being possible without these elements.

[0099] As shown in [Fig.3] according to a particular embodiment, the processor 4 controlled by the computer program PG1 ([Fig.l]) implements a certain number of modules, namely: an MD2 obtaining module, a first MD4 generation module, a first MD6 determination module, a second MD7 determination module, a third MD8 determination module, a fourth MD9 determination module, a fourth MD10 determination module, a fourth MD11 determination module, a fourth MD12 determination module, a fourth MD13 determination module, a fourth MD14 determination module, a fourth MD15 determination module, a fourth MD16 determination module, a fourth MD17 determination module, a fourth MD18 determination module, a fourth MD19 determination module, a fourth MD20 determination module, a fourth MD21 determination module, a fourth MD22 determination module, a fourth MD23 determination module, a fourth MD24 determination module, a fourth MD25 determination module, a fourth MD26 determination module, a fourth MD27 determination module, a fourth MD28 determination module, a fourth MD29 determination module, a fourth MD30 MD8 mination, an MD10 analysis module and a second MD12 generation module.

[0100] More specifically, the MD2 obtaining module can be configured to obtain DTI sensor data representative of the subject UR1.

[0101] The first MD4 generation module can be configured to generate, from the DTI sensor data, an ML1 model defining anatomical points PT1 of the subject UR1 over time.

[0102] The first determination module MD6 can be configured to calculate, from the ML1 model, kinematic variables VI representative of the evolution of the PSI posture of the subject UR1 over time.

[0103] The second determination module MD8 can be configured to determine, from the kinematic variables VI, at least one non-linear indicator of variability V2 representative of a temporal structure of the kinematic variables VL

[0104] The analysis module MD10 can be configured to analyze said at least one non-linear indicator of variability V2 by comparison with a respective reference value REF2.

[0105] The second generation module MD12 can be configured to generate a posture instruction CMD1 based on a result of the analysis carried out by the analysis module MD 10.

[0106] The configuration and operation of the MD2-MD12 modules of the device T1 will appear more precisely in the exemplary embodiments described below.

[0107] The tracking method implemented by the processing device T1 as previously described with reference to Figures 1-2 is now described according to particular embodiment examples in conjunction with Figures 3-18. To do this, the device T1 performs steps S2-S12 ([Fig.4]) by executing the computer program PG1.

[0108] It is assumed by way of example that the processing device T1 is used to monitor and improve the posture PSI of the subject UR1, the latter being able to stand on any surface, or possibly, he can use a movement assistance device 20 as the case may be.

[0109] During an obtaining step S2, the device T1 obtains DTI sensor data representative of the subject URL. This DTI sensor data can be generated by various types of sensor 12 capable of capturing the PSI posture of the subject UR1 over time. The DTI sensor data can be received from one or more posture sensors 12 or retrieved from a memory, for example the memory 6 ([Fig.l]).

[0110] According to an example, the sensor data DTI received by the processing device T1 comprises image data (or video data) DT la representative of the subject URL. As an example, [Fig.5] illustrates the obtaining S2 of image data DT1a representing a subject UR1 performing a squatting movement, other postures being possible however. The image data DT1a may comprise images IG1 representing all or part of the subject UR1 over time. These image data DT1a may be generated by at least one image sensor 12a, i.e. at least one image acquisition device such as a camera or the like. The processing device T1 may obtain (receive, determine, etc.) these image data DT1a in various ways depending on the case.

[0111] Obtaining image data DTla during step S2 is advantageous in that it makes it possible to deduce therefrom an accurate or faithful representation of the posture PSI of all or part of the body of the subject URL. From image data DTla, a model of what is visible in the image can be generated, which provides a more complete representation than that provided by other types of sensor, such as those described below.

[0112] The use of a plurality of cameras 12a (as for example in [Fig.2]) advantageously makes it possible to capture the PSI posture of all or part of the subject UR1 in space over time. It is thus possible to obtain a detailed visual representation and minimize the risks of occultation of an area of ​​interest of the body of the subject URL. By using multiple cameras 12a positioned appropriately in space, it is possible to acquire images (or a video) capturing the part or parts of the subject UR1 that it is desired to follow and analyze, such as for example a joint (knee, ankle, etc.) or a limb (lower limb or upper limb). In particular, it is possible to use a plurality of cameras 12a to make a 3-dimensional (3D) acquisition of the subject URL.

[0113] According to an example illustrated in [Fig.6], the DTI sensor data received by the processing device T1 comprise inertial data DTlb representative of the subject UR1 over time. To do this, one or more inertial sensors 12b ([Fig.l]) may be positioned on the subject UR1 (on his body or his clothing) in order to acquire this inertial data, which is then received and processed by the processing device TL. This inertial data DTlb may comprise, for example, acceleration data 30 acquired by at least one accelerometer 12b and / or gyroscopic data 32 acquired by at least one gyroscope 12b. In other words, the inertial data DTlb are for example acquired by means of at least one of an accelerometer type sensor 12b and a gyroscope type sensor 12b, said at least one sensor being positioned on the subject URL

[0114] As illustrated in [Fig.6], at least one accelerometer 12b can be used to generate acceleration data 30, for example linear acceleration data representative of an acceleration along one axis or along several axes (for example along two axes X and Y, or along three axes X, Y and Z) as a function of time t. It is possible to use three accelerometers 12b to measure the acceleration in the space of anatomical points PT1 of the subject UR1 over time. Similarly, at least one gyroscope 12b can be used to generate gyroscopic data 32, for example angular velocity data representative of an angular velocity (for example in degrees per second) as a function of time t.

[0115] According to one example, the DTI sensor data received by the processing device T1 comprises EMG data DT1c (electromyogram data) representative of the subject UR1 over time. More specifically, this DT1c data defines an EMG activity of the subject UR1 over time. To do this, one or more EMG sensors 12c may be positioned on the subject UR1 in order to acquire this EMG data DT1c, which is then received and processed by the processing device T1.

[0116] Electromyography (EMG) makes it possible to measure electrical activity of muscles of the subject UR1. This electrical activity can be captured by means of electrodes (EMG sensors 20c) arranged in contact with the subject, for example surface electrodes placed on the skin or needle electrodes inserted into the muscle tissue of the subject. The DTlc EMG data thus collected can in particular make it possible to analyze the electrical response of muscles to nerve stimulation, providing relevant information on the PSI posture or the state of the subject UR1, and this for the monitoring and management of various physical or medical conditions.

[0117] According to one example, the DTI sensor data obtained (S2, [Fig.4]) by the processing device T1 comprises at least one of the sensor data DT1a, DT1b and DT1c described above, or any combination of at least two of these types of sensor data.

[0118] According to one example, inertial data DTlb and / or EMG data DTlc are obtained at S2 ([Fig.4]) in addition to the image data DTla in order to further improve the capture of the PSI posture of the subject URL. The sensor data DTlb-DTlc produced by the inertial and EMG type sensors 12b-12c may be useful for posture analysis but, unlike the image data DTla, provide a fragmentary representation of the state of the subject in certain localized areas. It may be necessary to position multiple sensors 12b and / or 12c on the subject to obtain a sufficiently complete representation of the area of ​​the body that is desired to be analyzed.

[0119] It is subsequently considered by way of example that image data DTla are obtained during the obtaining step S2 by means of a plurality of image capturing devices 12a (for example cameras), these data DTla being subsequently used during the tracking method as described below.

[0120] During a generation step S4 ([Fig.4]), the processing device T1 generates, from the DTI sensor data obtained in S2, a model ML1 of ending anatomical points (or parts) PT1 of subject UR1 over time. Figures 8 and 9 represent examples of ML1 models generated during generation step S4.

[0121] According to an example, the image data DTla produced by each camera 12a comprises images IG1 defining respective 2D views (or representations) of the subject UR1 over time. Thus, during the generation S4 ([Fig.4]), the processing device T1 estimates (or predicts) the position PN1 of anatomical points PT1 of the subject UR1 over time in the 2D views (image IG1) from each camera 12a. Each position prediction constitutes a hypothesis of the position at which a respective anatomical point PT1 is located. A 2D model (for example taking the form of a 2D skeleton as illustrated in FIGS. 8-9) can thus be generated from each image IG1 produced by a camera 12a. The device Tl can then generate a 3D ML1 model by triangularizing the estimated positions PN1 of the anatomical points PT1 in each 2D view (each image IG1). In other words, a 3D model is constructed from the 2D captures of each camera 12a.This 3D model allows the PSI posture of subject UR1 to be accurately represented in space.

[0122] Alternatively, it is possible to generate a two-dimensional, or even one-dimensional, ML1 model to represent the URL subject. A 1D or 2D model makes it possible to model the UR1 subject according to only one or two dimensions, which may be sufficient and advantageous in certain cases, for example due to the more limited need for resources than for the generation of a 3D model.

[0123] The anatomical points PT1 used in S4 to construct the ML1 model can be adapted as appropriate depending on the posture to be analyzed and the desired objective. These anatomical points PT1 can, for example, define joints (elbow, knee, ankle, etc.) and / or characteristic points of the subject's body (for example, elements of the face or different parts of a limb).

[0124] Prediction of the positions PN1 of anatomical points PT1 can be performed by executing a prediction algorithm from the images IG1 defined in the image data DTla. This algorithm can use a prediction model based on artificial intelligence to accurately estimate the position PN1 of each anatomical point PT1 of interest.

[0125] The prediction of the PN1 positions can also take into account the relative position of the cameras 12a with respect to each other. A calibration step may be necessary prior to the obtaining steps S2-S4 to guarantee satisfactory accuracy of the prediction of the PN1 positions, and therefore the generation of a quality ML1 model.

[0126] We can thus construct in S4 ([Fig.4]) a 3D ML1 model presented for example in the form of a 3D skeleton representative of the PSI posture of the subject as shown in Figures 8-9. This ML1 model can be used to model the evolution of the respective PN1 position of the PT1 anatomical points over time.

[0127] According to an example, the device T1 assigns, to each estimated position PN1 of the anatomical points PT1 of the subject UR1 in the 2D views, a respective SCI confidence score which is representative of a level of confidence in said estimated position PN1. In other words, a respective SCI confidence score is associated with each position estimate PN1. The model ML1 is then generated (S4, [Fig.4]) by taking into account the SCI confidence scores. In particular, the model ML1 can be generated (S4) from the estimated positions PN1 of the anatomical points PT1 among which are excluded, by a first filtering, each estimated position PN1 of an anatomical point PT1 whose SCI confidence score does not reach at least a first threshold value SL1. In other words, this filtering based on SCI confidence scores makes it possible to generate a good quality ML1 model using only the estimated PN1 positions whose confidence score reaches at least the first SL1 threshold value.This SL1 value can be adapted on a case-by-case basis. By avoiding taking into account the estimated PN1 positions whose reliability is considered insufficient, it is thus possible to advantageously generate a precise and reliable ML1 model.

[0128] It may be preferable to reach a compromise by limiting the number of anatomical points in the ML1 model to keep only the most reliable points to ensure good model quality. If a PT1 anatomical point has an insufficient confidence score in all 2D views, for example because it is not visible by any camera, this point is then absent (excluded) from the final 3D model (partial model).

[0129] It may indeed be difficult for a camera 12a to capture all the parts of the subject corresponding to the anatomical points PT1 of interest. In certain cases, problems of annoying reflection or even limited image quality may make it difficult to detect an anatomical point PT1 from an image IG1. The greater the number of cameras 12a used, the lower the risk of occultation but the greater the cost in resources and complexity of implementation. The prediction of the PN1 positions advantageously makes it possible to construct a quality ML1 model regardless of the posture of the subject UR1, including in the case where anatomical parts PT1 of the subject UR1 are occulted or difficult to identify in certain IGL images. However, the quality of the position predictions varies depending on different factors.Depending on the number and position of the 12a cameras, some PT1 anatomical parts may not be visible in the 2D views produced by the 12a cameras, which may result in more or less reliable estimates of certain anatomical points. Taking into account the SCI confidence score as previously described makes it possible to generate a complete and good quality ML1 model.

[0130] According to an example, to generate the model ML1 (S4, [Fig.4]), the device T1 may retain, for a given anatomical point PT1 at a given instant, only a single position estimate PN1 from the 2D view of a respective camera 12a, i.e. the best estimate among those available, in order to further improve the quality of the model ML1. For example, if a first camera 12a visualizes an anatomical point PT1 better than a second camera 12a at a given instant (in the event of occultation for example), it may be preferable to retain only the estimated position PN1 obtained from the image IG1 of the first camera 12a.

[0131] According to an example, during the generation S4 ([Fig.4]), the processing device T1 compares first and second SCI confidence scores associated respectively with first and second estimated positions PN1 for the same anatomical point PT1 in 2D views from the cameras 12a. The model ML1 is then generated from the estimated positions PN1 of the anatomical points PT1 to which a second filtering is applied according to which, if the difference between said first and second SCI confidence scores reaches at least a second threshold value SL2, the one among the first and second estimated positions PN1 having the lowest confidence score is excluded (by taking it into account to generate the model ML1). In other words, only the estimated position PN1 with the highest SCI confidence score is used to generate the model ML1. This second filtering can be repeated over time to improve the quality of the model ML1.

[0132] It is thus possible to compare the respective SCI confidence scores obtained for position estimates PN1 of the same anatomical point PT1 (representing for example a knee or other) in several 2D views, or in several 2D skeletons from several cameras 12a, and to eliminate the estimated position PN1 having the lowest confidence score, but only in the event of a strong disparity between the respective SCI confidence scores, which may indicate a significant risk of error. In this way, it is possible to maximize the number of position estimates PN1 taken into account during the generation S4 and thus further improve the quality of the model ML1. The second threshold value SL1 can for example be defined in the model of the prediction algorithm used to generate the model ML1.

[0133] During a determination or calculation step S6 ([Fig.4]), the processing device T1 determines (or calculates), from the model ML1 generated in S4, kinematic variables (or data) VI representative of the evolution of the PSI posture of the subject UR1 over time. The number and type of the kinematic variables VI determined in S6 can be adapted as appropriate. Generally speaking, the kinematic variables VI characterize the dynamics of the PSI posture of the subject UR1 over time.

[0134] According to one example, the kinematic variables VI determined in S6 comprise at least one of velocity data and acceleration data calculated from the estimated positions PN1 of the anatomical points PT1 of the model ML1 over time. In other words, the kinematic variables VI can characterize velocities and / or accelerations of points of the model ML1, for example anatomical points PT1. These can be angular and / or translational velocities and accelerations as appropriate.

[0135] A kinematic variable V1 can thus define, for example, a speed component or an acceleration component along a given direction, for example along an X, Y or Z axis.

[0136] According to one example, the kinematic variables VI determined in S6 comprise at least any one of the following variables, or a combination of at least two of these variables: a position, an angle, a speed and an acceleration. In other words, the kinematic variables V1 can characterize positions, angles, speeds and / or accelerations of points of the model ML1, for example anatomical points PT1. These can be, for example, angular or translational positions as the case may be, or even angular and / or translational speeds and accelerations as the case may be.

[0137] According to an example, at least a portion of the kinematic variables VI determined in S6 is DTI sensor data obtained in S2. In other words, it is possible to determine a kinematic variable VI from a DTI sensor data without any intermediate processing, using the model ML1 generated in S4. Alternatively, a kinematic variable V1 is determined in S6 by processing from the DTI sensor data defined in the model ML1.

[0138] As an illustration, [Fig. 10] schematically represents kinematic variables VI calculated in S6 over time, these variables representing a displacement (in meters) of a center of mass of the ML1 model in space. In this example, the three curves represent translational movement components of the center of mass in 3 dimensions, namely along axes X, Y and Z respectively (lateral, vertical and anteroposterior plane). In the example considered, the center of mass corresponds to the pelvis of subject UR1 while the latter performs a squat movement. These kinematic variables Via show in this case that: - the movement of the center of mass along Z is negligible over time t which means that the subject UR1 is centered laterally; - the center of mass of the subject UR1 moves along Y over time t between a reference position “0” and a low position corresponding to - 0.7 meters; and - during the squat movement, the center of mass moves back slightly (by 0.1 meters) along X due to the backward movement of the buttocks of subject UR1 (slight backward movement) then returns to its initial position along X.

[0139] As an illustration, [Fig.l 1] schematically represents a kinematic variable VI determined in S6 over time, this variable representing an angular displacement (in degrees) in a given direction of a joint, namely a knee of the subject UR1. In this example, the curve therefore represents the evolution of an angular movement component as a function of time (according to a percentage of a walking cycle) while the subject UR1 performs a walking movement. As this figure shows, the evolution of the knee flexion angle has two peaks during a complete walking cycle. Indeed, at the start of the cycle, the heel touches the ground, then the knee passes under the subject's body (1st angular peak).Following this first flexion, the knee extends into a posterior position (almost zero flexion) before reaching a second, larger angular peak during which the subject's toe leaves the ground and the knee is brought forward until the knee returns to the initial position.

[0140] During a determination step S8 ([Fig.4]), the processing device T1 determines, from the kinematic variables V1 obtained in S6, at least one non-linear variability indicator V2 representative of a temporal structure of the kinematic variables VI (or of at least one variable VI among those previously calculated in S6). One or a plurality of these indicators V2 can be determined as the case may be.

[0141] A nonlinear indicator of variability V2 is a statistical or mathematical indicator that characterizes in a complex way how one or more indicators of kinematics VI vary over time. Unlike a linear indicator that would assume proportional and constant relationships, a nonlinear indicator of variability captures more complex dynamics such as chaos, nonproportional interdependence, or emergent behaviors. These nonlinear indicators may include, for example, sampling entropy, the Lyapunov exponent, the fractal dimension, and other methods from chaos theory and dynamical systems. These indicators have been found to be particularly effective for analyzing time series or data where traditional linear models fail to capture the entirety of the underlying dynamics, thus providing a deeper understanding of the systems under study.

[0142] According to one example, each kinematic variable VI defines a component of movement (for example a position, speed or acceleration, angular or in translation) in one dimension over time. The device T1 thus determines in S8 ([Fig.4]) at least one non-linear indicator of variability V2 by projection of the kinematic variables VI into a space of dimensions N, where N >1. From this or these indicators V2, it is advantageous to reconstruct kinematic variables in a space of dimension N (N>1).

[0143] For illustrative purposes, [Fig. 12] includes different representations C1-C6 of the evolution of signals over time as well as the respective value of a Liapunov exponent representative of the variability of the signal considered. This example shows several signals with different signal temporal structures (periodic, non-periodic or random). Signals C1, C3 and C5 on the one hand, and signals C2, C4 and C6 on the other hand, vary in the same range of values ​​(30 and 80, respectively) and have the same average (equal to 0) for both groups. However, their temporal structures may or may not differ within the same group, which is reflected in the values ​​of the Liapunov exponents LyE indicated by the reference sign 34 (varying between -0.001 and 0.564 in this example).For example, although varying in different ranges of values, signals C1 and C2 have the same value of the Liapunov exponent because they are characterized by the same temporal structure (purely periodic signals, LyE = 0). Signals C3 and C4 (chaotic) each have a certain degree of repetition (repetition of a variation pattern), which is reflected by an increase in their Liapunov exponent LyE values ​​(close to 0.1). Signals C5 and C6 are completely random so that each point in time is uncorrelated with respect to other points, which results in even higher Liapunov exponent LyE values ​​(close to 0.5).

[0144] By way of illustration, [Fig.13] illustrates other examples of signals (A), (B) and (C) (on the left) representative of kinematic variables V1 according to one dimension and respectively periodic, chaotic and random, as well as their corresponding spatial representations in a 3-dimensional space.

[0145] [Fig. 14] takes the right column of [Fig. 13] in 2 dimensions to place the elements on a plane representing the predictability (x axis) and the complexity (y axis) of the signal. It appears that the random signals (C) and periodic signals (A) are respectively little and very predictable although weakly complex, unlike the chaotic signals (B) which are complex with a variable predictability lying between the first two.

[0146] According to one example, the non-linear variability indicator(s) V2 determined in S8 ([Fig.4]) comprise at least one of: - an entropy value or an entropic indicator; - an exponent of Lyapunov; - a result of a fractal analysis (for example a fractal analysis value or statistical self-affinity value or a DFA rectified fluctuation analysis in English); - a surogate indicator; - a geometric approach indicator, for example UCM for “Uncontrolled Manifold”, TNC for “Tolerance-Noise-Covariation”, MIP for “Minimum Intervention Principle” or GEM for “Goal Equivalent Manifold”.

[0147] Each type of nonlinear variability indicator provides a type of information on how the V1 kinematic data vary over time. In particular, they can provide information on the regularity or predictability of a movement.

[0148] Entropy is a mathematical tool for quantifying the regularity and unpredictability of fluctuations in time data series. In practice, the presence of repetitive fluctuation patterns in a time series makes it more predictable than a time series in which such patterns are absent. Entropy thus reflects the probability that similar observations are not followed by other similar observations. A time series containing many repetitive patterns has a relatively low entropy; on the contrary, a less predictable process has a higher entropy. Using an entropy value advantageously makes it possible to characterize a level of disorganization, or unpredictability, in the variations of the kinematic variables VI, which advantageously makes it possible to identify relevant posture changes and thus improve the subject's posture analysis.

[0149] As an illustration, [Fig. 15] represents an example in which an approximate entropy indicator V2-1 (also called ApEn or "approximate entropy" in English) is determined in S8 ([Fig.4]) as a non-linear indicator of variability V2 from a time series of angular flexion of the knee VL. As indicated in (A), the time series is first divided into short vectors of similar length m. One of these vectors is represented in [Fig.l5](A) by the arrow between the points u(44) and u(45). Then, for each vector thus determined, the processing device T1 determines the number of other vectors which are similar to said vector considered. Vectors are considered similar to the original vector when their tails and tips are contained within a band of width r above and below those of the original vector, here u(44) ± r and u(45) ± r.The vectors that were found to be similar to the original vector are represented by the other arrows. As illustrated in [Fig.l5](B), this operation can then be repeated for vectors that are one unit longer than the shorter ones (e.g. for a vector extending between u44 and u46 as illustrated in 15(B)), i.e. of length m+1. The ApEn is then obtained by calculating the natural logarithm of the relative prevalence of repeating motifs of length m compared to those of length m+1.

[0150] The largest Liapunov exponent, also called simply the "Liapunov exponent", is a measure of the sensitivity to initial conditions in a dynamical system. It can be used to quantify the chaotic nature of a system nonlinear dynamics. It measures how the trajectories of a dynamic system diverge from slightly different initial conditions. In a chaotic system, small initial variations can lead to very different trajectories, which means that the system is sensitive to the initial conditions. Using a Lyapunov exponent (or function) advantageously allows estimating the stability of an equilibrium point, i.e., the stability of the kinematic variables VI.

[0151] To calculate the Liapunov exponent, two initial trajectories that are very close in state space (usually, very close points in the time series) can be selected. These trajectories can then be tracked over time. The Liapunov exponent is then calculated as the limit (e.g., by taking the average over all possible trajectories) of the exponential growth of the deviations between these trajectories. As a result: a positive Liapunov exponent indicates sensitivity to initial conditions and suggests that the system is chaotic. In this case, small initial perturbations can lead to exponential divergences of the trajectories, making long-term prediction difficult. A zero exponent indicates that the trajectories remain close over time, meaning that the system is stable and predictable.Conversely, a negative exponent indicates that the trajectories converge over time, meaning that the system is stable and tends to return to an equilibrium state. The result RS1 determined in S10 ([Fig.4]) by the processing device T1 can therefore include an indication of the sign of the obtained Liapunov exponent, or an indication of whether the obtained Liapunov exponent is positive (> 0), negative (> 0) or zero (= 0).

[0152] DFA (for "Detrendred Fluctuation Analysis") is a fractal analysis technique that quantifies the fluctuations in a time series. This technique includes in particular the decomposition of a signal into small time scales, then the calculation of the relationship between the fluctuations and the size of the time scales. The alpha coefficient of the DFA can then be used to assess the presence of fractality in a time series. An alpha coefficient value between 0.5 and 1 indicates positive autocorrelation (or persistence) in the time series, which suggests a fractal structure. The closer the alpha value is to 1, the more the series is considered fractal and therefore presents similarities or repetitive patterns.In practice, an autocorrelated time series is characterized by a positive correlation between successive values, i.e., high values ​​are likely to be followed by high values ​​and low values ​​by low values. Conversely, an alpha coefficient value between 0 and 0.5 indicates anti-correlation (or anti-persistence) in the time series. This is characterized by an inverse relationship between successive values, i.e., when the value . previous value is high, it is likely that the next value is low and vice versa.

[0153] GEM is a method, or indicator, that allows a movement to be represented in a multidimensional space. This indicator allows, after defining a task objective, to see how one or more kinematic variables are associated with the success of this objective by looking at the "good" variability (beneficial to the success of the task objective) and the "bad" variability (leading to a deterioration in performance).

[0154] For illustrative purposes, [Fig. 16] shows an example in which a GEM indicator V2-2 is determined in S8 ([Fig.4]) as a non-linear indicator of variability V2. Part (A) of [Fig. 16] shows the GEM indicator for maintaining a constant walking speed (v). The central point represents the preferred average operating point. Each triangle represents a combination of stride time and stride length. The points that lie exactly on the diagonal line of the GEM indicator have reached the same speed and satisfy the objective, namely in this example maintaining a constant walking speed v. Part (B) of [Fig. 16] shows the time series of the deviations ôT (deviation tangential to the GEM) and ôP (deviation perpendicular to the GEM) for the data set shown in (A).

[0155] During an analysis step S10 ([Fig.4]), the processing device T1 analyzes said at least one non-linear variability indicator V2 (determined in S8) by comparing it with a respective reference value REF2. A result RS1 is produced from this analysis S10.

[0156] According to an example, the device T1 determines a single non-linear indicator of variability V2 in S8 ([Fig.4]) and compares this to a reference value REF2 to determine the result RS1.

[0157] Alternatively, the device T1 determines a plurality of non-linear indicators of variability V2 in S8 ([Fig.4]) and compares each of them to a respective reference value REF2 to obtain the result RS1. The use of a combination of non-linear indicators of variability V2 of different types can for example advantageously make it possible to better characterize the variability of the kinematic values ​​V1 in their complexity and thus improve the result RS1 of the analysis S10. Each non-linear indicator of variability V2 can give a type of information so as to obtain a more complete analysis result covering various aspects of the variability, such as the regularity and predictability of the posture, in view for example of a task objective.

[0158] According to an example, the device T1 verifies (S 10, [Fig.4]) whether said at least one non-linear variability indicator V2 determined in S8 complies with one (or at least one) conformity criterion CRI with respect to its respective reference value REF2. For example, it is verified whether a non-linear variability indicator V2 is greater than or less than its respective value REF2 or whether the difference between these two values ​​is greater than or less than a threshold value. The RS1 result determined in S10 is then a function of whether the conformity criterion is met or not.

[0159] According to an example, the verification of the conformity of a non-linear indicator of variability V2 to a plurality of conformity criteria CRI can be carried out in order to obtain the result RS1. By way of example, it can be verified whether a Liapounov exponent representative of the variability respects a plurality of conformity criteria CRI.

[0160] The result RS1 may be a function of whether a plurality of non-linear variability indicators V2 comply with a respective CRI conformity criterion with respect to a respective reference value REF2. In other words, the device T1 may carry out successive comparisons over time to verify that the evolution of a non-linear variability indicator V2 complies with CRI criteria over time.

[0161] As an example, the device T1 can check whether a Lyapunov exponent LyE determined in S8 [Fig.4]) as a non-linear indicator of variability V2 meets the following two CRI compliance criteria: (1) LyE > 0 (which indicates that the movement of the subject UR1 is not purely periodic or stereotyped) and (2) LyE < 0.5 (which indicates that the movement of the subject UR1 is not too random). It has indeed been observed that a too low LyE value suggests that the subject UR1 is not in good condition (injury, pathology, etc.) which induces stereotyped behavior.Conversely, a LyE value that is too high is not desirable since the random nature of a movement suggests certain disorders or disturbances in the subject URL. A large variability is necessary for an athlete to adapt to any situation in a sport, for example rugby, but excessive variability can occur in the case of uncontrollable or disorganized movements, which can result from a disorder. By checking the conformity of this Lyapunov exponent LyE to the two criteria above (compared to the reference values ​​REF2 equal to 0 and 0.5), we can thus advantageously ensure that the subject movement UR1 presents a satisfactory variability.

[0162] According to an example, the processing device T1 determines in S8 ([Fig.4]) a plurality of non-linear variability indicators V2, each of them being compared in S10 with a respective reference value REF2 to obtain the result RS1 of said analysis S10. It has notably been observed that the combination of an entropic indicator V2 in combination with a Liapunov exponent LyE makes it possible to effectively analyze the variability due to their complementarity. Indeed, these two indicators have different time series characteristics. For example, a signal may have great regularity (low entropy) but may have diverse values ​​in terms of sensitivity to the initial conditions (Liapunov exponent). These two indicators can therefore provide advantageous information on different and particularly complementary aspects (regularity and sensitivity to initial conditions) of the subject's posture, and thus further improve posture analysis.

[0163] According to an example, the processing device T1 determines the GEM indicator (step S8, [Fig.4]) which gives us access to the time series of deviations tangential and perpendicular to the task objective (as illustrated in [Fig. 16](B)). It is then possible to apply one or more non-linear analysis techniques, such as DFA, on these deviation time series. The GEM indicator provides information on the variability which is beneficial or detrimental to the success of the task objective, while the other non-linear analysis techniques provide relevant information on the temporal structure of the variability. It is thus possible to further improve the relevance of the posture analysis.

[0164] According to an example, a non-linear indicator V2 determined in step S8 ([Fig.4]) comprises a first sequence of values ​​over time. Thus, the device T1 can determine a first sequence of values ​​defining the evolution of a non-linear variability indicator V2 over time. In this case, the device T1 compares for example this first sequence of values ​​of said at least one non-linear indicator V2 over time with respectively a second sequence of reference values ​​REF2. Thus, the values ​​of this non-linear indicator V2 over time can be compared to respective reference values ​​REF2. The result RS1 of the analysis S10 can then be determined as a function of a result of the comparison of the first and second sequences.In other words, by comparing the values ​​of a non-linear variability indicator V2 with respective reference values ​​REF2 over time, it is advantageous to finely analyze the temporal structure of the kinematic data VI, and therefore the way in which they vary over time. During these comparisons, the device T1 can, for example, verify the conformity of each value of the first sequence with a respective conformity criterion CRI with respect to the corresponding reference value REF2.

[0165] As an example, the device T1 can verify whether a Lyapunov exponent LyE determined in S8 [Fig.4]) as a non-linear indicator of variability V2 increases over time, or even increases according to a minimal growth rate. It has been observed that an injured person typically exhibits stereotypical behavior with a low level of variability. Thus, one can verify, for example, whether a convalescing patient increases his LyE over time, which indicates an increase in variability, synonymous with recovery.

[0166] According to an example, during the tracking method, the processing device T1 also determines at least one linear indicator V3 of variability from the kinematic variables VI and compares this linear indicator of variability with a second reference value REF3. The RS1 result of S10 analysis is then also a function of this comparison. Taking into account at least one non-linear indicator V2 combined with at least one linear indicator V3 makes it possible to further improve the quality of the S10 analysis. Linear indicators can thus be used in addition to non-linear indicators to further improve the posture analysis.

[0167] According to a particular example, at least one non-linear variability indicator V2 is determined in S8 ([Fig.4]) and a result RS1 is determined in S10 from this or these indicators V2. The processing device T1 can then apply one or more linear analyses to the result RS1 (and vice versa). This result RS1 can then be used during step S12 ([Fig.4]) described below.

[0168] Still with reference to [Fig.4], during a generation step S12, the processing device T1 generates a posture instruction CMD1 as a function of the result RS1 of the analysis S10. This posture instruction CMD1 can then be sent to a control device 20 ([Fig.l]) to guide the subject UR1 in a posture, a movement or a physical activity. Sending such an instruction UR1 can for example advantageously make it possible to adapt, improve and / or correct the posture UR1 of the subject URL. The nature and the number of posture instructions CMD1 can vary depending on the case.

[0169] The posture instruction CMD1 is for example intended for the subject UR1 and / or any other user in order to assist the subject in his postures or movements. This instruction can for example be transmitted to the subject UR1 himself or to a professional to help him in his care of the subject UR1 (for example for the development of a training program). The posture instruction CMD1 can for example give an instruction so that the subject UR1 increases or decreases the intensity of a physical exercise, lengthens or shortens a physical exercise, or even adapts a posture or a movement.

[0170] According to an example, during the generation step S12, the device T1 restores (or renders), as a first posture instruction CMD1a, by means of a user interface, posture information IF1 specifying how to correct or improve the posture of the subject URL. In other words, the device T1 can send a first posture instruction CMD1a to a user interface to trigger the restitution, in an appropriate form, of posture information IF1 specifying an adaptation, correction or improvement of the posture of the subject URL. According to a variant, the posture information IF1 specifies a posture, other than the posture PSI, to be performed by the subject UR1, for example to improve or maintain his physical condition.

[0171] As already indicated, the posture information IF1 can be restored, under the control of the device T1, in various forms, the manner in which the information IF1 is restored being a function of the type of user interface used.

[0172] According to one example, the processing device T1 sends at least a first posture instruction, more precisely denoted CMD1a ([Fig.1]), to a display device 20a to cause the display of posture information IF1 (in visual form). According to one example, the processing device T1 can send a first posture instruction CMD1a to a sound device 20b to cause the emission of posture information IF1 in sound form (for example by beeps or via a synthesized voice). According to one example, the processing device T1 can send a first posture instruction CMD1a to a light device 20c to cause the emission of posture information IF1 in light form (for example by activating a light indicator or by adapting the color of an emitted light). Several user interfaces 20 among those described can also be used in combination.

[0173] By way of example, the processing device T1 can send a first instruction command CMD1a to cause the display of information IF1 comprising an indication to correct or improve the posture PSI of the subject UR1. The information IF1 can for example indicate to the subject UR1 and / or to a third party (a health professional or other) that a part of the body of the subject UR1 must be moved or repositioned in a given direction for correction purposes in order to accomplish a given task (for example a jump, a run, a squat movement, etc.). The information IF1 can for example be displayed on a screen or in holographic form.

[0174] [Fig. 17] represents a particular example of embodiment in which the device T1 sends (S12, [Fig.4]) at least one posture instruction CMD1a to cause the display of a first posture information IF1 on a screen (or display device) 20a and / or to cause the restitution of a second posture instruction IF1 in the form of light indications by means of light sources 20c arranged around the subject URL. The screen 20a and / or the light sources 20c can thus transmit instructions to the user UR1 so that he adapts, improves and / or corrects his posture PSL. The reference 24 in [Fig. 18] indicates for example the position of the feet of the subject UR1 in an initial default position.

[0175] [Fig. 18] represents an exemplary embodiment in which a screen 20a is used to display (S12, [Fig.4]) posture information IF1 and a treadmill system is configured (S12) according to a CFI configuration in response to a command CMD1 sent as a posture instruction.

[0176] According to an example, during the generation step S12 (figures 1 and 4), the device T1 sends, as a second posture instruction CMDlb, a command causing a parameterization (or a configuration) of a movement assistance device 20d (also called a physical conditioning device) to adapt, correct or improve the PSI posture of the subject. This command can trigger a setting to allow the realization of a posture other than the PSI posture.

[0177] As already indicated, the assistance device 20d may be any device configured, under the control of the processing device T1, to facilitate, guide and / or intensify the performance of postures, movements and / or physical activities by a subject or individual (weight training machine, treadmill, elliptical bike, exercise bike, rowing machine, multi-gym station, etc.). The assistance device 20d may be more or less complex depending on the case. In particular, it may be configurable according to at least one operating parameter (for example a running speed of the treadmill for a treadmill, a resistance force for an exercise bike or an elliptical bike, a duration of a training program, etc.). The processing device T1 may thus control the configuration of at least one parameter of the assistance device 20d by sending a second posture instruction CMDlb.

[0178] By way of example, the device T1 sends in S12 (figures 1 and 4) a posture instruction CMDlb to a device 20d of the treadmill type to adapt an operating parameter such as the speed of movement of the treadmill or an angle of inclination of the treadmill.

[0179] As already indicated, the device T1 can check in S10 ([Fig.4]) whether a non-linear variability indicator V2 determined in S8 complies with a conformity criterion CRI with respect to its respective reference value REF2. According to an example, if the conformity criterion is not complied with, the posture instruction generated in S12 ([Fig.4]) is configured to correct the posture PSI of the subject. In other words, the posture instruction PSI specifies a posture correction to be carried out in the form of posture information IF1 and / or a configuration command CMD1b.

[0180] The manner in which the PSI posture must be corrected may be determined in various ways by the device T1 depending on the case. According to one example, during the analysis S10 ([Fig.4]), the processing device T1 determines whether a non-linear variability indicator V2, called non-compliant indicator V2a, does not meet a CRI compliance criterion. If so, the device T1 determines at least one kinematic variable VI, called target kinematic variable Via, associated with the non-compliant indicator V2a and determines a correction to be applied to said at least one target kinematic variable Via to improve the non-compliant indicator V2a with respect to the CRI compliance criterion (i.e. with respect to the reference value REF2).In other words, the device T1 identifies at least one target kinematic variable Via on which to act to correct the non-compliant indicator V2a as well as a correction to be applied to this target kinematic variable Via in order to improve the non-compliant indicator V2a according to the CRI compliance criterion considered. The posture instruction CMD1 generated in S12 ([Fig.4]) can then be a function of the . correction to be applied to said at least one target kinematic variable Via to correct the posture of the subject UR1.

[0181] The non-compliant indicator V2a can in fact be linked to a plurality of kinematic variables VI. It is then appropriate to identify the kinematic variable(s) VI on which it is necessary to act to correct the non-compliant indicator V2a according to the conformity criterion CRI and thus improve the PSI posture of the subject URL

[0182] According to one example, during the analysis step S10 ([Fig.4]), the processing device T1 determines said at least one target kinematic variable Via as well as the correction to be applied to it by an optimization method (for example of the “optimal control” type) taking as input: - a non-compliant indicator trend objective V2a (by an increase or decrease objective, or an objective of reaching a target value or a range of values); - a function defining a relationship of the non-compliant indicator V2a with a plurality of associated kinematic variables V2; and - constraint data defining movement constraints that the ML1 model must respect.

[0183] According to an example, the processing device T1 determines a GEM indicator as a non-linear variability indicator V2 as previously described. The trend objective can therefore aim to minimize the “bad” variability (variability detrimental to the success of the task objective) because it is deemed too high (non-compliant indicator). The definition of the GEM provides information on the correlation (relationship function) of this indicator with several kinematic variables VI, such as the speed and position of anatomical parts of the subject. The constraint data to be respected can include, for example, biomechanical rules, such as angular amplitudes or movement speeds that the human body cannot (or must not) exceed. By combining these three elements in an optimization tool, it is advantageously possible to obtain optimal posture options respecting these criteria and thus to generate a relevant posture instruction.

[0184] The optimization method thus used in S10 ([Fig.4]) can output setpoint data defining said at least one target kinematic variable Via and the correction to be applied to it to modify the non-compliant indicator V2a according to the trend objective to reach or tend towards the trend objective. The posture setpoint CMD1 can then be generated in S12 ([Fig.4]) from the setpoint data thus determined.

[0185] According to an example, the trend objective provides for example the maximization, or minimization, of the non-compliant indicator V2a or the modification of this indicator so that it is included in a range of values. The function defining a relationship of the non-compliant indicator V2a with a plurality of associated kinematic variables V2 can result or be deduced from the very definition of the non-compliant indicator V2a. Finally, the constraint data taken into account during the optimization method can vary depending on the case: they can for example define movements that certain parts of the subject's body UR1 or certain of its anatomical points PT1 cannot perform (for example an angular displacement beyond which a knee cannot move, etc.). It is thus possible to effectively correct a non-compliant indicator V2a and thus advantageously improve a posture or a movement. By correcting a posture to tend towards an objective, it is possible to advantageously help the user UR1 in his posture or his movement.

[0186] According to an example, prior to the calculation step S6 ([Fig.4]), the processing device T1 performs a recognition, from the model ML1 generated in S4, of a movement made by the subject UR1. This recognition of the movement can be carried out for example by means of a prediction model based on artificial intelligence. The device T1 then selects, as a function of the recognized movement, kinematic variables VI to be calculated during the calculation step S6.

[0187] Indeed, it may be useful not to calculate the same kinematic variables V1 according to the posture or movement performed by the user URL. In other words, the relevant kinematic variables may differ according to the posture considered. We can thus adapt (or select) the kinematic variables VI that we calculate in S6 according to the situation in which the subject URL is placed. By calculating only the relevant kinematic variables, we can advantageously avoid unnecessary processing, obtain savings in resources (time, processing) and improve the performance of the tracking method.

[0188] According to an example, the device T1 further determines a task objective to be performed and selects the kinematic variables VI to be calculated based on both the recognized movement and the task objective, in order to further improve the performance of the method.

[0189] Generally speaking, the present invention advantageously makes it possible to monitor and adapt the posture of a subject in a precise and reliable manner. In particular, it is possible to evaluate the quality of a posture, static or dynamic, of a subject over time and to guide or improve this posture by generating a posture instruction adapted to the behavior of the subject. Such monitoring makes it possible in particular to evaluate and improve the health of the subject and / or his performance, and this for various purposes depending on the case, such as for example for assistance with physical activity, physical preparation, play, physical rehabilitation and / or re-athleticization. The invention further allows precise and reliable monitoring of the posture, static or dynamic, of a subject and this with a cost and limited implementation complexity. The present invention may also provide diagnostic assistance.

[0190] It has indeed been observed that if a person is injured, blocked or presents certain troubles or disorders, he or she may exhibit stereotypical behavior which results in a low level of variability of his or her kinematic variables. Conversely, excessive variability of the kinematic variables suggests other troubles or disorders which result in disorganized or uncontrollable movements. In theory, a relatively large variability of the kinematic variables is desirable to allow the subject to adapt to all situations during physical exercise, but within certain limits in order to maintain good control of the movements.

[0191] By analyzing at least one non-linear indicator of variability based on kinematic variables, it is advantageous to evaluate in detail and reliably the quality of a subject's movement in order to take into account complex aspects that are not perceptible on the sole basis of kinematic variables. Depending on the result of this analysis, a posture instruction can be advantageously generated, for example to adapt, improve or correct the subject's posture and thus help to improve the subject's health and / or performance.

[0192] The invention therefore offers a high-performance tool, both to movement professionals such as health and / or sports professionals (doctors, therapists, physiotherapists, physical trainers, sports coaches / trainers, etc.) and to the general public, for analyzing and improving the posture of a subject.

[0193] As understood by a person skilled in the art, all the embodiments and variants described above, some of which have been deliberately simplified to facilitate explanations, constitute only non-limiting examples of implementation of the present disclosure. In particular, a person skilled in the art may envisage any adaptation or combination of the embodiments and variants described above, in order to meet a particular need.

[0194] The present invention is therefore not limited to the exemplary embodiments described above but extends in particular to a monitoring method which would include secondary steps without thereby departing from the scope of the present invention. The same would apply to a processing device, or more generally to a processing system, for implementing such a method.

Claims

Claims

1. Processing method, implemented by a processing device (Tl), for tracking the posture of a subject (UR1), said method comprising: a) obtaining (S2) sensor data (DTI) representative of the subject; b) generating (S4), from the sensor data, a model (ML1) defining anatomical points (PT1) of the subject over time; c) determining (S6), from the model, kinematic variables (VI) representative of the evolution of the posture of the subject over time; d) determining (S8), from the kinematic variables, at least one non-linear variability indicator (V2) representative of a temporal structure of the kinematic variables (VI); e) analyzing (S 10) said at least one non-linear variability indicator by comparison with a respective reference value (REF2); and f) generation (S12) of a posture instruction (CMD1) based on a result of the analysis.

2. The method of claim 1, wherein the sensor data obtained in a) comprises image data (DTla) captured by a plurality of image capture devices (12a), the image data from each image capture device defining a respective 2D view of the subject over time; generation b) comprising: - estimating a position (PN1) of the anatomical points (PT1) of the subject over time in the 2D view from each image acquisition device; and - generating the 3D model by triangularizing the estimated positions of the anatomical points in each 2D view.

3. Method according to claim 2, in which the generation b) comprises: - attribution, to each estimated position (PN1) of the anatomical points of the subject in the 2D views, of a respective confidence score (SCI) representative of a level of confidence in said estimated position; the model (ML1) being generated from the estimated positions of the anatomical points among which are excluded, by a first filtering, each estimated position of an anatomical point whose confidence score does not reach at least a first threshold value (SL1).

4. Method according to claim 3, in which the generation b) of the model comprises: - comparison of first and second confidence scores associated respectively with first and second estimated positions for the same anatomical point in 2D views from the image acquisition devices; the model being generated from the estimated positions (PN1) of the anatomical points (PT1) to which a second filtering is applied according to which, if the difference between said first and second confidence scores reaches at least a second threshold value, the one among the first and second estimated positions having the lowest confidence score is excluded.

5. Method according to any one of the preceding claims, wherein said at least one non-linear indicator (V2) determined in d) comprises a first sequence of values ​​over time; the analysis e) comprising: - comparison of the first sequence of values ​​of said at least one non-linear indicator over time with respectively a second sequence of reference values; the result of the analysis e) being a function of a result of said comparison of the first and second sequences.

6. Method according to any one of the preceding claims, in which the generation f) comprises at least one of: - restitution, as a first posture instruction (CMDla), by means of a user interface (20a-20c), of posture information (IF 1 ) specifying how to correct the posture of the subject; and - sending, as a second posture instruction (CMDlb), of a command causing a parameterization of a movement assistance device (20d) to correct the posture of the subject.

7. Method according to any one of the preceding claims, wherein the analysis e) comprises: - checking whether said at least one non-linear variability indicator (V2) meets a conformity criterion (CRI) with respect to the respective reference value; wherein, if the conformity criterion is not met, the posture instruction generated in f) is configured to correct the posture of the subject.

8. Method according to claim 7 in which, if a non-linear indicator of variability (V2a) - called non-compliant indicator - does not comply not the conformity criterion (CRI), the analysis e) comprises: - determination of at least one kinematic variable (Via), called target kinematic variable, associated with the non-compliant indicator (V2a) and a correction to be applied to said at least one target kinematic variable to improve the non-compliant indicator compared to the reference value according to the conformity criterion; said posture instruction being generated in f) as a function of the correction to be applied to said at least one target kinematic variable to correct the posture of the subject.

9. Method according to claim 8, wherein the determination of said at least one target kinematic variable (Vla) and the correction to be applied is carried out by an optimization method taking as input: - a trend objective of the non-compliant indicator; - a function defining a relationship of the non-compliant indicator with a plurality of associated kinematic variables; and - constraint data defining movement constraints that the model must respect; the optimization method delivering as output setpoint data defining said at least one target kinematic variable and the correction to be applied to modify the non-compliant indicator according to the trend objective; the posture setpoint being generated in f) from the setpoint data.

10. Method according to any one of the preceding claims, in which the method comprises, prior to calculation c): - recognition, from the model, of a movement carried out by the subject; and - selection, as a function of the recognized movement, of the kinematic variables to be calculated in c).

11. Computer program (PG1) comprising instructions for executing the steps of a processing method according to any one of the preceding claims when said program is executed by a computer.

12. Processing device (Tl) for assisting a subject (UR1) in performing a posture (PSI), said device comprising: - obtaining module (MD2) configured to obtain sensor data (DTI) representative of the subject; - first generation module (MD4) configured to generate, from sensor data, from a model (ML1) defining anatomical points (PT1) of the subject over time; - first determination module (MD6) configured to calculate, from the model, kinematic variables (VI) representative of the evolution of the subject's posture over time; - second determination module (MD8) configured to determine, from the kinematic variables, at least one non-linear variability indicator (V2) representative of a temporal structure of the kinematic variables; - analysis module (MD10) configured to analyze said at least one non-linear indicator of variability by comparison with a respective reference value (REF2); and - second generation module (MD 12) configured to generate a posture instruction (CMD1) based on a result (RS1) of the analysis.

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