Method for discriminating between a walking stride and a running stride of a moving pedestrian

Abstract

This method (1200) for discriminating between a walking stride and a running stride of a moving pedestrian comprises the following steps: detection (1210), from motion measurements of a single lower limb of the pedestrian, of at least one stride taken by the pedestrian; evaluation (1230), for the stride or strides thus detected, of an average lower limb speed during the stride and a relative duration of lower limb contact with the ground during the stride; and classification (1240) of the stride as a walking stride or a running stride based on the average speed and relative contact time thus evaluated. Figure for the abstract: Fig. 5

Description

Title of the invention: Method for discriminating between a walking stride and a running stride of a moving pedestrian. Field of the invention

[0001] The present invention relates to monitoring a person's physical activity. More specifically, the present invention relates to discriminating between a walking episode and a running episode when a person is moving on foot. Technological background

[0002] Human bipedal locomotion activities, such as walking and running, are defined as a series of alternating movements of the lower limbs, called strides, in a rhythmic motion that results in forward movement of the body. For each lower limb, a sequence of one or more successive strides is thus identified during such a movement. These strides are separated from one another by the moment, called "heel strike," when the foot makes contact with the ground.

[0003] From the perspective of each lower limb, considered independently of the other, each stride consists of two main phases: a first phase called the stance phase, during which the foot is in contact with the ground, and a second phase called the swing phase, during which the foot is not in contact with the ground. The transition between these two phases, which corresponds to the moment when the foot ceases to be in contact with the ground, is called "toe-off." At the beginning of the stance phase, just after heel contact, the person's weight is at least partially transferred to that lower limb. At the end of the stance phase, just before toe-off, the person's weight is progressively unloaded from that lower limb.

[0004] With reference to Figures 1 and 2, which respectively show a walking cycle and a running cycle, walking is distinguished from running by the fact that: - Walking includes bipedal phases, also called double support phases, during which both feet are in contact with the ground (in other words, the support phases of the strides overlap), while - the race does not include any bipedal phase and instead includes suspension phases during which neither foot touches the ground (in other words, the oscillation phases of the strides overlap).

[0005] Thus, from the point of view of each lower limb: - Walking is characterized by the fact that: • During toe-lifting, the other lower limb is in the first half of its stance phase, marking the end of a double support phase and the beginning of a single-leg phase, and that • at the heel contact, the other lower limb is in the second half of its stance phase, which marks the end of a single-leg phase and the beginning of a double-support phase; - The race is characterized by the fact that: • During toe-lifting, the other lower limb is in the second half of its swing phase, marking the end of a single-leg stance phase and the beginning of a suspension phase, and that • at heel contact, the other lower limb is in the first half of its swing phase, which marks the end of a suspension phase and the beginning of a single-leg phase.

[0006] It was realized that it would be desirable to be able to monitor a person's physical activity in such a way as to be able to detect their episodes of movement while distinguishing, within these, episodes of walking from episodes of running.

[0007] Monitoring a person's physical activity has various applications. Many people, for example, wish to increase or regulate their calorie expenditure, particularly for body mass control. In this context, it can be useful to estimate the physical activity performed by these individuals and to monitor it continuously over time. Some athletes may seek to optimize their training or simply track the evolution of their performance. And regardless of a specific objective, everyone can be interested in their level of physical fitness. Finally, in a medical context, it can be essential to estimate and monitor the evolution of a patient's physical activity. For pathologies associated with muscular difficulties, for example, measuring physical activity is an indicator of the patient's condition. The physician can then monitor the progression of the disease and evaluate the effectiveness of a treatment.This can be used to demonstrate the validity of a treatment or to adapt it according to the patient's response.

[0008] Systems have thus been developed to differentiate between walking and running episodes in a person's activity on foot. This differentiation makes it possible to specify the physical activity in progress and, thereby, to better assess the physical effort exerted and the person's fitness level.

[0009] Such systems are known, for example, from US 8,949,070 B1 and US 9,302,170 B2. However, while these documents do describe devices for monitoring a person's physical activity using inertial sensors to detect and classify physical activity into different categories, including walking and running, they remain very vague as to how this classification is carried out. is achieved. However, while it is relatively easy to differentiate walking from running using inertial sensors for people with a symmetrical gait, such differentiation is made much more complex for people with an asymmetrical gait, for example, those with a limp.

[0010] Examples of concrete implementations of systems for differentiating between a person's walking and running episodes are known from: - M. Ermes, J. Parkka, J. Mantyjarvi, and I. Korhonen, “Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions,” IEEE Trans. Inform. Technol. Biomed., vol. 12, no. 1, pp. 20-26, Jan. 2008, doi: 10.1109 / TITB.2007.899496, - A. Mannini and AM Sabatini, “Gait phase detection and discrimination between walking-jogging activities using hidden Markov models applied to foot motion data from a gyroscope,” Gait & Posture, vol. 36, no. 4, pp. 657-661, Sep. 2012, doi: 10.1016 / j.gaitpost.2012.06.017, and - J. Taborri, E. Palermo, and S. Rossi, “Automatic Detection of Faults in Race Walking: A Comparative Analysis of Machine-Learning Algorithms Fed with Inertial Sensor Data,” Sensors, vol. 19, no. 6, p. 1461, Mar. 2019, doi: 10.3390 / s 19061461.

[0011] However, these known systems do not provide complete satisfaction. Indeed, they are often complex and costly to implement.

[0012] There is therefore a need for a simple and inexpensive process to implement that makes it easy to differentiate between the walking and running episodes of a pedestrian in motion. Description of the invention

[0013] One objective of the invention is to enable easy, simple, and inexpensive discrimination between the walking and running phases of a pedestrian in motion. Another objective is for this discrimination to be reliable and robust, suitable for all types of gait, and in particular for asymmetrical gaits such as those of people with limps.

[0014] To this end, the invention relates, according to a first aspect, to a method for discriminating between a walking stride and a running stride of a pedestrian in motion, implemented by a data processing unit, comprising the following steps: - detection, based on movement measurements of a single lower limb of the pedestrian, of at least one stride taken by the pedestrian, - evaluation, for the one or each stride thus detected, of the following stride parameters: average lower limb speed during the stride, and relative duration of lower limb contact with the ground during the stride, and classification of the stride as walking stride or running stride based on the average speed and relative duration of support thus evaluated.

[0015] According to particular embodiments of the invention, the discrimination method also has one or more of the following characteristics, taken individually or in any technically possible combination(s): The classification of the stride includes determining a score based on the average speed and relative duration of support and comparing this score to a predetermined threshold, the stride being classified as a walking stride or a running stride depending on the result of this comparison; The stride parameters assessed also include stride cadence, with the classification of the stride as walking or running also depending on said stride cadence; the score is also a function of said stride rate; The score is a function of the value S given by the following equation: (measure ^min' ^max) ( AIltefiiire> ^miir ^max^ ) X / ((measurement* (min? (may)

[0017] Or : Vmesure, Amesure and Cmesure are respectively the average speed, relative support time and stride cadence being evaluated, Vmin, vmax, Amin, Amax, Cmin and Cmax are predetermined parameters, and f is a smoothed step function given by the relation:

[0018] ( X Xmin. Xmax ) — 0.5x | sin| x? | + 11, xtllin <x<xlmix \ \ 2 / /

[0019] and the predetermined threshold is such that the stride is classified as a walking stride when the value S is less than 0.5 and as a running stride when the value S is greater than 0.5; - The predetermined parameters are such that: Vmin < vmeasurement < vmax if and only if the average velocity is between 1 m / s and 2 m / s, and / or Amin < Amesure < Amax if and only if the relative support time assessed is between 50 and 60% of the total stride time, and / or • Cmin < CmesUre < Cmax if and only if the evaluated cadence is between 1 stride per second and 1.5 strides per second; and - the movement measurements are acquired by an inertial measurement device attached to the lower limb and preferably worn at the level of an ankle of said lower limb.

[0020] The invention also relates, according to a second aspect, to an analysis device for discriminating between a walking stride and a running stride of a moving pedestrian, comprising a data processing unit configured to: - to detect, from movement measurements of a single lower limb of the pedestrian, at least one stride taken by the pedestrian, - evaluate, for the one or each stride thus detected, the following stride parameters: • average lower limb speed during the stride, and • relative duration of lower limb contact with the ground during the stride, and - classify the stride as a walking stride or a running stride based on the average speed and the duration of support thus evaluated.

[0021] According to one embodiment of the invention, the analysis equipment also has the following characteristic: - the analysis equipment includes an inertial measurement device configured to acquire lower limb motion measurements and transmit these measurements to the data processing unit, said inertial measurement device being adapted to be attached to the lower limb and preferably worn on an ankle of the lower limb.

[0022] The invention also relates, according to a third aspect, to a computer program product comprising code instructions for the execution of a discrimination process according to the first aspect when said program is executed on a computer.

[0023] The invention also relates, according to a fourth aspect, to a means of storage readable by a computer equipment on which is stored a computer program product according to the third aspect. Brief description of the Figures

[0024] Other features and advantages of the invention will become apparent from the following description, given solely by way of example and with reference to the accompanying drawings, in which: - Fig. 1 is a schematic representation of a walking cycle, - [Fig. 2] is a schematic representation of a racing cycle, - Figure 3 is a diagram of an analytical instrument according to an example embodiment of the invention. - [Fig. 4] is a diagram of a measurement device for the analysis equipment in [Fig. 3], and - [Fig.5] is a diagram illustrating an activity analysis process implemented by the equipment in [Fig. 1]. Detailed description of an example of implementation

[0025] The analysis equipment 10 shown in [Fig.3] is intended for the analysis of the physical activity of a pedestrian 12 in motion and more particularly for distinguishing between a walking stride and a running stride of the pedestrian 12. For this purpose, the equipment 10 includes a measuring device 14 attached to a lower limb 20 of the pedestrian 12, in particular to a leg 21 of said lower limb 20.

[0026] It is recalled here that, from an anatomical point of view, a lower limb such as the lower limb 20 consists of three segments: the thigh 22, articulated to the trunk 23 by the hip 24, the leg 21, articulated to the thigh 22 by the knee 25, and the foot 26, articulated to the leg 21 by the ankle 27. Here and in the following, it is in this sense that these terms are used, the term "leg" being used in particular to designate only the segment of the lower limb between the knee and the ankle and not, as in common parlance, the entire lower limb.

[0027] The measuring device 14 is fixed to the leg 21 of the lower limb 20, that is to say, in the terrestrial frame of reference, it presents a movement substantially identical to that of the leg 21. In particular, it is placed between the knee 25 (excluded) and the ankle 27 (included), for example, as shown, substantially on the ankle 27.

[0028] With reference to [Fig. 4], the measuring device 14 comprises a bracket 31 and an attachment member 32 for attaching the bracket 31 to the leg 21. It also comprises an inertial device 33 mounted on the bracket 31. In the example shown, it further comprises a communication system 36, typically a wireless communication system, mounted on the bracket 31 for communication between the measuring device 14 and an external device such as a mobile terminal (not shown), for example, a multifunction mobile phone, or a remote server (not shown). Optionally, it also comprises a storage module 38 mounted on the bracket 31.

[0029] The support 31 is typically made up of a housing.

[0030] The fastening member 32 is here constituted by a bracelet, for example with a hook-and-loop fastener, adapted to encircle the leg 21 and allow for a secure connection. Alternatively (not shown), the fastening member 32 consists of any element allowing a secure connection with the leg 21.

[0031] The inertial device 33 includes a gyroscope 40 for measuring the angular velocity of the measuring device 14 along a system of three orthogonal axes defining a moving frame (Rm) fixed to the measuring device 14, that is, for measuring the three components of an angular velocity vector in said moving frame (Rm). It is thus understood that the gyroscope 40 is typically composed of a set of three gyroscopes, each associated with one of the three axes, in particular in a tri-axis configuration (i.e., each capable of measuring one of the three components of the angular velocity vector).

[0032] The inertial device 33 also includes an accelerometer 42 for measuring the acceleration of the measuring device 14 along a system of three orthogonal axes defining a moving frame of reference attached to the measuring device 14, which is advantageously the same as the moving frame of reference (Rm) of the gyroscope 40. In other words, the accelerometer is capable of measuring the three components of an acceleration vector in said moving frame of reference. It is thus understood that the accelerometer 42 typically consists of a set of three accelerometers, each associated with one of the three axes, in particular in a tri-axis configuration (i.e., each capable of measuring one of the three components of the acceleration vector). These accelerometers are sensitive to external forces, including gravitational forces, applied to the inertial device 33, and allow for the measurement of a specific acceleration.

[0033] The communication system 36 is configured to implement short-range wireless communication, for example Bluetooth or Wi-Fi, and / or to connect to a mobile network (typically UMTS / LTE / 5G) for long-distance communication. Alternatively (not shown), the communication system 36 is, for example, a wired connection (typically USB).

[0034] The communication system 36 is configured for example to transfer data from the storage module 38 to another storage module, typically a server (not shown), and / or to transmit analysis data to an external device for presentation to a human operator, typically for display on a screen.

[0035] Optionally, the measuring device 14 also includes an array of magnetometers (not shown) linked to the support 31, i.e., each exhibiting a motion substantially identical to that of the support 31 in the Earth's frame of reference, and spatially spaced apart from one another. Each magnetometer is a tri-axis magnetometer designed to measure a magnetic field along three axes. To this end, each magnetometer typically consists of three single-axis magnetometers (not shown) oriented along axes substantially perpendicular to each other. These axes are preferably the same as those of the system of three orthogonal axes of the gyroscope 40 and / or the accelerometer 42.

[0036] The magnetometer array, due to its specific geometry, is well-suited to allowing the determination, at each measurement instant of the magnetometers, of a spatial gradient of the measured magnetic field, in particular the coefficients of this gradient along each of the axes of the moving frame (Rm) attached to the support 31. Each coefficient of said gradient is, for example, determined by a method using the vector measurements of the magnetic field obtained by the magnetometers, combined with optimization methods such as least squares or median filtering, or based on the intrinsic properties of the magnetic field described by Maxwell's equations. However, any other suitable conventional method for calculating the coefficients of the spatial gradient of the magnetic field is also suitable.

[0037] Still with reference to [Fig.4], the analysis equipment 10 also includes a data processing unit 50.

[0038] The data processing unit 50 is configured to use the measurements from the inertial device 33 and, where applicable, the magnetometer array (for example, by implementing the method described in EP 2 541 199), so as to: - detect at least one stride taken by the pedestrian, - evaluate parameters of the stride(s) thus detected, and - classify the stride as a walking stride or a running stride based on the parameters thus evaluated.

[0039] For this purpose, the data processing unit 50 is, in the example shown, a programmable machine, such as a DSP (Digital Signal Processor) or a microcontroller. It comprises a processor or CPU (Central Processing Unit) 52 and a RAM (Random Access Memory) and / or ROM (Read Only Memory) type memory 54. The processor 52 is configured to execute instructions loaded into the memory 54. When the data processing unit 50 is powered on, the processor 52 is able to read instructions from the memory 54 and execute them. These instructions form a computer program causing the processor 52 to carry out certain steps of a process 1000 ([Fig. 5]) which will be detailed below.

[0040] Alternatively (not shown), the data processing unit 50 consists of a dedicated machine or component, such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit).

[0041] The data processing unit 50 also includes a buffer memory 56 for the temporary storage of information necessary for the implementation of the process 1000.

[0042] In the example shown, the data processing unit 50 is mounted on the support 31 of the measuring device 14. Alternatively (not shown), at least part of the data processing unit 50 is located remotely, for example in a mobile terminal (not shown) and / or in a remote server (not shown). In other words, at least part of the steps of the process 1000 is carried out by a mobile terminal and / or a remote server. The communication system 36 is then configured to send the data from the inertial device 33 and, where applicable, from the magnetometer array of the measuring device 14 to the mobile terminal and / or the remote server.

[0043] A process 1000 implemented by the equipment 10 will now be described, with reference to [Fig.5].

[0044] As shown in Figure 5, the method 1000 begins with a step 1100 of acquiring motion measurements of the lower limb 20. During this step, the inertial device 33 acquires the acceleration y(tk) and the rotational velocity w(tk) of the measurement point at each of a plurality of sampling instants tk. It should be noted that the acceleration y(tk) and the rotational velocity S(tk) are vectors, each having three components along each of the axes of the moving frame (Rm) attached to the support 31 of the measurement device 14. The sampling instants tk are separated from each other by a time dt very small compared to the characteristic time of the pedestrian's movements 12, this time dt being, for example, approximately equal to 10 ms.

[0045] Step 1100 follows a start-up of the measuring device 14 and typically lasts until the measuring device 14 is switched off.

[0046] The process 1000 further includes, following step 1100 or, more precisely, following the acquisition of acceleration vectors y(tk) and rotational velocity w(tk) for a sufficient number of sampling instants tk, a step 1200 of discrimination of a walking stride from a running stride of the pedestrian 12. This step 1200 is implemented by the data processing unit 50.

[0047] Step 1200 includes a first substep 1210 of detecting at least one stride of the pedestrian 12. During this substep 1210, the data processing unit 50 processes the motion measurements acquired by the measuring device 14, for example by means of the method described in WO 2019 / 243609, so as to detect when a stride has been taken and to determine its start and end times.

[0048] The first substep 1210 is followed by a second substep 1220 for estimating a trajectory from a reference point of the lower limb 20 during the stride. During this substep 1220, the data processing unit 50 processes the motion measurements acquired by the measuring device 14 in order to estimate a trajectory from a reference point of the lower limb 20, for example from a point of

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] ankle 27, during the stride. This estimation is typically carried out by classical methods of changing reference points and double integration of the movement measurements acquired by the measuring device 14, the double integration being preferably recalibrated each time the foot 26 touches the ground, as described in FR 3 042 266. The second substep 1220 is followed by a third substep 1230 for evaluating stride parameters. During this third substep 1230, the data processing unit 50 processes the trajectory estimated in substep 1220 and the start and end times of the stride determined in substep 1210 to evaluate several stride parameters. These parameters include stride length, stride duration, average stride speed, absolute stance phase duration, and relative stance phase duration. Advantageously, these parameters also include stride cadence. Stride length is defined as the distance traveled by the reference point of the lower limb 20 between the beginning and end of the stride. This distance is preferably a resultant distance, that is, measured between a first position of the reference point at the beginning of the stride and a second position of the same reference point at the end of the stride. Stride length is then typically given by the following formula: — ( ^end " start ) + ( end " (^end~ ^start)~ Or : - L is the stride length, - Xstart, Ystart and Zstart are the coordinates of the reference point in an inertial frame of reference at the beginning of the stride, and - Xfin, Yfin and Zfin are the coordinates of the reference point in the inertial frame at the end of the stride. In a preferred embodiment, the resulting distance is calculated in a horizontal plane. The formula for stride length is then simplified as follows: (X end " start) + (Y end ~ start) Alternatively, this distance is a real distance, that is to say, it is constituted by the curvilinear length, in space, of the trajectory followed by the reference point during the stride. Stride duration is defined as the time elapsed between the start of the stride, when the beginning of the stride is detected, and the end of the stride, when the end of the stride is detected. It is typically given by the following formula: ~ ? end " T beginning

[0058] where: - D is the stride duration, - Tstart is the moment the stride begins, and - Tfin is the moment of the end of the stride.

[0059] Stride cadence is defined as the inverse of stride duration. It is typically given by the following formula:

[0060]

[0061] where: - C is the stride cadence, - D is the stride duration.

[0062] Average stride speed is defined as the ratio of stride length to stride duration. It is typically given by the following formula:

[0063] r = A

[0064] where: - v is the average stride speed, - L is the stride length, and - D is the stride duration.

[0065] The absolute duration of the stance phase is defined as the duration during which the foot 26 of the lower limb 20 is in contact with the ground. It is typically measured using the method described in the document B. Mariani, H. Rouhani, X. Crevoisier, and K. Aminian, “Quantitative estimation of foot-flat and stance phase of gait using foot-wom inertial sensors,” Gait & Posture, vol. 37, no. 2, pp. 229-234, Feb. 2013, doi: 10.1016 / j.gaitpost.2012.07.012.

[0066] The relative duration of the stance phase is defined as the ratio between the absolute duration of the stance phase and the stride duration. It is typically given by the following formula:

[0067] A = i

[0068] where: - A is the relative duration of the support phase, - d is the absolute duration of the support phase, and - D is the stride duration.

[0069] The third substep 1230 is followed by a fourth substep 1240 for classifying the stride as a walking stride or a running stride. During this fourth substep 1240, the data processing unit 50 processes at least some of the stride parameters evaluated during the third substep 1230 in order to deduce whether the stride is a walking stride or a running stride. In particular, the stride parameters processed during this fourth substep 1240, and The factors used to classify stride include at least the average stride speed and the relative duration of the stance phase. Here, they also include the stride cadence.

[0070] In the example shown, this classification step 1240 comprises a first substep 1242 of determining a score based on at least some of the stride parameters evaluated in the third substep 1230, followed by a second substep 1244 of comparing this score to a first predetermined threshold. Depending on the result of this comparison, the comparison substep 1244 is followed either by a substep 1246 of classifying the stride as a walking stride, or by a substep 1248 of classifying the stride as a running stride.

[0071] The score determined during the first substep 1242 is a function at least of the average stride speed and the relative duration of the stance phase. Advantageously, it is also a function of the stride cadence. It is typically a function of, for example equal to, the value S given by the following equation: [ S — (y measure, ^min? Amax) ) X measure ^max)

[0073] where: • vmesure, Amesure and CmesUre are respectively the average stride speed, the relative duration of the stance phase and the stride cadence evaluated, • vmin, vmax, Amin, Amax, Cmin and Cmax are predetermined parameters, and • f is a smoothed step function given by the relation:

[0074] P. J ( x, x^^, xlnux ) — 0.5 x ( sinl xj 1 + 1 j, xtlt^n < x < x(rMX f Xmjn X

[0075] The predetermined parameters are, for example, such as: - vmin < vmeasurement < vmax if and only if the average stride speed evaluated is between 1 m / s and 2 m / s. - Amin < Amesure < Amax if and only if the relative duration of the assessed stance phase is between 50 and 60% of the stride duration, and - Cmin < CmesUre < Cmax if and only if the evaluated cadence is between 1 stride per second and 1.5 strides per second.

[0076] The first predetermined threshold is such that the stride is classified as a walking stride when the value S is less than 0.5 and as a running stride when the value S is greater than 0.5.

[0077] Alternatively (not shown), classification step 1240 also includes an additional substep comparing the average stride speed to a second predetermined threshold. The stride is then classified: - as a walking stride if and only if the score is below the first predetermined threshold and the average stride speed is below the second predetermined threshold, and - as a running stride in other cases.

[0078] The first threshold is for example such that, when the score is equal to the first threshold, the value S is approximately equal to 0.5, and the second threshold is for example between 2.4 and 2.6 m / s.

[0079] As a further alternative (still not shown), the classification step 1240 includes comparing the relative duration of the stance phase to a first threshold and comparing the average stride speed to a second threshold, the stride being classified: - as a running stride if and only if the stance phase duration is less than the first threshold and the average stride speed is greater than the second threshold, and - as a walking stride in other cases.

[0080] The first threshold is for example approximately equal to 0.5 and the second threshold is for example between 1.9 and 2.1 m / s.

[0081] Thus, thanks to the invention described above, it is possible to easily distinguish a walking stride from a running stride of a pedestrian, and thus to distinguish a walking episode from a running episode of that pedestrian, simply by using stride parameters easily deducible from measurements acquired by an inertial measuring device worn by the pedestrian. This discrimination is also robust since, by combining the relative duration of the stance phase and the average stride speed to perform said discrimination, it is avoided that the typical walking strides of people with a limp, which have a short relative duration of the stance phase but a low average stride speed, are classified as running strides. This robustness is further reinforced by taking into account the stride cadence to perform the classification.

Claims

Demands

1. A method (1200) for discriminating a walking stride from a running stride of a pedestrian (12) in motion, implemented by a data processing unit (50), comprising the following steps: - Detection (1210), from motion measurements of a single lower limb (20) of the pedestrian (12), of at least one stride taken by the pedestrian (12), - Evaluation (1230), for the stride or each stride thus detected, of the following stride parameters: • Average speed of the lower limb (20) during the stride, and • Relative duration of contact of the lower limb (20) on the ground during the stride, and - Classification (1240) of the stride as a walking stride or a running stride according to the average speed and the relative duration of contact thus evaluated.

2. A discrimination method (1200) according to claim 1, wherein the classification (1240) of the stride comprises the determination (1242) of a score based on the average speed and the relative duration of support and the comparison (1244) of this score to a predetermined threshold, the stride being classified as a walking stride or a running stride according to the result of this comparison.

3. A discrimination method (1200) according to claim 1 or 2, wherein the evaluated stride parameters also include a stride cadence, the classification (1240) of the stride as a walking stride or a running stride also depending on said stride cadence.

4. A discrimination method (1200) according to claims 2 and 3 taken together, wherein the score is also a function of said stride cadence.

5. A discrimination method (1200) according to claim 4, wherein the score is a function of the value S given by the following equation: “^fiymesure? mesure? ^'nûre where: • vmesUre, AmesUre, and CmesUre are respectively the average speed, relative stance time, and stride cadence being evaluated, • vmim ^max, Amin, Amax, Cmin, and Cmax are predetermined parameters, and • f is a smoothed step function given by the relation: 0, x<,x^„ f (Xx,ni,„ ) = 0.5 x (sin(x§ j + 1 j, x^, <x<xmlu 1 X ■ < r ’’ Anun — •* et le seuil prédéterminé est tel que la foulée soit classifiée en tant que foulée de marche lorsque la valeur S est inférieure à 0,5 et en tant que foulée de course lorsque la valeur S est supérieure à 0,5.

6. A discrimination method (1200) according to claim 5, wherein the predetermined parameters are such that: - vmin < vmesure < vmax if and only if the average velocity evaluated is between 1 m / s and 2 m / s, and / or - Amin < AmesUre < Amax if and only if the relative support time evaluated is between 50 and 60% of a total stride time, and / or - Cmin < CmesUre < Cmax if and only if the cadence evaluated is between 1 stride per second and 1.5 strides per second.

7. A discrimination method (1200) according to any one of the preceding claims, wherein the motion measurements are acquired by an inertial measuring device (14) attached to the lower limb (20) and preferably worn at the level of an ankle (27) of said lower limb (20).

8. Analysis equipment (10) for discriminating a walking stride from a running stride of a moving pedestrian (12), comprising a data processing unit (50) configured to: - Detect, from motion measurements of a single lower limb (20) of the pedestrian (12), at least one stride taken by the pedestrian (12), - Evaluate, for the stride or each stride thus detected, the following stride parameters: • Average velocity of the lower limb (20) during the stride, and • Relative duration of lower limb support (20) on the ground during the stride, and - Classify the stride as a walking stride or a running stride based on the average speed and the duration of support thus evaluated

9. Analysis equipment (10) according to claim 8, comprising an inertial measuring device (14) configured to acquire motion measurements of the lower limb (20) and transmit these measurements to the data processing unit (50), said inertial measuring device (14) being adapted to be attached to the lower limb (20) and preferably worn on an ankle (27) of the lower limb (20).

10. Product computer program comprising code instructions for performing a discrimination process (1200) according to any one of claims 1 to 7 when said program is executed on a computer.

11. Computer equipment readable storage means (54) on which a computer program product according to claim 10 is stored.

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