Method for generating a velocity-slope-time model characterizing a physical activity of a participant and associated method of use

Abstract

Title: Method for generating a speed-slope-time model characterizing a physical activity of a participant and associated method of use. According to a first aspect, the invention provides a method for generating a speed-slope-time model characterizing, for a given participant and a human-locomotion endurance physical activity, such as trail running, and a method for using the speed-slope-time model thus generated. The technical effect of the invention is to allow the individualization and use of the concept of critical speed for a participant in a physical activity, taking into account, at each instant, the slope, altitude, and type, or even conditions, of terrain, to indicate to the participant, in real time, during the physical activity, the critical speed not to be exceeded in order to optimize their performance. Figure for the abstract: Fig. 9.

Description

Title of the invention: Method for generating a velocity-slope-time model characterizing the physical activity of a participant and associated method of use technical field

[0001] The present invention relates to the field of endurance physical activities involving human locomotion. More particularly, it relates to a method for generating a velocity-slope-time model characterizing a physical activity of a participant and an associated method of use. STATE OF THE ART

[0002] Trail running (or 'mountain running') is one of the endurance activities involving human locomotion, such as cycling, rowing, cross-country skiing, ski touring, or swimming, for example. These activities are closely linked to the participant's ability to produce and maintain power, mechanically defined as the product of force and velocity, in order to perform a movement. In cycling, for example, power is obtained by applying a torque at the crankset at a certain angular velocity (i.e., pedaling cadence). A change in gear ratio (the ratio between chainrings and sprockets) allows maintaining an optimal pedaling cadence, whether on flat ground or uphill, depending on the desired speed or power. The same applies to rowing, where the length of the oars can be modified to vary the rowing cadence.In contrast, in trail running, the incline has a greater impact since the runner has less leeway in adjusting their cadence and stride length. For a given speed, an increase in force is therefore necessary as the incline increases.

[0003] Each participant in a physical activity involving human locomotion is characterized by a force-velocity-time relationship that exhibits significant inter-individual variability. It should be noted that this variability becomes apparent when the mechanical conditions of locomotion are variable (e.g., due to incline in trail running), whereas it remains less noticeable for flat running, such as a marathon.

[0004] In the field of endurance activities involving human locomotion, including flat running, the concept of critical power or speed is widely used. It serves both as an indicator of an athlete's endurance capacity and as valuable information for race management strategy, particularly by helping to avoid acute fatigue through continuous management of running speed during physical activity.

[0005] However, although the methods and technologies are effective today for While analyses of flat running do not take into account the specific characteristics of other endurance activities involving human locomotion, such as trail running, these specific characteristics include slope, altitude, and terrain conditions (technicality and humidity, for example), which are particularly, but not exclusively, related to trail running and can vary from one section of the course to another.

[0006] Some solutions use a correction factor for the slope, referring to the equivalent speed on a flat surface. However, this type of correction is often based on an average of data taken from a group of individuals, which, given the aforementioned large inter-individual variability, does not work (or works poorly) for most users.

[0007] The present invention aims precisely to make it possible to take into account these specificities (slope, altitude, type / condition(s) of terrain), in order to allow each practitioner of an endurance activity with human locomotion to benefit from the concept of "critical speed" for each force condition imposed by the environment (for example the slope in trail running).

[0008] Several solutions exist, but all of them have limitations that the present invention aims to overcome.

[0009] For example, the solution developed by Garmin® and called PacePro™ has been integrated into certain GPS watch models since November 2019. User feedback seems mixed regarding the paces indicated by the watch and which the runner is encouraged to maintain. Indeed, segments of the course are defined automatically by the watch, or by the user, and are used to calculate an average pace to be maintained on those segments. From the same GPS watch manufacturer, there is a feature called Stamina, integrated into certain Garmin® watch models since January 2022. It provides the user with an indicator similar to an energy reserve. The total capacity of this reserve is estimated based on the user's training data.The runner can then manage their pace to avoid completely depleting their energy reserves before the end of the race, as such depletion often leads to a significant slowdown or even withdrawal. However, no pace is indicated; the user must therefore manage their own pace and assess whether the remaining energy reserves will be sufficient to reach the finish line. These solutions do not appear to take into account altitude, terrain type, or conditions.

[0010] As another example, Coros®, another manufacturer of GPS watches, has improved its "Effort Pace®" function for estimating equivalent flat speed. This function can now take into account each runner's individual profile based on their training data. This allows for real-time, based of the slope, the runner's equivalent flat speed. However, even with this improved functionality, it cannot handle certain specific factors such as terrain type or conditions, or altitude. Furthermore, the concept of critical speed is not applied; the runner therefore benefits from an indication of equivalent flat speed, but must determine for themselves what speed they should run at, relative to this indication, based on terrain conditions and their fatigue level during the race, in order to manage their strategy.

[0011] As another example, the company RunMotion® offers an application for road and mountain running that generates personalized training plans. However, while this application allows for managing the training aspect preceding a race, it does not aim to create a race plan or guide the runner in real time, with, among other things, a recommended pace to follow based on the type of terrain.Part of the application appears to aim at offering the user a performance prediction, but only for road races. The mathematical model used to predict a user's performance was the subject of a scientific publication ("A minimal power model for human running performance", Matthew Mulligan, Guillaume Adam, Thorsten Emig, November 16, 2018), with one of the founders of RunMotion®, namely Guillaume Adam, as a co-author.

[0012] It should also be noted that the STRAVA© platform offers users a feature that estimates a flat-ground equivalent speed after analyzing an endurance activity involving human locomotion, and in particular a trail run. However, this feature does not provide real-time guidance for the flat-ground equivalent speed, nor does it take into account altitude or the type or conditions of the terrain. Furthermore, to estimate the flat-ground equivalent speed, an average of the physical abilities of runners registered on the platform is used rather than the individual physical abilities of each runner.

[0013] An objective of the present invention is to provide, in real time, to a practitioner of an endurance physical activity with human locomotion, such as a trail runner (or 'mountain running'), a speed of movement to respect, thus enabling him to optimize his race management according to his physical abilities, and the course which is presented to him, this course being characterized by a slope, an altitude and a type of terrain, possibly terrain conditions, and thus limit the appearance of fatigue. SUMMARY

[0014] To achieve this objective, according to a first aspect of the invention, a method is provided for generating, calibrating, or adjusting a velocity-slope-time model characterizing a human locomotor endurance physical activity of a participant. The method according to the first aspect of the invention is essentially as it includes the following steps: a. receive a set of geolocation data representative of the latitude, longitude, and altitude of the participant at different times during at least one session of said physical activity, b. process at least part of the received geolocation data to calculate representative data, per unit of length chosen, of a slope profile traversed by the user during each session, c. Based on at least part of the received geolocation data and the previously calculated representative slope data, calculate an average slope and an average speed for the user for each of a plurality of predefined target durations, d. Based on the calculated average gradients and average speeds, deduce (or select) a maximum average speed for each target duration from among the plurality of predefined target durations and each from among a plurality of predefined target gradients, and e. Based on the deduced maximum average speeds, generate the speed-slope-time model of the practitioner by determining a plurality of parameters on which the speed-slope-time model depends, by adjusting said model to the maximum average speeds

[0015] According to a second aspect of the invention, a method is provided for using a velocity-slope-time model characterizing a human locomotor endurance physical activity of a practitioner. The method according to the second aspect of the invention is essentially such that it comprises the following steps: a. to receive coefficients defining, for the practitioner, their speed-slope-time model, b. receive a tile matrix comprising a route that the participant intends to follow during a future session of said physical activity, then c. at a plurality of times, preferably at each time, during the session: i. receive geolocation data representative of a latitude, longitude and altitude of the practitioner, ii. Based on at least part of the received geolocation data, calculate the slope traversed by the user, iii. Based on at least part of the received geolocation data, calculate the speed of the participant, iv. Based on at least some of the geolocation data received, estimate the type of terrain traversed by the user, or even the terrain conditions during the session, v. Based on the calculated slope and speed, determine, using the speed-slope-time model and the received coefficients, a critical speed that the user must not exceed. vi. depending on the estimated type of terrain, or even the terrain conditions encountered, apply a correction coefficient to the determined critical speed, If, for a period exceeding a predefined threshold value, the calculated speed of the practitioner is greater than the critical speed corrected by the application of the correction coefficient, indicate this to the practitioner.

[0016] A third aspect of the invention relates to a computer program product, preferably recorded on a non-transient medium, comprising instructions which, when executed by at least one of a processor and a computer, cause at least one of the processor and the computer to execute the generation process according to the first aspect of the invention and / or the usage process according to the second aspect of the invention.

[0017] The invention as introduced above has the technical effect of enabling the individualization and use of the critical force-speed concept of a practitioner (individual capacity) of a physical activity, taking into account, at each moment, the environmental conditions of slope, altitude and the type, or even the conditions (for example wet or dry), of terrain, to indicate to the practitioner, in real time, during the physical activity, the speed which will optimize performance and limit the onset of fatigue.

[0018] The main advantages that result from this include: a. The generation and visualization of the speed-slope-time relationship characterizing the athlete allows for the identification of areas for improvement in order to adapt their training or race choices, for example. Weaknesses may include a lack of endurance during long activities, or a lack of strength that is a disadvantage on the steepest slopes; and / or b. Personalized, real-time guidance during physical activity regarding the target speed and characteristics of the run (incline, terrain type (or technical difficulty), altitude, and even terrain conditions (rain or sunshine)); the "real-time" aspect is particularly valuable given the highly variable terrain conditions during certain physical activities, such as trail running; and / or c. The ability to warn the participant of exceeding their target speed, which, after only a few minutes of activity, could generate significant fatigue and lead to a sharp decrease in the participant's physical performance, sometimes resulting in a longer race time, or even a abandonment in certain cases; and / or d. the prevention of injury to the participant that may be linked to an effort exceeding that corresponding to their target speed and sustained for too long a period, such an injury sometimes leading to withdrawal; and / or e. the reduction of the abandonment rate during a race; this aspect is interesting for the organizers of a sporting event since it reduces the logistics of repatriation and increases the satisfaction rate of participants. BRIEF DESCRIPTION OF THE FIGURES

[0019] The aims, objects, features and advantages of the invention will become clearer from the detailed description of an embodiment thereof, which is illustrated by the following accompanying drawings in which:

[0020] [Fig.1] Fig.1 graphically represents an example of a speed-slope-time model adjusted to a practitioner by an implementation of the method according to the first aspect of the invention and / or used by a practitioner during their human locomotion endurance physical activity by an implementation of the method according to the second aspect of the invention.

[0021] [Fig.2] Fig.2 graphically represents the measurement points illustrated on Fig.1 so as to better illustrate the influence of the duration of the effort exerted on the speed of movement of the practitioner.

[0022] [Fig.3] The [Fig.3] graphically represents the measurement points illustrated on the [Fig.1] so as to better illustrate the influence of the slope on the speed of movement of the practitioner.

[0023] [Fig.4] Fig.4 graphically represents the known relationship between the level of ability of a practitioner to exert effort and the altitude at which the practitioner is located.

[0024] [Fig.5] The [Fig.5] represent a capture of a screen through which the practitioner can be kept informed of an exceeding of his critical speed by an implementation of the method according to the second aspect of the invention.

[0025] [Fig.6] Fig.6 graphically represents an example of the evolution of the speed of movement of a practitioner and that of his critical speed as a function of time, during a physical endurance activity with human locomotion.

[0026] [Fig.7] Fig.7, to be read in conjunction with Fig.6, graphically represents when, by an implementation of the method according to the second aspect of the invention, an indication of exceeding its critical speed is indicated to the practitioner during a physical endurance activity with human locomotion, and to which, among two levels of importance (noted "light" and "strong"), this indication is associated.

[0027] [Fig-8] The [Fig.8] is a flowchart illustrating one implementation method of the process according to the first aspect of the invention.

[0028] [Fig.9] The [Fig.9] is a flowchart illustrating one implementation method of the process according to the second aspect of the invention.

[0029] The drawings are given by way of example and are not limiting of the invention. They constitute schematic representations of principle intended to facilitate understanding of the invention and are not necessarily to scale with practical applications. In particular, the values ​​indicated on the different axes of the illustrated graphs are not necessarily representative of reality. DETAILED DESCRIPTION

[0030] Before proceeding to a detailed review of embodiments of the invention, optional features that may be used in combination or alternatively are listed below:

[0031] According to an example of the first aspect of the invention, endurance physical activity involving human locomotion includes at least one of the following: running, and in particular trail running, cycling, cross-country skiing, ski touring, etc. Where appropriate, the slope criterion is then replaced by a criterion adapted to the physical activity in question. For example, for cycling, the criterion could be defined according to the ratios between chainrings and sprockets and / or pedaling speed.

[0032] According to another example of the first aspect of the invention, the geolocation data was collected using a GPS watch, a smartphone, or a third-party application, for example offered via an application platform, the geolocation data is where appropriate received from the GPS watch, the smartphone, or a server hosting the third-party application that collected the data.

[0033] According to another example of the first aspect of the invention, the generation method according to the first aspect of the invention is implemented by a computer server.

[0034] According to another example of the first aspect of the invention, prior to the processing step, the geolocation data is received in JSON, GPX, or FIT format and then converted into raster form, or is received directly in raster form. According to this example, the processing of the geolocation data in the subsequent steps of the generation process according to the first aspect of the invention is advantageously facilitated.

[0035] According to another example of the first aspect of the invention, the data representing the slope profile traversed by the practitioner during each session are calculated as a unit length derivative of the geolocation data representing the altitude.

[0036] According to another example of the first aspect of the invention, at least part of The geolocation data used to calculate the average slopes and speeds of the user for predefined target durations includes geolocation data representing the user's latitude and longitude at different times during each session.

[0037] According to another example of the first aspect of the invention, the unit of length is chosen to be between 20 cm and 1 km, and is for example substantially equal to 1 m.

[0038] According to another example of the first aspect of the invention, with the geolocation data defined in a spherical coordinate system, the processing step of the geolocation data in question comprises, before the calculation of the data representing the slope profile: a. a transformation of geolocation data by changing the reference frame from a spherical frame to a Cartesian frame, then b. a first interpolation, for example linear, on the transformed geolocation data, at a sampling frequency between 0.02 and 10 Hz, preferably approximately equal to 1 Hz, to obtain time-sampled data, then c. a second interpolation, for example linear, on the transformed data defined on the time basis, over a sampling distance of between 0.1 and 500 m, preferably approximately equal to 1 m, to obtain geographically sampled data, so that the calculation of representative slope profile data is based on geographically sampled data. This example simplifies the calculation of representative slope profile data, at least in terms of numerical implementation.

[0039] According to another example of the first aspect of the invention, the method further comprises, after the first interpolation and before the second interpolation, the removal from the time-sampled data of those data that do not exist in the data sampled before the first interpolation, and for which the elapsed time exceeds a threshold value between 1 second and 1 minute, preferably substantially equal to 10 seconds. According to this example, it is advantageous to avoid taking into account, for subsequent calculations, geolocation data that reveal a (micro-)pause of the participant during a session or that are distinct from (micro-)pauses and that are related to optimizing the size of the data recorded by the watch and its battery consumption.

[0040] According to another example of the first aspect of the invention, the method further comprises, after the first interpolation and before the second interpolation, the application of a low-pass filter to the geolocation data representing the altitude, in order to smooth out any measurement inaccuracies. According to this example, this avoids to use, for subsequent calculations, measurements that could distort them.

[0041] According to another example of the first aspect of the invention, the method further comprises, after calculating the representative slope profile data, a third interpolation, for example linear, on the representative slope profile data, at a sampling frequency between 0.02 and 10 Hz, preferably substantially equal to 1 Hz, so that the calculation of the average speeds is a function of the calculated and time-sampled representative slope profile data. According to this example, subsequent calculations are facilitated, at least in terms of numerical implementation.

[0042] According to another example of the first aspect of the invention, the calculation of the data representing the slope profile is followed, preferably immediately, by the application of a low-pass filter to said data representing the slope profile. According to this example, it is thus avoided to consider, for subsequent calculations, slope measurements that are potentially representative of aberrations and could distort the generation of the velocity-slope-time model.

[0043] According to another example of the first aspect of the invention, the target durations are predefined and included in a set of intervals ranging from a minimum value of between 1 and 600 seconds, preferably substantially equal to 120 seconds, to a maximum value of between 6 minutes and 6 hours, preferably substantially equal to 14 minutes, the intervals of the set, preferably regular and / or adjacent to each other, taking a value of between 1 second and 10 minutes, preferably substantially equal to 2 minutes.

[0044] According to another example of the first aspect of the invention, the target slopes are predefined within a set of intervals ranging from a minimum value between -10% and -60%, preferably substantially equal to -50%, to a maximum value between 10% and 60%, preferably substantially equal to 50%, the intervals of the set, preferably regular and / or adjacent to each other, taking a value between 0.1 and 10%, preferably substantially equal to 2%.

[0045] According to the previous example, the step of calculating the average slopes and speeds is implemented, for each target duration, using moving averages. This saves computation time and central processing unit (CPU) resources.

[0046] According to another example of the first aspect of the invention, the step of deducing the maximum average speed for each target time is preceded by a removal, for each target time and each target slope, from the average slopes and calculated average speeds, of representative data of slopes not having a majority of values, for example at least 80% of values, located in the corresponding target slope, preferably with a tolerance substantially equal to + / - 2.5%.

[0047] According to another example of the first aspect of the invention, the step of deducing the maximum average speed for each target duration and each target slope further includes removing, from the deduced maximum average speeds, maximum average speeds that are substantially less than 0.5 m / s and substantially greater than 7 m / s. According to this example, it is advantageous to avoid taking into account, for the subsequent generation process according to the first aspect of the invention, maximum average speeds whose validity is uncertain.

[0048] According to another example of the first aspect of the invention, the generation method comprises, after the step of deducing the maximum average speed for each target duration and each target slope, a recording on a non-transient medium of the maximum average speeds deduced with the corresponding target durations and slopes.

[0049] According to another example of the first aspect of the invention, the step of generating the speed-slope-time model of the practitioner includes at least the step of constructing a two-dimensional matrix containing the maximum average speeds per target time and target slope, so that the adjustment of the speed-slope-time model is carried out on the basis of the matrix thus constructed.

[0050] According to another example of the first aspect of the invention, the adjustment of the velocity-slope-time model is implemented by implementing a method for minimizing the residuals of an equation p) defining the velocity-slope-time model, where V is the maximum average velocity, f is the time and P is the slope.

[0051] According to another example of the first aspect of the invention, the velocity-slope-time model is defined by an equation V(t, p) taking the following form: V(t, p) = D / 1+V£p), where D is a reserve of distance achievable above the critical speed (in meters), f is the duration of an effort made (in seconds), P is the slope (in %), and whose limit, when the duration of the effort made t tends towards infinity, defines a critical speed-slope relationship, denoted VL(p), characterizing the practitioner.

[0052] According to another example of the first aspect of the invention, the speed-slope-time model is defined in part by a critical speed-slope relation Vc(p) taking the form of the inverse of a polynomial of at least the second degree, having as its variable the slope p and whose parameters, denoted for example a, b and c, are determinable by fitting the equation V(t, p) defining the speed-slope-time model on the basis of the maximum average speeds deduced.

[0053] According to another example of the first aspect of the invention, the critical velocity-slope relationship V^p) takes the form of the inverse of a second-degree polynomial: Vc(p) = 1 / (ax p2 + bx p + c) where the parameters a, b and c are determinable (or determined) by fitting the critical velocity-slope relationship V / p) on the basis of the maximum average speeds deducted.

[0054] According to an example of the second aspect of the invention, the speed-slope-time model was generated by implementing a method for generating a speed-slope-time model characterizing a human locomotion endurance physical activity of a practitioner according to the first aspect of the invention.

[0055] According to another example of the second aspect of the invention, each tile is defined by a surface, for example square, of the geographical area on which the practice session of said physical activity takes place or is intended to take place.

[0056] According to another example of the second aspect of the invention, the geolocation data is received at a frequency substantially equal to 1 Hz.

[0057] According to another example of the second aspect of the invention, further comprising, prior to the steps of calculating the slope and the user's speed, the application of an infinite impulse response (IIR) filter to the received geolocation data. According to this example, the method of use according to the second aspect of the invention makes it possible to process all the geolocation data without imposing a size limit on this data, or regardless of the size of this data, and therefore makes it possible to filter the geolocation data without knowing its size a priori.

[0058] According to another example of the second aspect of the invention, the estimation of the type of terrain traversed includes: a. identifying the tile corresponding to the geolocation of the user, b. for each of the paths listed on the identified tile, calculating the distance between the user's geolocation and said path, and c. retrieval of data characterizing the path closest to the geolocation of the practitioner.

[0059] According to another example of the second aspect of the invention, the correction coefficient to be applied is determined based on the characterization data retrieved.

[0060] According to another example of the second aspect of the invention, the correction coefficient to be applied is substantially between 0.5 and 1, the higher the technicality of the type of terrain traversed, the lower the correction coefficient, and / or in which the application of the correction coefficient consists of multiplying the critical speed determined by the correction coefficient.

[0061] According to another example of the second aspect of the invention, the determination, using the velocity-slope-time model and the received coefficients, of the critical velocity not to be exceeded includes the calculation of the critical velocity according to the following equation: Vc = ( 1 - Coeffvo2max xh) / (axp^ + bx p+where: a. h is the altitude of the practitioner, in meters; b. P is the slope traversed, in %; a, b, and c are the coefficients received, and d. Coej j vo2max is a coefficient reflecting the decrease in the physical capacities of the practitioner as a function of altitude, this coefficient is for example approximately equal to 6x105.

[0062] According to another example of the second aspect of the invention, the indication given to the practitioner is of a first level of importance if the calculated speed of the practitioner is greater than the critical speed corrected by the application of the correction coefficient for a period greater than a first predefined threshold value and less than a second predefined threshold value, and of a second level of importance, greater than the first, if the calculated speed of the practitioner is greater than the critical speed corrected by the application of the correction coefficient for a period greater than the second predefined threshold value, the second predefined threshold value being greater, for example substantially twice greater, than the first predefined threshold value.

[0063] A parameter "approximately equal to / greater than / less than" a given value means that this parameter is equal to / greater than / less than the given value, to within 20% or 10% of that value. A parameter "approximately between" two given values ​​means that this parameter is at least equal to the smaller of the given values, to within 20% or 10% of that value, and at most equal to the larger of the given values, to within 20% or 10% of that value.

[0064] A preferred embodiment of the invention is described below, with reference to the accompanying figures. More particularly, the embodiment described below constitutes a sports tracking system applied to trail running (mountain running), it being understood that the various aspects of the invention are not limited to application to this physical activity.

[0065] With reference to Figures 8 and 9, the generation methods 100 and the usage methods 200 according to the first two aspects of the invention can be developed in the form of an application for a GPS watch and smartphone. They allow a trail runner to be provided, in real time, with a speed to maintain in order to limit fatigue, to optimize their race management according to their physical abilities, and the course they are facing, characterized by its slope, altitude, and type or conditions of terrain.

[0066] The generation 100 and use 200 methods described below in the context of trail running can be considered as based on the use of the concept of a runner's critical speed applied to trail running, taking into account the slope, altitude and the type or conditions of terrain, for each instant of time during an activity.

[0067] The main advantages of applying this critical speed concept to trail running include at least one of the following: a. Obtain a real-time indication during the race of the critical speed to maintain based on the runner's physical condition, which can change over the course of their training or even within a single activity, and the characteristics of the race (slope, type of terrain (or technicality), altitude, terrain conditions). The real-time aspect is particularly interesting given the highly variable terrain conditions in trail running; b. Avoid exceeding your critical speed, which would generate significant fatigue after only a few minutes, and lead to a sharp decrease in physical performance, resulting in a longer race time, or even abandonment in some cases; c. Preventing injuries that can be linked to exertion exceeding one's critical speed sustained for too long, which can also lead to withdrawal in some cases; and d. Reducing the dropout rate during a race is an important aspect for organizers as it reduces the logistics of repatriation and increases runner satisfaction.

[0068] The generation method 100 according to the first aspect of the invention is illustrated in particular by the flowchart in [Fig. 8]. This flowchart represents an algorithm capable of determining the speed-slope-time relationship of a trail runner. The algorithm's input data consists of GPS training data for trail running. This data is not necessarily measured / recorded by the invention, but can be retrieved from third-party software or devices, for example, those mentioned in the introduction. This first aspect of the invention makes it possible, based on this GPS data, to establish the individual speed-slope-time profile for the runner.

[0069] To this end, the user's speed at each instant is calculated from the derivative of position with respect to time. In addition, the type of terrain (trail, road, asphalt), the terrain conditions (wet or dry, for example), the slope, and the altitude are associated with each user's GPS position during each of their training sessions.

[0070] GPS data from several months, for example from the last 12 months, are considered in order to calculate the maximum average speed for each time condition (between 10 seconds and 1 hour; or even several hours) and slope condition (between -50% and +50%).

[0071] A mathematical model of a speed-slope-time model is then fitted with the maximum average speeds recovered for each of the conditions, of slope and duration. From this data and the files of the course of a competition (defined in (GPX format for example), the method of using the second aspect of the invention, in particular illustrated by the flowchart of [Fig.9], determines the maximum speed at which the participant must run on each portion of the race in order to maximize his performance and limit his fatigue.

[0072] This model is also adaptive to reflect the runner's changing physical condition over time, whether it improves or declines. When new training data is received, the model can be recalculated / readjusted.

[0073] Once adjusted, the user's speed-slope-time model can then be integrated into a GPS watch and smartphone application. The application retrieves real-time GPS position data, including altitude, during an activity in order to calculate, in real time, the speed 206 and the slope 205, from the derivative of position and altitude with respect to time.

[0074] The critical speed not to be exceeded under the current conditions (slope, type of terrain (or technicality), terrain conditions, altitude) is then calculated 208 using the model.

[0075] Instructions to maintain this running speed are provided to the runner via notifications issued by their GPS watch or smartphone, the notifications taking, for example, the form of audible and / or visual signals, vibrations, etc. The screen of the GPS watch or smartphone may also indicate the current speed and target speed, and / or their deviation.

[0076] Method for generating the model using training data

[0077] The method for generating the speed-slope-time model 1 (see [Fig. 1]), which characterizes the user's mountain run, according to an embodiment of the first aspect of the invention, can take place on a computer server, for example, when a prospective user registers on the platform. Their speed-slope-time model will be generated using their previously recorded training data. The generated model 100 can then be used by the smartphone application and GPS watch during a trail running activity to guide the user on the appropriate speed.

[0078] Retrieving input data

[0079] Geolocation data (or GNSS data, for example GPS data) including longitude, latitude and altitude data and time of a trail activity are received 110 and extracted from input files from, for example, a GPS watch, a smartphone, or a third-party application already used by the practitioner.

[0080] Only files less than one year old may be kept, so that the profile is representative of the practitioner's fitness level.

[0081] GNSS data contained in a structured computer file, such as .fit, .gpx or .json files for example, can advantageously be converted into matrices, so as to facilitate their processing 120 in the following steps.

[0082] Data processing

[0083] Once received 110, the GPS data is processed 120. This processing can take various forms, including the one described below, which, although preferred, is not a priori limiting to the generation process 100 according to the first aspect of the invention. In particular, in the flowcharts illustrated in Figures 8 and 9, the steps indicated by a dashed outline are not considered essential.

[0084] Treatment 120 according to his preferred version includes: a. The transformation 121 of GPS data defined in a spherical frame into Cartesian data (x,y,z ; metric); b. A linear (re-)interpolation 122 of the data from the transformation 121 at a frequency substantially between 0.02 and 10 Hz, preferably substantially equal to 1 Hz; c. The deletion 123 of the interpolated data 122 when a pause in activity is detected. The data resulting from the transformation 121 may, before linear (re-)interpolation 122, be sampled at a frequency lower than 1 Hz, particularly to conserve resources (battery or storage capacity on the non-transient medium of the device used). A lack of data for a period exceeding a predefined threshold is then considered a pause. d. The application 124 of a low-pass filter on the altitude data in order to smooth out inaccuracies in measuring devices that could distort speed and slope calculations in the following steps; e. The linear (re-)interpolation 125 of the data from the previous step 124, every meter in order to have data on a distance basis rather than a time basis for the following calculations; f. Calculating the slope 126 traversed as the derivative of the altitude with respect to the distance; g. The application 127 of a low-pass filter on the slope data from the previous step; and h. Linear (re-)interpolation of the data at a frequency of 1 Hz.

[0085] Identification of record speeds by slope and duration

[0086] The target durations and slopes considered by the algorithm are respectively between: a. 10 seconds and 1 hour, or even several hours, in regular increments of seconds, and b. -50% and 50%, in regular increments of 0.5%.

[0087] For each target duration, the calculation 130 of the average slope and the average speed at each second of the activity is advantageously applied to the data from the step by implementing a moving average (to save processing time or CPU time, and to benefit from the non-necessarily finite nature of the set of input data of this type of average, hence the evolutionary aspect of the algorithm).

[0088] Then, for each target duration and slope, the calculation 130 of the average slope and average speed at each second of the activity can be immediately followed by a suppression 131 of the data which are not within the predefined tolerances: a majority, for example 80%, of the slope within the target slope with a tolerance of a few percent, for example a tolerance of + / -2.5%.

[0089] This is followed by a step, conducted on the basis of the average slopes and the calculated average speeds 130, of deduction 140, in particular by selection, of a maximum average speed 11 (See figures 1, 2 and 3) for each target duration among the plurality of predefined target durations and each among a plurality of predefined target slopes.

[0090] During the recovery 130 of the maximum average speed for each slope and duration condition, only a speed slightly above 0 m / s, for example substantially greater than or equal to 0.5 m / s, and substantially less than or equal to 10 m / s is considered valid for the following steps. Maximum average speed values ​​that do not meet these conditions are advantageously discarded 141.

[0091] Identification of record speeds by slope and duration

[0092] The simplified example below illustrates the deduction step 140 by selecting the maximum average speeds 11 as a function of three target durations (2, 6 and 12 minutes) and three target slopes (10, 0 and -10%), for data from two activities, denoted A and B, with the record speeds for the different duration and slope conditions:

[0093] Activity A:

[0094] [Tables 1] 2 min 6 min 12 min 10% 3 m / s 2 m / s 1 m / s 0% 5 m / s 4 m / s 4 m / s -10% 6 m / s 5 m / s 4 m / s

[0095] Activity B:

[0096] [Tables2] 2 min 6 min 12 min 10% 4 m / s 3 m / s 2 m / s 0% 6 m / s 5 m / s 3 m / s -10% 5 m / s 4 m / s 3 m / s

[0097] The deduction step 140 by selection of maximum average speeds 11 consists of extracting the record speeds for each condition and leads, from the example below, to the result presented in the following table, where the best records of each of the activities A and B have been selected for each condition of duration and slope:

[0098] [Tables3] 2 min 6 min 12 min 10% 4 m / s 3 m / s 2 m / s 0% 6 m / s 5 m / s 4 m / s -10% 6 m / s 5 m / s 4 m / s

[0099] The generation method 100 according to the first aspect of the invention can then include, after the step of deducing the maximum average speed 11 for each target duration and each target slope, a recording on a non-transient medium of the maximum average speeds 11 deduced with the corresponding target durations and slopes.

[0100] It is therefore also possible to graphically project the deduced maximum average speeds 11 onto a three-axis coordinate system: slope P, time f, and critical speed Vc, as illustrated in [Fig. 2], to better illustrate the influence of the duration of the effort exerted on the user's speed, or as illustrated in [Fig. 3], to better illustrate the influence of the slope on the user's speed. In [Fig. 2], a significant decrease in average speed is observed with an increase in the duration of the effort. In [Fig. 3], a significant decrease in average speed is observed with an increase in the absolute value of the slope.

[0101] Model construction

[0102] Generation 100 of the velocity-slope-time model 1 according to the embodiment of the invention described herein may then comprise: a. The construction 151 of a two-dimensional matrix containing the maximum average speed 11 for each target duration and gradient derived from all the training data provided; and b. Based on the previously constructed matrix 151, the adjustment 152 of the model defined by the following equation, by the method of minimizing the residuals of the model V(t, p), where V is the maximum average speed; f is time; P is the slope, where [Math.l] / To- dI t+ Or : • D = reserve of achievable distance above the critical speed in meters • 1 = duration of the effort performed in seconds • a, b, and c = dimensionless coefficients of the second-degree polynomial describing the critical velocity-slope relationship, obtained by fitting data 11, • P = slope in %.

[0103] The critical velocity-slope relationship is then defined as the limit of the model V(t, p) for a time 1 that tends towards infinity; it is denoted Vc(p).

[0104] The unit for the slope is % for the convenience of runners who are used to using this unit. However, it is easy to use radians with the following formula:

[0105] [Math.2] (P radians = percent)

[0106] Note here that the critical velocity-slope relationship is defined, in the embodiment described here, as the inverse of a second-order polynomial.

[0107] Once the adjustment 152 has been made, its parameters a, b, etc. are determined and it is possible to graphically draw the corresponding model. This takes the form of a finite surface in a three-axis coordinate system: slope P, time f, and critical velocity Vc, as illustrated in [Fig. 1]. [Fig. 1] shows a decrease in average velocity associated with an increase in the duration of effort and the slope (in absolute value).

[0108] Real-time calculation algorithm for speed during an activity

[0109] An embodiment of the method for using the model as generated above is described below, in particular with reference to [Fig. 9]. It is implementable on a smartphone or GPS watch when the user, having previously generated their speed-slope-time model 1, begins a trail running activity.

[0110] The user launches the application and retrieves, on their GPS watch or mobile phone, the speed-slope-time model generated, for example, by a server, or at least the coefficients a, b, etc. of the corresponding critical speed-slope relationship. Using the application, the user traces or imports a route, preferably defined by GPS coordinates, which they intend to follow during their next trail run. The route is thus preloaded into the application.

[0111] The route retrieval 202 may more specifically include loading a tile matrix, for example those implemented by the third-party application OpenStreetMap®, over which the preloaded route will pass. The tiles are defined by a square area of ​​the mapped geographic zone, containing among other things the listed paths and roads.

[0112] These steps, which can be described as preparatory to the method of use 200 according to the second aspect of the invention, can be carried out by the user via an application interface, an illustration of which is given in [Fig.5].

[0113] During the race, the method of use according to the second aspect of the invention provides for the acquisition 203 at a frequency of 1 Hz of GPS position (latitude / longitude) and altitude data. It should be noted here that altitude data is not necessarily accessible via GPS data, or solely via GPS data, whether for the implementation of the generation method 100 according to the first aspect of the invention or that of the method of use 200 according to the second aspect of the invention; it can be provided by the GPS watch or smartphone, or any other device capable of determining the altitude at which the user is geolocated at any given moment.

[0114] Preferably, a filtering 204 of the altitude and / or a determined distance between two positions of the user between two consecutive measurement times is performed with an infinite impulse response (IIR) filter. This makes it possible to process all the data of a signal without imposing a size on the signal, or regardless of the signal size, and therefore to filter the data 204 without knowing its size a priori. Furthermore, in the event of a loss of communication network, particularly between the geolocation system, for example GPS, and the user's GPS watch or smartphone, a reconnection of 3 seconds is advantageously sufficient to regain the benefit of the information provided 210.

[0115] The method of use according to the second aspect of the invention then provides for the calculation 205 of the slope traversed at each instant as the derivative of the altitude with respect to the distance and the calculation 206 of the speed as the derivative of the distance with respect to time.

[0116] Possibly concurrently with steps 205 and 206, the method of use according to the second aspect of the invention provides for the estimation 207 of the type of terrain encountered, as well as, where applicable, the terrain conditions encountered. This estimation may more particularly include: a. Identification of the tile corresponding to the user's GPS position, b. For each path listed on this tile, calculate the distance between the GPS position and said path then c. Retrieving characterization data for the nearest path, This characterization data allows us to estimate the type of terrain traversed.

[0117] This is followed by the calculation 208 of the critical speed to be respected as a function of the slope, the altitude, and the user model, with the following equation:

[0118] [MATH 3] y — 1-6x10 ^x / v where ; c a'^p^+h'^p+c a. h is the altitude of the practitioner in meters, b. P is the slope in %, and c, b, and c are the coefficients of the second-degree polynomial describing the critical velocity-slope relationship.

[0119] Note that the coefficient “6 x 10’” corresponds to the decrease in physical capacities as a function of increasing altitude, which is illustrated by the graph in [Fig.4],

[0120] The calculated speed 206 of the practitioner and the calculated critical speed 208 can be represented on the same graph in the manner illustrated by [Fig.6], which, in relation to [Fig.7], allows us to illustrate a way in which indications of exceeding critical speed can be managed.

[0121] Preferably, however, before being compared to the speed at each instant of the user, the critical speed is corrected by applying a correction coefficient 209 to the critical speed to be respected according to the type of terrain, or even the terrain conditions. The correction coefficient to be applied 209 is determined more specifically according to the characterization data retrieved. This coefficient, denoted below as coef, is applied more specifically in the following manner:

[0122] [Math.4] corrected — COCf review

[0123] It is typically between 0.5 (path) and 1 (asphalt).

[0124] Then, if, for a period exceeding a predefined threshold value, the calculated speed 206 of the practitioner is greater than the critical speed corrected by the application 209 of the correction coefficient, the method of use 200 according to the second aspect of the invention provides for indicating it 210 to the practitioner, for example using his GPS watch or his smartphone.

[0125] More specifically, and with reference to [Fig. 7], the indication 210 given to the practitioner may be of a first level of importance, marked "LIGHT" on [Fig. 7], if the calculated speed 206 of the practitioner is greater than the critical speed corrected by the application 209 of the correction coefficient for a period greater than a first predefined threshold value and less than a second threshold value predefined, and a second level of importance, higher than the first, marked "STRONG" in [Fig. 7], if the calculated speed 206 of the practitioner is higher than the critical speed corrected by applying the correction coefficient 209 for a period exceeding the second predefined threshold value, the second predefined threshold value being higher, for example, substantially twice as high, as the first predefined threshold value. The notification of the higher importance level may be more visible and / or audible than that of a lower importance level.

[0126] Thus, each plateau in [Fig. 7] that takes a value greater than 0 m / s corresponds to sending a notification to the runner to reduce their running speed, preferably until they reach the critical speed defined by their model. Only one of the illustrated notifications is of the second level of importance.

[0127] The invention is not limited to the embodiments previously described and extends to all embodiments covered by the invention.

[0128] For example, if the user has too little data, whether in terms of quantity or variability (slopes, durations, and intensity of effort), model 1 may not be able to be generated, or may generate with too low a confidence level. A simplified test protocol could be offered to the user to quickly circumvent this limitation. This protocol would require no measuring device other than the user's GPS watch or smartphone, and might only require the user to provide a recorded route of a chosen section of road over a known distance, such as an athletics track, or a hilly route with varying gradients, for example, to supplement the received training data. Using this protocol, which the user could perform alone, a model 1 could then be generated, although with potential limitations regarding its relevance compared to complete training data.

[0129] Another improvement to the various aspects of the present invention concerns the characterization of the type of terrain traversed: trail, drivable road, or asphalt. Currently, the method of use 200 according to the first aspect of the invention is based on data provided by the OpenStreetMap® mapping service. For each listed road or path, metadata allows portions of it to be characterized. However, there are limitations to this method, including: a. A portion of a path is characterized by a "type," or even by terrain conditions, for the entire portion. However, this portion of a path may contain sufficiently significant variations in terrain to make running more or less difficult within the same section; and / or b. The metadata offered by the OpenStreetMap® mapping service comes from official country-specific classifications of roads and paths. The same classification may have different characteristics depending on the country. Furthermore, since officially unclassified roads and paths are entered by the OpenStreetMap® contributor community, the contributor's subjective interpretation of the path's classification, as well as their understanding of the numerous classification possibilities, can have a significant impact on it; and / or c. In general, the metadata offered by the OpenStreetMap® mapping service may not represent the current state of a path or road. The quality of a path may, for example, have deteriorated since its classification: appearance of pebbles due to erosion, growth of vegetation, etc.

[0130] As mentioned above, the various aspects of the present invention could be applied to other human-powered endurance activities, and in particular to cross-country skiing, cycling, or ski touring, for example, which differ from running only in that they are physical activities involving equipment that interacts between the human and their environment. These human-powered endurance activities are thus very good candidates for the application of the speed-slope-time model described above. Applying the invention to these other physical activities would make the performance optimization offered by the present invention available to the general public, based solely on their training data.

[0131] It should also be noted that the notion of "time" has been used throughout the preceding text, but that this could be replaced by a related notion, namely the notion of "endurance," where appropriate these two notions being linked by a proportionality, or more generally a determined function. These two notions can therefore be considered relatively equivalent to each other.

Claims

Demands

1. A method for generating (100) a velocity-slope-time model (1) characterizing, for a given practitioner, a human locomotor endurance physical activity, the method comprising the following steps: • receive (110) a set of geolocation data representative of the latitude, longitude and altitude of the practitioner at different times during at least one session of said physical activity, • process (120) at least part of all the received geolocation data (110) to calculate (126) representative data, per unit of length chosen, of a slope profile traversed by the user during each session, • based on at least part of the received geolocation data (110) and the previously calculated representative slope data (126), calculate (130) an average slope and an average speed of the user for each of a plurality of predefined target durations, • based on the calculated average gradients and average speeds (130), deduce (140) a maximum average speed (11) for each target duration from among the plurality of predefined target durations and each from among a plurality of predefined target gradients, and • on the basis of the maximum average speeds (11) deduced (140), generate (150) the speed-slope-time model (1) of the practitioner by determining a plurality of parameters on which the speed-slope-time model depends, by adjusting (152) said model to the maximum average speeds (11).

2. A generation method (100) according to the preceding claim, wherein, the geolocation data being defined in a spherical coordinate system, the processing step (120) of the geolocation data considered comprises, before the calculation (126) of data representative of the slope profile: • a transformation (121) of geolocation data by a change of reference frame from the spherical reference frame to a Cartesian coordinate system, then • a first interpolation (122), for example linear, on the transformed geolocation data, at a sampling frequency between 0.02 and 10 Hz, preferably approximately equal to 1 Hz, to obtain temporally sampled data, then • a second interpolation (125), for example linear, on the transformed data defined on the time basis, over a sampling distance between 0.1 and 500 m, preferably approximately equal to 1 m, to obtain geographically sampled data, so that the calculation (126) of the data representing the slope profile is a function of the geographically sampled data.

3. A generation method (100) according to the preceding claim, further comprising, after the first interpolation (122) and before the second interpolation (125), a deletion (123), from the time-sampled data, of those which do not exist in the data sampled before the first interpolation (122), and for which the elapsed time is greater than a threshold value between 1 second and 1 minute, preferably substantially equal to 10 seconds.

4. A generation method (100) according to any one of the two preceding claims, further comprising, after the first interpolation (122) and before the second interpolation (125), the application of a low-pass filter (124) on the geolocation data representative of the altitude, in order to smooth out any measurement inaccuracies.

5. A generation method (100) according to any one of the three preceding claims, further comprising, after the calculation (126) of the representative slope profile data, a third interpolation (128), for example linear, on the representative slope profile data, at a sampling frequency between 0.02 and 10 Hz, preferably substantially equal to 1 Hz, so that the calculation (130) of the average speeds is a function of the representative slope profile data calculated (126) and sampled over time.

6. Generation method (100) according to any one of the claims previous, in which the calculation (126) of the data representing the slope profile is followed, preferably immediately, by the application of a low-pass filter (127) on said data representing the slope profile.

7. A generation method (100) according to any one of the preceding claims, wherein the target durations are predefined and fall within a set of intervals ranging from a minimum value of between 1 and 600 seconds, preferably substantially equal to 120 seconds, to a maximum value of between 6 minutes and 6 hours, preferably substantially equal to 14 minutes, the intervals of the set, preferably regular and / or adjacent to each other, taking a value of between 1 second and 10 minutes, preferably substantially equal to 2 minutes.

8. A generation method (100) according to any one of the preceding claims, wherein the target slopes are predefined within a set of intervals ranging from a minimum value of -10% to -60%, preferably substantially equal to -50%, to a maximum value of 10% to 60%, preferably substantially equal to 50%, the intervals of the set, preferably regular and / or adjacent to each other, taking a value of between 0.1 and 10%, preferably substantially equal to 2%.

9. A method for generating (100) according to any one of the preceding claims, wherein the step of deducing (140) the maximum average speed for each target time is preceded by a removal (131), for each target time and each target slope, from the average slopes and calculated average speeds (130), of representative data of slopes not having a majority of values, for example at least 80% of values, located in the corresponding target slope, preferably with a tolerance substantially equal to + / -2.5%.

10. A generation method (100) according to any one of the preceding claims, wherein the step of deducing (140) the maximum average speed for each target time and each target slope further comprises a suppression (141), from the deduced maximum average speeds (140), of the maximum average speeds substantially below 0.5 m / s and substantially above 7 m / s.

11. A generation method (100) according to any one of the preceding claims, wherein the adjustment (152) of the velocity- model slope-time is implemented by implementing a method for minimizing the residuals of an equation V(t, p) defining the velocity-slope-time model, where V is the maximum average velocity,' is the time and P is the slope.

12. A generation method (100) according to any one of the preceding claims, wherein the speed-slope-time model (1) is defined by an equation V{t, p) taking the following form: p} — ^ + Ve(p)' • where D is a reserve of distance achievable above the critical speed (in meters),1 is the duration of an effort performed (in seconds), P is the slope (in %), and • whose limit, when the duration of the effort performed 1 tends towards infinity, defines a critical speed-slope relation, denoted Vc ( p ), characterizing the practitioner.

13. A generation method (100) according to any one of the preceding claims, wherein the speed-slope-time model (1) is defined in part by a critical speed-slope relation Vc{p} taking the form of the inverse of a polynomial of at least the second degree, having as its variable the slope P and whose parameters, denoted for example a, b and c, are determinable by fitting the equation V(t, p) defining the speed-slope-time model (1) on the basis of the maximum average speeds (11) deduced (140).

14. Generation method (100) according to the preceding claim, wherein the critical velocity-slope relation Vc(p) takes the form of the inverse of a second-degree polynomial: Vc(p) = where the parameters a, b and c, are determinable by fitting the critical velocity-slope relation Vc(p) on the basis of the maximum average velocities (11) deduced (140).

15. A method of using (200) a determined speed-slope-time model (1) characterizing a human locomotor endurance physical activity of a practitioner, the method of use (200) comprising the following steps: • receiving (201) coefficients defining, for the practitioner, their speed-slope-time model (1), • receiving (202) a tile matrix comprising a course that the practitioner intends to follow during a session of practice of said upcoming physical activity, then • at multiple moments, preferably at every moment, during the session: i. receive (203) geolocation data representative of the practitioner's latitude, longitude and altitude, ii. Based on at least part of the received geolocation data (203), calculate (205) a slope traversed by the user, iii. Based on at least part of the received geolocation data (203), calculate (206) a speed of the practitioner, iv. based on at least part of the geolocation data received (203), estimate (207) the type of terrain traversed by the practitioner, or even the terrain conditions during the session, v. Based on the calculated slope and speed (205 and 206), determine (208) using the speed-slope-time model (1) and the coefficients received (201), a critical speed not to be exceeded by the user, vi. depending on the estimated terrain type (207), or even the terrain conditions encountered, apply (209) a correction coefficient to the determined critical speed (208), vii. if, for a period exceeding a predefined threshold value, the calculated speed (206) of the practitioner is greater than the critical speed corrected by the application (209) of the correction coefficient, indicate this (210) to the practitioner.

16. Method of use (200) according to the preceding claim, wherein the speed-slope-time model (1) was generated by implementing a method of generating (100) a speed-slope-time model characterizing a human locomotion endurance physical activity of a practitioner according to any one of claims 1 to 14.

17. A method of use (200) according to any one of the two preceding claims, wherein each tile is defined by a surface, for example square, of the geographical area on which the practice session of said physical activity takes place or is intended to take place.

18. A method of use (200) according to any one of the three preceding claims, wherein the geolocation data are received (203) at a frequency substantially equal to 1 Hz.

19. Method of use (200) according to any one of the four preceding claims, further comprising, before the calculation steps (205 and 206) of the slope and speed of the practitioner, the application (204) of an infinite impulse response (IIR) filter on the received geolocation data (203).

20. A method of use (200) according to any one of the five preceding claims, wherein the estimation (207) of the type of terrain traversed includes: • the identification of the tile corresponding to the geolocation of the user, • for each of the paths listed on the identified tile, the calculation of the distance between the geolocation of the user and said path, and • the retrieval of characterization data of the path closest to the geolocation of the user.

21. Method of use (200) according to the preceding claim, wherein the correction coefficient to be applied (209) is determined according to the characterization data retrieved.

22. Method of use (200) according to any one of the seven preceding claims, wherein the correction coefficient to be applied (209) is substantially between 0.5 and 1 and / or wherein the application (209) of the correction coefficient consists of multiplying the determined critical speed (208) by the correction coefficient.

23. A method of use (200) according to any one of the eight preceding claims, wherein the determination (208), using the velocity-slope-time model (1) and the received coefficients (201), of the critical velocity not to be exceeded comprises calculating the critical velocity according to the following equation: y _ 1-Coeffy, WHERE: c crxp2+b'xp+c • h is the altitude of the participant, in meters; • P is the slope traversed, in %; • a, / 2, etc. are the coefficients received (201), and • Coef fvo2wÆv is a coefficient reflecting the decrease in the participant's physical capacities as a function of altitude; this coefficient is, for example, approximately equal to 6 x 10°-

24. A method of use (200) according to any one of the nine preceding claims, wherein the indication (210) given to the practitioner is of a first level of importance if the calculated speed (206) of the practitioner is greater than the critical speed corrected by the application (209) of the correction coefficient for a period greater than a first predefined threshold value and less than a second predefined threshold value, and of a second level of importance, greater than the first, if the calculated speed (206) of the practitioner is greater than the critical speed corrected by the application (209) of the correction coefficient for a period greater than the second predefined threshold value, the second predefined threshold value being greater, for example substantially twice greater, than the first predefined threshold value.

25. Product computer program, preferably recorded on a non-transient medium, comprising instructions which, when executed by at least one of a processor and a computer, cause at least one of the processor and the computer to execute the generation method (100) according to any one of claims 1 to 14 and / or the use method (200) according to any one of claims 15 to 24.

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