Classifications

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  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
  • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
  • A61B5/1107—Measuring contraction of parts of the body, e.g. organ, muscle
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  • A61B5/7271—Specific aspects of physiological measurement analysis
  • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
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  • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
  • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
  • A61B5/002—Monitoring the patient using a local or closed circuit, e.g. in a room or building
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  • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
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  • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
  • A61B5/1113—Local tracking of patients, e.g. in a hospital or private home
  • A61B5/1114—Tracking parts of the body
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  • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
  • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
  • A61B5/1118—Determining activity level
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  • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
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• • A—HUMAN NECESSITIES
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  • A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
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  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
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  • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
  • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
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  • A61B2562/0223—Magnetic field sensors

Definitions

• the present invention relates to a method for analyzing the biomechanical activity of a subject and exposure to a biomechanical risk factor in the context of physical activity. It also relates to a system implementing this method.
• the field of the invention covers in particular physical ergonomics and the ergonomic evaluation of workstations and physical assistance equipment (exoskeletons, cobots, robots).
• the methods used are essentially based on the analysis of the movement and postures of an operator.
• the prevention specialist or ergonomist observes the situation and fills out scoring grids according to criteria specific to each company. Some are based on a dated method called RULA (Right Upper Limb Assessment) widely used for lack of better: the observer evaluates the operator's posture at a key moment in the task by estimating the main joint angulations adopted.
• RULA Light Upper Limb Assessment
• monitoring the muscle activity of the human body is an important function in many applications related to health, sports and robotics.
• the characterization of the state of a muscle (such as fatigue), and the evolution of this state according to the movements of the whole body, can give valuable information on the condition of the muscle to, for example, optimize training for an athlete or adapting the physical workload for an industrial operator.
• recognizing the signs of muscle fatigue helps prevent the risk of injury from physical activity in a sporting or industrial context.
• EMG electromyography
• MMG mechanomyographic methods
• the vibratory activity is produced by the lateral oscillations of the muscle fibers which appear at the resonant frequency of the muscle [3]
• the MMG signal of a low frequency nature (2-250 Hz), is obtained by means of accelerometers , microphones, piezoelectric sensors or by laser. Scientific research shows that the analysis of MMG signals allows us to examine many characteristics of muscle function such as neuromuscular fatigue [4], the effectiveness of anesthesia [5], or certain syndromes neuromuscular like Parkinson's [6]
• mechano-myography [7]: acoustic or phonic myography (AMG, PMG) and vibratory myography (VMG).
• AMG acoustic or phonic myography
• VMG vibratory myography
• acoustic myography uses pressure sensors, microphones, or piezoelectric transducers while vibrational myography uses accelerometers almost exclusively.
• the preferred device of this invention uses MEMS capacitive accelerometers but can be extended to the other sensors mentioned.
• inertial units made up of three-axis accelerometers, three-axis rate gyros and three-axis magnetometers.
• the usual designations of inertial units are acronyms from English: Inertial Reference System (1RS), Inertial Navigation System (INS), or Inertial Measurement Unit (IMU).
• INS Inertial Navigation System
• IMU Inertial Measurement Unit
• the instruments used for capturing movements and postures can include optical systems without markers with, in particular, the emergence of so-called depth cameras. They make it possible to resolve the ambiguities inherent in monocular systems (segmentation, auto-occultations and ambiguities induced by planar projection) by directly providing depth images of the scene to estimate a person's posture.
• Another advantage of this type of camera is that the 3D scene information is provided from a single point of view [9]
• a limitation of depth cameras comes from their range of less than 5m. This limitation can be overcome by using multiple cameras, but a restrictive calibration or a very controlled environment becomes necessary. An understanding of muscle activity in correlation with whole body movements is therefore critical in contexts with high biomechanical stress outside controlled environments.
• MSDs musculoskeletal disorders
• biomechanical stresses are evaluated using three criteria: the intensity of the stress, the frequency of exposure to this stress and the duration of exposure.
• certain environmental factors can aggravate these biomechanical factors: mechanical pressures, shocks and impacts, vibrations and thermal environments.
• EMG electrodes The combination of EMG electrodes, MMG sensors and IMU sensors has already been the subject of several patents for various applications.
• document US20130317648A1 [10] relates to a sleeve integrating an EMG electrode array and an IMU unit for the recognition of movements intended for the control of machines or robotic systems.
• Document US 10292647 B1 discloses a portable device comprising a contraction sensor and a movement sensor and transmitting signals to a processor which analyzes the signals.
• the contraction signal determines whether the user's muscle is contracted or relaxed.
• the contraction and movement data is sent and viewed on the smart device screen along with video of the user performing movement. Simultaneous viewing of video and sensor data provides immediate feedback to the user regarding the timing of trunk contractions with body movements in an athletic, training, or therapeutic motion to allow that user to modify and modify '' improve the coordination of trunk contraction with body movements to improve movement performance and achieve better results.
• these systems do not allow an analysis of the vibratory behavior of a muscle of a subject aimed at producing indicators of muscle fatigue from portable and autonomous devices.
• the aim of the present invention is to remedy these drawbacks by proposing a new method for identifying biomechanical risks in an uncontrolled environment, based on objective measurements of the biomechanical factors of a subject, the degree and frequency of exposure of this subject to those same factors.
• the automation of this process via ultra-portable instrumentation, communicating the biomechanical data wirelessly, over long acquisition periods is also the objective of the invention.
• a system for analyzing the biomechanical activity of a human subject. and exposure to a biomechanical risk factor in the context of physical activity comprising: a. means for picking up vibratory signals, attached to one or more first body segments of the subject, the measurement of which reflects local muscle activity; b. means for sensing signals representing the movement of the subject, the measurement of which reflects the orientation and movement of one or more second body segments in 2 or 3 dimensions; vs. means for processing these signals so as to extract therefrom indicators representative of the intensity of the biomechanical stress,
• this analysis system further comprises: d. means for detecting a drift of the vibratory signals with respect to a frame of reference for the vibratory behavior of said muscle or muscles in the context of physical activity, e. means for predicting a physiological pause time necessary for the subject's muscles to recover their reference vibratory behavior.
• the invention also deals with a method for analyzing the biomechanical activity of a human subject subjected to physical exercise, and the exposure of one or more biomechanical risk factors such as:
• This method of analysis uses a measurement system, focused on one or a multitude of body segments of interest, in a context of deployment in an uncontrolled environment, comprising: a. capture of vibratory signals by a vibration sensor, attached to one or more first body segments of the subject, the measurement of which reflects local muscle activity; b. a capture of signals representing the movement of the subject, the measurement of which reflects the orientation and movement of one or more second body segments in 2 or 3 dimensions; vs. processing of these signals to extract indicators representative of the intensity of the biomechanical stress, this processing using data fusion techniques.
• this method further comprises: d. detection of a drift of the vibratory signals with respect to a frame of reference for the vibratory behavior of said muscle or muscles in the context of physical activity, e. a prediction of a physiological pause time necessary for the subject's muscles to recover their reference vibratory behavior.
• the fusion of MMG and IMU data has the potential to add another dimension to the analysis of human activity by creating a bridge between the dynamics of movement and muscle activity.
• recovery of joint mobility is based solely on the range of motion that the patient can produce at the time.
• Information on muscle activity can provide critical information on the progression of recovery from a physiological and biomechanical perspective.
• these muscle activity tests are done exclusively by EMG, in controlled environments and over very short periods of time.
• the identification method further comprises capturing the context or the scene in which the human subject is moving by optical capturing means, and processing this context or context information. scene, so as to generate information on the posture and gesture of the human subject in correlation with the signals of vibratory behavior.
• the posture information can be used to build up a classification of movements in a predetermined frame of reference.
• the identification method according to the invention can further comprise a cross analysis of the signals of movement and muscle activity, so as to deliver information on the performance and the health of the human subject during a physical activity in a context. work or sports activity.
• It can also include a fusion of movement data and data on muscle activity, so as to recommend a personalized arrangement of physiological breaks so that the muscle tissues of the human subject regain their rested state in the metabolic and mechanical sense after exertion.
• the means for picking up movement signals can advantageously comprise a MEMS technology inertial measurement unit IMU (Inertial Measurement Unit).
• IMU Inertial Measurement Unit
• the IMU inertial unit can be integrated together with the muscle activity sensor means to obtain co-localized measurements at the level of a body segment of the human subject.
• the IMU inertial unit can be of the six-axis type measuring rectilinear accelerations (three axes) and rotations (three axes).
• the means for capturing the movement signals may further include a magnetometer (three axes) to determine the orientation of the body segment with respect to the Earth's magnetic north.
• the means for capturing muscle activity can advantageously comprise an MMG (mechanical-myographic) accelerometer designed to generate a mechanical-myographic signal.
• MMG mechanical-myographic
• This sensor is ideally a high performance capacitive MEMS accelerometer derived from seismic prospecting [16].
• the analysis system can implement a plurality of measurement nodes firmly attached to body segments of a human subject.
• Each measurement node comprises means of communication with a receiving station which implement a communication protocol of the Bluetooth Low Energy (BLE) type.
• BLE Bluetooth Low Energy
• FIG. 1 illustrates the principle of the method according to the invention, producing information on the degree of exposure to biomechanical risk factors
• FIG. 2 illustrates an architecture of a measurement node implemented in the method according to the invention
• FIG. 3 is a block diagram illustrating the operations performed by the method according to the invention.
• FIG. 4 is an example of data recovery by the analysis method according to the invention.
• FIG. 5 is an illustration of another type of result produced by the device: the muscular effort and its drift over time are measured in real time during the person's activity and the calculation of the necessary rest time is provided to avoid injuries and prevent the risk of MSDs (after Baillargeon [17]);
• FIG. 6 is an illustration of the method for calculating a physiological pause time from the processed data of the device.
• the upper curve shows the evolution of the RMS amplitude obtained from the filtered MMG mechanomyographic signal, and reflecting the intensity of the biomechanical stress.
• the lower curve shows the average MPF power frequency obtained after mechanomyographic signal processing in the frequency domain. This curve reflects the strategy for recruiting muscle fibers.
• FIG. 7 illustrates an application of the invention for the ergonomic evaluation of work situations and equipment used by industrial operators.
• the method according to the invention is used for the ergonomic analysis of an exoskeleton supporting portable power equipment;
• FIG. 8 shows typical signals collected from industrial operators in the activity shown in Figure 7.
• the upper curve shows the angular amplitudes in flexion / extension of the right shoulder while the lower curve shows the muscle vibrations of the biceps. rights associated with movements produced;
• FIG. 10 illustrates another application of the invention for the sports field.
• a runner is equipped with the measuring system according to the invention at the level of the vastus right lateral;
• FIG. 11 shows the typical signals collected during a repetitive stride by a runner.
• the upper curve presents the angular amplitudes in flexion / extension of the right hip while the lower curve presents the muscular vibrations of the right vastus lateralis associated with the movements produced;
• FIG. 12 shows a time-frequency representation of the stride in a runner from the raw signals of Figure 11.
• the abscissa is the time
• Each vertical line represents a stride cycle with its frequency signature, that is, that is, the distribution of the energy of the signal over the frequency band of interest;
• FIG. 13 illustrates a particular example of the implementation of a process for obtaining a quality mechanomyographic signal (MMG) used for the present invention.
• FIG. 14 illustrates a particular example of the implementation of a step for extracting parameters of muscle activity.
• FIG. 1 The principle is illustrated in FIG. 1: the preferred use of the device integrates the IMU sensor and the muscle activity sensor in the same box or measurement node 1. However, these two sensors can be dissociated.
• the invention also includes an integration of the components directly within a garment, for example, in the form of a compressive band 2 serving to hold the sensor on the body segment, thus providing good mechanical coupling to detect muscle vibrations and limit measurement artefacts.
• the method according to the invention also extends to measurements of postures and movements using optical systems without markers such as depth cameras 3.
• This can prove to be an interesting alternative to IMU sensors in an uncontrolled environment. with a restricted movement space, or even offer a redundancy in the measurements in order to validate the postures and movements of the body while offering the elements of context in which the subject evolves (obstacles, objects, etc.).
• On-board signal processing electronics make it possible to perform certain calculation operations to facilitate wireless communication to an external receiver 4 (smartphone type).
• This receiver can then perform complex operations on the data and / or communicate them to a computer / server 5 via a mobile data network.
• the cross-analysis of movement and muscle activity data provides key information about a person's performance and health in their daily activity.
• the fusion of movement data and muscle activity allows in particular a personalized arrangement of physiological breaks so that muscle tissue returns to its rested state in the metabolic and mechanical sense after exertion.
• IMU sensor available on the market
• MEMS sensitive element for integration into a connected device (clothing or object)
• depth cameras for detecting movements and postures.
• the external IMU sensor can be chosen as a fully integrated IMU sensor for 3D motion capture, for example the MTw Awinda [18] motion tracker from the company XSENS.
• the specifications of this sensor are summarized in Table 1:
• a MEMS technology IMU inertial unit can be integrated together with the muscle activity sensor to obtain co-localized measurements at a given body segment.
• This unit can be six axes measuring rectilinear accelerations (three axes) and rotations (three axes).
• We can also associate a magnetometer (three axes) to determine the orientation of the body segment with respect to the Earth's magnetic North.
• Components from Invensense were selected for their performance and cost. Their characteristics are summarized in Table 2:
• the Microsoft® Kinect® is a low cost system consisting of a color camera (RGB), an infrared camera and an infrared projector. This system was used to capture the movements and postures of industrial operators by Plantard in [9] The characteristics of the Kinect VI and V2 are presented in table 3:
• the preferred use of the device integrates the IMU sensor and the MMG sensor in one housing.
• these two sensors can be separated, with one system for measuring posture and another for measuring muscle activity.
• the system according to the invention also extends to measurements of postures and movements using optical systems without markers such as depth cameras. This can prove to be an interesting alternative to IMU sensors in situations where the workstations are part of a well-defined environment, or to offer information on the context of the scene and redundancy in the measurements in order to validate the postures. and body movements.
• Electronics embedded in the sensors make it possible to perform certain calculation operations to facilitate wireless communication to an external receiver 4 (for example a smartphone or a data collector).
• This receiver can then perform complex operations on the data (synchronization, segmentation, processing) and communicate analysis results to a computer 5, a smartphone or a cloud.
• the results produced concern, for example, the level of exposure to certain biomechanical risk factors (postures, intensity of muscle activity) and the monitoring of these factors during physical activity.
• alerts may possibly be produced to warn the person when exposed to a demanding situation from a biomechanical point of view: - Too strong: intense muscle activity obtained via the mechanomyographic signal
• One embodiment of this invention provides a second level of analysis after post data processing.
• the movement and muscle activity data are processed on a PC by an algorithm that calculates the biomechanical efficiency of the subject’s gestures and characterizes their physiological and biomechanical impact on the body.
• Another result expected by the data fusion is the calculation of a physiological pause time so that the subject's muscles recover their reference vibratory behavior, thus avoiding exposure to the risks of accidents or occupational diseases.
• MEMS accelerometer ensuring the performance requirements for measuring MMG signals.
• the motion sensor can integrate:
• MEMS 9D inertial unit for joint integration with the MMG sensor.
• the data collector (receiver) can integrate:
• Merging inertial and mechanomyographic data can include:
• an algorithm for calculating the biomechanical efficiency from the data of the source file comprising the data from the synchronized movement and mechanomyographic sensors.
• the researchers For the conditioning of the mechanomyographic signal, under laboratory conditions, the researchers have generally oversampled the signals with a frequency of the order of 1 kHz or 2 kHz while the characteristic frequencies of the MMG signal are below 250 Hz. With a view to a field deployment with wireless data communication, a compromise between volume of data to be transmitted and sampling was found by fixing the sampling frequency at 500 Hz according to the Nyquist criterion.
• the raw signals from the accelerometer are then digitized and then conditioned.
• the digitized MMG signal has two components: a static component (DC) and a dynamic component (AC).
• the DC component is not useful for assessing muscle activity and therefore should be filtered.
• body movements are low frequency components that pollute the information of muscle activity.
• the cut-off frequency of the high pass filter is included in a band between 2 Hz and 50 Hz with a preference for 20 Hz in order to clean the parasitic components mentioned above.
• the application of a low-pass filter cuts high-frequency noise and limits the band of interest to frequencies characteristic of muscle micro-contractions.
• a preferred value of 250 Hz has been established.
• a five-pole Butterworth filter is ideal for the makes its gain constant in its passband despite a lower roll-off compared to Chebyshev or elliptical filters.
• the ADXL355 digital accelerometer offers programmable low-pass and high-pass filters to select the frequency band of interest.
• Mechanomyographic signal processing is based on the same developments as its electromyographic counterpart. We can divide the methods into four groups: the temporal and frequency methods (the most classic) then the time-frequency and time-scale methods (more recent). Choosing an appropriate processing method is therefore crucial for the objective analysis of MMG signals. Indeed, during an isometric contraction (contraction of a muscle without changing the length of the muscle), the signal can be assumed to be stationary (that is to say that its statistical properties are invariant over time) and the classical signal processing methods based on the Fourier transform are therefore applicable. However, during movements with varying dynamics, a muscle can change its length or recruit more motor units, giving rise to so-called non-stationary signals. In this type of muscle activity, the use of time-frequency or time-scale methods becomes necessary.
• the MMG signal has three components (MMGX, MMGY and MMGZ), representing the accelerations induced by the vibrations of the muscle fibers along the three directions of space (X, Y, Z).
• MMGX, MMGY and MMGZ representing the accelerations induced by the vibrations of the muscle fibers along the three directions of space (X, Y, Z).
• a “total” acceleration signal is calculated by the following operation:
• the RMS Root Mean Square amplitude of the “total” MMG signal then makes it possible to obtain information on the force developed by a muscle.
• N the observation window equal to the characteristic period of the movement sequence divided by 2.
• This characteristic period is defined by a cycle of the movement studied, for example, a step in the case of walking, a stride in the case of a run, or the period of handling an object.
• a window of 1 second makes it possible to establish an RMS amplitude with sufficient attributes on the physiological and biomechanical behavior of the underlying muscles.
• PSD power spectral density
• FFT Fast Fourier Transform
• AR autoregressive
• the mean frequency (MPF for Mean Power Frequency) can be determined by the following formula: with PSD in g ⁇ / Hz, the power spectral density of the MMG signal and fs in Hz, the sampling frequency. MPF is an important metric for examining changes in muscle condition and detecting characteristic signs of fatigue.
• the disadvantage of such a method is the selection of an adequate data range which can introduce a resolution defect in the frequency domain.
• One of the innovative features of this invention is the use of the orientation measurements of the IMU sensor to segment the MMG signal appropriately.
• WT wavelet transform
• WVT Wigner-Ville transform
• a method used in particular in McLeod's invention [15] is the decomposition into wavelet packets (WPA for Wavelet Packet Analysis). This differs from other time-frequency methods because of the multi-scale decompositions of the starting signal which are separated into low frequency (approximation levels) and high frequency (detail levels) coefficients. These coefficients then form a “wavelet packet.”
• WPA Wavelet Packet Analysis
• This method is very efficient for the analysis of MMG signals but requires heavy post processing and does not lend itself to automation of the estimation of muscle fatigue in real time. In addition, complex calculation operations are required, which seems incompatible with integration into a connected object that must communicate wirelessly for several hours, and in uncontrolled environments.
• the present invention nevertheless allows a multi-resolution analysis based on the decomposition into maximum overlapping wavelets (Maximum Overlap Discrete Wavelet Transform: MODWT) within the framework of the post-processing of the raw MMG data. This technique makes it possible to extract movement artefacts from the muscle signal with more precision than with a conventional filtering technique.
• MODWT Maximum Overlap Discrete Wavelet Transform
• a measurement node 1 the architecture of which is shown in FIG. 2, is positioned on one or more body segments of a person in order to estimate the level of biomechanical stress during his activity. Each node is attached firmly to the body segment via elastic bands, compressing the sensor lightly against the skin and thus providing good mechanical coupling to detect muscle vibrations and limit measurement artifacts.
• each node can communicate to and / or receive information from a receiving station (eg a smartphone).
• a receiving station eg a smartphone.
• the communication protocol chosen for this invention uses Bluetooth Low Energy (BLE).
• BLE Bluetooth Low Energy
• the advantage of BLE is reduced energy consumption and allowing the slave device to remain "discoverable" by a master organ while minimizing its consumption.
• a slave device can remain connected to a master organ and exchange data at periodic times.
• BLE there is no limit to the number of devices supported by a single master, as opposed to conventional Bluetooth limited to seven devices.
• the standard gross throughput in BLE is theoretical 1 Mbps but remains capped at 250 kbps in practice, to be shared between all the slave nodes.
• a feature of the method according to the invention is to allow an analysis of the various biomechanical risk factors, by communicating a multitude of sensors simultaneously, while supporting a range of use of 8 hours.
• the data is also stored in a micro-SD type memory.
• the measuring node supports wired communication via a USB port.
• the receiving station using near field communication (NEC), can activate the sensors and associate them with a body position.
• the detection of the position of the sensor on the body makes it possible to adjust certain signal acquisition parameters such as the template of certain filters.
• the acquisition can then begin with a simple command at the receiving base.
• the raw motion data from the IMU sensor will then be processed and merged by the microcontroller of the measurement node in order to send information on the orientations and positions of each segment and articulation to the receiving base.
• the internal processing of the movement data allows the output signal to be downsampled in order to optimize the battery consumption and the volume of information to be transmitted.
• the sampling of the output IMU data is conventionally between 50 Hz and 120 Hz.
• the receiving station then performs calculation operations: counting and detection of excessive amplitudes of movement, etc. Alerts on the postures and repeated movements can be sent to an external computer or be produced on a smartphone.
• the use of depth cameras or other optical systems without markers allows access to the movement data necessary for the biomechanical analysis and further helps to capture the contextual elements of the scene.
• the MMG sensor integrated into the device, provides indicators on the force exerted by the muscles and muscle fatigue as well as the distribution of stress over all the body segments analyzed.
• FIG. 3 A block diagram describing all the operations and analyzes carried out by the method according to the invention is proposed in FIG. 3.
• the IMU sensor characterizes certain parameters such as the number of technical movements per minute, the speed of the movements. gestures and joint angulations.
• the vibratory signals from the MMG sensor are broken down into 1 second segments at which the RMS amplitude level and the average frequency of the MPF power spectrum are calculated. It is commonly accepted in the art that the RMS amplitude of the MMG signal reflects the level of force exerted by the muscles.
• the analysis of the MPF makes it possible to highlight changes in muscle activation (muscle fiber recruitment strategy, muscle fatigue, etc.), which makes it possible to give an index of fatigue.
• An example of the restitution of biomechanical risk factors is given in figure 4.
• the analysis of the drifts over time of these biomechanical risk factors by linear regression can be carried out in order to calculate the linear regression coefficient (method of Pearson). This indicator is useful for extrapolating the level of physical strain over time and predicting the physiological pause time required for the subject's muscles to recover their reference mechanical state (see Figure 5, after Baillargeon [17]).
• the method of the present invention as well as the associated measurement system can be used to calculate the pause time necessary for the muscles of the lumbar zone to return to a reference state, corresponding to a low level. physical strain, illustrated in FIG. 6.
• the level of muscular activity is characterized by the amplitude RMS 61 obtained from the filtered MMG mechanomyographic signal. It is associated with the average power frequency MPF 62, the variations of which indicate the change in the muscle fiber recruitment strategy.
• a maximum on-call threshold 63 is first of all determined, taken for example at 30% of the maximum value of the filtered mechanomyographic signal.
• this on-call threshold can be envisaged, for example by taking the maximum voluntary force (FMV) at the level of the muscle group of interest.
• FMV maximum voluntary force
• a minimum on-call threshold 64 is then determined, taken for example at 10% of the maximum value of the filtered mechanomyographic signal.
• the objective is to calculate the break time to bring the level of physical strain on the lumbar muscles to the minimum threshold.
• An average value of the RMS amplitude and the average power frequency MPF is calculated for the segment of interest 65. The calculation is reproduced for each activity segment and a linear interpolation is performed for the average RMS amplitude and the Average MPF 66. It is noted that the RMS profile increases during the activity, demonstrating an activity more and more demanding for the lumbar muscles, as well as a decrease in the MPF, demonstrating the recruitment of a greater number of muscle fibers.
• the technique used in the present invention uses the linear regression coefficient calculated previously to determine a profile of linear decrease in the average RMS amplitude towards the threshold. minimum physical strain. Conversely, a linear growth profile is determined to bring the MPF back to a reference state, called non-tired.
• the method of the present invention as well as the associated measurement system can be used to carry out ergonomic ratings at the level of a workstation or an item of equipment.
• a worker illustrated in FIG. 7, carrying an exoskeleton 71 in order to facilitate the wearing of a portable power instrument 72.
• the movements and vibratory activity are measured at the level of the right shoulder and the arm. right by the measurement system 1 to determine whether F exoskeleton is beneficial to the health and safety of the worker (correction of postures, distribution of the load carried at arm's length, etc.).
• FIG. 8 A typical example of data collected is presented in FIG. 8.
• Another innovative mode of operation of this invention lies in the use of movement data to segment physical activity into different states (maintaining a position, recognizing cyclic gestures, etc.).
• This segmentation makes it possible to determine the optimum acquisition parameters of the MMG sensor (acquisition window, filter template and sampling) in order to analyze the impact on the muscles of a specific gesture or series of gestures.
• This method is used in particular for the segmentation of squat activity by Woodward in [22]
• the microcontroller of the measurement node calculates the level of RMS amplitude to detect the level of strain on the muscle, and gives an estimate of the PSD with each acquisition.
• the data extracted from the MMG sensor is transmitted back to the receiving station which determines a time-frequency representation of the activity.
• This method makes it possible to correlate very specific gestures or series of gestures with vibratory signatures of muscle activity.
• the segmentation can be done a posteriori by manual action or automatically by the receiving station thanks to gesture recognition methods combined with machine learning techniques.
• This mode of operation can find applications in the field of sport where an athlete seeks to develop his performance by the perfection of the technical gesture.
• a runner 101 is equipped with the measurement system 1 in order to analyze the muscle fatigue produced by repeated gestures such as striding.
• the IMU and MMG signals typical of the right hip joint are presented in figure 11 in which the stride is characterized by the pattern 111.
• the duration of this pattern (less than 1 second) makes it possible to determine a window d acquisition for post-processing of the MMG signal.
• the knot of measurement proceeds to the estimation of the PSD which is then processed by the receiving station to give the STFT representation of figure 12.
• the power spectral density PSD is estimated by the Burg or Yule-Walker algorithm and can be observed on a frequency band between 20 Hz and 250 Hz.
• Each vertical line represents an acquisition with its frequency signature, that is to say the distribution of the energy of the signal on the frequency band of interest.
• the variation of the MPF over time reflects the change in the strategy for recruiting muscle fibers, in particular by the ratio between the high and low frequency components of the MMG signal. High frequencies are caused by fast twitch fibers while low frequencies are caused by slow twitch fibers.
• a drop in the ratio between high and low frequency (and therefore a decrease in MPF) over a long period makes it possible to objectify peripheral fatigue.
• the fast fibers will have a tendency to block and will then be partially supplemented by the activation of the slow fibers.
• the phases of intense contractions for short times will mobilize more rapid muscle fibers and therefore increase the MPF.
• the mechanomyographic signal obtained by a 3-axis accelerometer, can detect behavioral changes in muscle activity due to fatigue and intensity of exertion.
• the filtering of movement artefacts is a real issue since they will have changing characteristics related to the subject's activity. Indeed, for the analysis of the quadriceps during a classic walk, the movements of the leg have a frequency of around 1 Hz, and go up to 4 Hz during a fast run. In addition, the shocks transmitted by the impact of the foot will, for their part, transmit vibrations along the leg with a spectrum comprising components up to 20 Hz, to then be attenuated by the abdominal lumbar belt.
• the movement signals coming from the inertial units, make it possible to identify static postures, abrupt gestures, or pauses between different series of movements and will thus isolate "sequences" in the body. mechanomyographic signal.
• the identification of these sequences is the result of a physical activity segmentation operation which will facilitate the choice of signal processing operations to be applied to the mechanomyographic signal (MMG).
• MMG mechanomyographic signal
• a 3-pole digital Butterworth high-pass filter with a 0.5 Hz cutoff is a good candidate for mechanomyographic signal processing.
• Other types of filters can be used such as Chebyshev or elliptical filters, which have steeper slopes in the rejected band at the expense of ripples in the passband and / or rejected.
• the use of the acceleration signal of the IMU sensor can be used in an adaptive filtering process using, for example, the LMS (Least Mean Square) algorithm.
• LMS Least Mean Square
• Another technique employing multi-resolution analysis (MRA) by the application of 7-level "db6" Daubechies wavelets can reconstruct the mechanomyographic signal deprived of motion components.
• a low pass filter also cuts out high frequency noise.
• a cutoff between 200 Hz and 250 Hz is ideal for analysis of the mechanomyographic signal.
• the use of a digital Butterworth low pass filter or a Savitzky-Golay filter are common practices in the art today.

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