Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

• the present invention relates to the field of data analysis relating to the field of cognitive and behavioral neurosciences.
• the invention relates more particularly to a method for analyzing fine motor skills by acquiring and analyzing the movements of an individual, and to a device for carrying out this method.
• Fine motor skills correspond to the execution of precise movements, in particular thanks to the muscles of the fingers, and more generally of the hand, or of the face and to the muscular control of the muscles concerned. Gripping and manipulating small objects, performing painstaking gestures, such as drawing or writing, or controlling facial muscles are examples of fine motor skills.
• Rosenblum exploit dysgraphia, a disorder of written expression, in order to identify neurodevelopmental disorders such as attention deficit with or without hyperactivity, autism spectrum disorders, or coordination acquisition disorders. These methods are based on the analysis of handwriting in children being in particular in primary classes, that is to say knowing how to write. However, writing is a complex and codified gesture. In addition, the evolution of learning to write can last more than ten years, as described in the publications Handwriting development, written by KP Feder et al. and Factors that relate to good and poor handwriting, written by H. Cornhill et al. These methods therefore do not allow the early identification of neurodevelopmental disorders.
• the invention aims to meet this need and it achieves it, according to one of its aspects, by virtue of a method for acquiring and analyzing the fine motor skills of an individual, comprising the following steps: a) presentation of at least one course on a support, inviting the individual to perform a free movement with at least one finger and / or an accessory on the support, this movement being linked to the course presented, b) recording of an execution time of at least part of the route, c) recording of the successive positions of the finger and / or of the accessory when performing at least part of the route, d) analysis of the recordings to generate at least one random variable describing the successive positions of the finger and / or the accessory according to a predefined statistical model, e) generation of a score representative of fine motor skills, from at least the duration of carrying out at least part of the route and of a statistical measurement of the random variable, characteristic of a quantity of information, of a disorder or of a chaos contained in the recording of the successive positions of the finger and / or accessory.
• Such a method is implemented with computer means.
• the recording is done in a computer memory, and the analysis and generation of the score is done by computer.
• the invention is based on a statistical modeling of the movement performed by the individual with the finger and / or the accessory when performing the course.
• the statistical measurement resulting from this modeling makes it possible to highlight an individual's cognitive and motor functions, such as attention, planning and memorization, which are important markers of good health.
• the invention enables the possible presence of pathologies to be detected simply and quickly, in particular by highlighting an atypical course in children, an abnormal decline in the elderly, or a progression or regression during therapeutic treatment.
• One of the advantages of the method according to the invention is that it allows the early identification of cognitive and / or motor disorders via the analysis of fine motor skills regardless of age, language or level of mastery of the language. 'writing.
• the course can in particular be done without instruction and without special knowledge, making it easier for any individual to complete.
• the course can be done remotely by the individual, for example at home or at school, and does not require a trip to a health specialist.
• the analysis of the movement performed makes it possible in particular to highlight hesitations or errors when completing at least part of the course.
• the movement can be analyzed in particular by recording the positions of the finger and / or the accessory when performing at least part of the route.
• the recording of speeds and / or accelerations of the movement of the finger and / or the accessory and / or the recording of finger pressure and / or the accessory on the support and / or the recording of inclinations of the finger and / or the accessory with respect to the support during production of at least part of the course can allow a more detailed analysis of the movement.
• the inclinations of the finger and / or the accessory can be acquired by image analysis with optionally optical markers attached to the finger and / or the accessory (colored, reflected pellets, light emitters), and / or the accessory can possibly include an accelerometer.
• the acquisition of the recordings is carried out with a constant sampling frequency.
• the at least part of the route corresponds to the route as a whole.
• Carrying out several courses that may have varying degrees of difficulty makes it possible to characterize more precisely the cognitive and / or motor capacities of the individual.
• the statistical modeling of these recordings makes it possible to carry out the analysis by quantifying the characteristics of the movement such as the fluidity and / or the control of the movement, in particular via the determination of a statistical measurement.
• the statistical measure can be an entropy measure, including multiscale entropy, coarse entropy, "sample entropy", Tsallis entropy, or differential entropy.
• Entropy makes it possible to measure the disorder and the uncertainty of the actions carried out by the individual during the completion of the course.
• the statistical measure can also be defined from a measure of chaos, such as a measure of fractal dimensions or a measure of Lyapunov exponent.
• the statistical measure is a differential entropy, which can be described by the following formula:
• the probability density of the random variable can be estimated by a statistical model, in particular by a mixture of Gaussians (GMM).
• GMM mixture of Gaussians
• the differential entropy can then be defined by [Math 2] where ⁇ is the covariance matrix and N the dimension of the Gaussians, for example equal to 2, corresponding for example to the dimension of the coordinates of the positions of the finger and / or of the accessory.
• the number of Gaussians can be determined so as to limit the calculation time of the statistical measurement while retaining an amount of information contained in the recording sufficient for the analysis.
• the mixture of Gaussians comprises for example 30 Gaussians or more.
• the differential entropy measure of a Gaussian is related to its variance and quantifies the dispersion of the Gaussian distribution.
• a disorderly movement will therefore have a high entropy measure, unlike a fluid gesture.
• Variance can be defined as a statistical measure, as well as an average or any other value relative to the statistical model.
• the statistical model can be a hidden Markov model, characterized by a number of states S.
• the states can correspond to portions of the path, that is to say to a position or a set of successive positions. finger and / or accessory.
• the number of S states may depend on the length of time the individual completes the course.
• the states may correspond to portions of the course each extending over the same duration and / or the states may include the same number of successive positions of the finger and / or of the accessory. Thus, the longer the realization time, the greater the number of S states.
• the number of S states can be predefined.
• the states may in particular include a number of successive positions of the finger and / or of the different accessory, and / or correspond to portions of the course extending over variable durations.
• An intermediate statistical measure can be calculated for each of the S states and / or an intermediate realization time can be recorded for each state of the hidden Markov model.
• the statistical measure can be defined from the intermediate statistical measures and / or the completion time can be defined from the intermediate periods.
• An intermediate score representative of fine motor skills can be generated for each state of the hidden Markov model, from at least the intermediate duration of the portion corresponding to the state and the intermediate statistical measure calculated for the state, the score that can be defined from intermediate scores, statistical measures intermediate, and / or intermediate durations.
• the analysis of fine motor skills is advantageously finer and more precise, making it possible to locate the portion or portions of the course where the individual has had atypical behavior.
• the statistical measurement in combination with at least the duration of the course of the course advantageously makes it possible to generate the score representative of fine motor skills.
• Steps a), b), c), d) and e) are advantageously repeated several times for the same individual, preferably between 2 and 40 times, better between 10 and 30, even better about 20 times. An average score can then be calculated from the scores of each step e).
• steps a), b) and c) are repeated several times for the same individual, and steps d) and e) are carried out once from the recordings of steps b) and c) repeated several times.
• the sum of the times for carrying out step b) is less than 10 minutes.
• An individual's concentration may decline when completing at least part of the route or several routes take too long.
• Parameters other than successive positions and duration, can help characterize an individual's fine motor skills more precisely, further improving the analysis.
• a movement having a constant speed during the realization of the course is in particular representative of a controlled movement.
• a more detailed analysis of the movement is for example carried out by one or more additional recording (s), of speeds and / or accelerations of the finger and / or of the accessory and / or of pressure of the finger and / or of the accessory on the support and / or inclinations of the finger and / or the accessory with respect to the support when performing the course, the score being generated at least from this or these recording (s).
• the analysis of the recording (s) is carried out so as to generate at least one random variable describing the speed and / or one random variable.
• the score generated in step e) being calculated from at least one statistical measurement of the at least one random variable characteristic of a quantity of information, of a disorder or of a chaos contained in the recording (s) of the speed, acceleration, pressure or inclination of the finger and / or the accessory.
• the identification of atypical behaviors is preferably carried out by comparing the score representative of fine motor skills with at least one reference score.
• the reference score is for example an average score.
• the score can be compared to scores recorded in a database. These recorded scores can be derived from implementations of the method according to the invention.
• the comparison can in particular be carried out by calculating a z-score then by performing a thresholding; the z-score expressing the deviation from the mean, the thresholding then makes it possible to highlight significant deviations from this mean.
• the comparison can also be carried out by means of a machine learning method, in particular a classification method, for example K-means, hierarchical classification, GMM (Gaussian Mixture Model), k-medoids, DBSCAN (density-based spatial clustering of applications with noise), k nearest neighbors, random forest, graph method.
• classification methods may or may not be supervised. This list is not exhaustive.
• the comparison is preferably carried out to compare scores of individuals that are substantially similar, particularly in terms of age, education, and / or language.
• the comparison is preferably carried out for substantially similar degrees of difficulty of course and / or for identical courses.
• the comparison can also be carried out for the same individual performing for example different routes, and / or performing one or more routes at different moments in time. These different moments in time can correspond to a moment before treatment and a moment after treatment.
• An evolution of the fine motor skills of the individual can be determined by comparing the scores resulting from the achievements of the course at different points in time. The comparison can be carried out on the basis of intermediate scores, allowing the identification of the portions comprising an atypical behavior of the individual.
• An alert can be generated based on the result of the comparison, for example with the aim of carrying out a more detailed analysis a posteriori, for example by presenting the individual with different paths which may be of different difficulties; the alert can be communicated, for example automatically by email, sms, by being displayed on the support and / or recorded in a database, to a specialist in cognitive and / or motor disorders, to a doctor, to a person in charge of the individual, to the individual and / or a relative of the individual.
• the generation of the score, the comparison and / or the generation of an alert can be carried out at a distance from the completion of the route and the detection of successive positions and / or the duration of completion of at least part of the route.
• the route can be randomly generated and / or saved.
• the course presented can be a labyrinth.
• a labyrinth preferably defines a single path connecting a starting point to an ending point.
• the labyrinth can have at least one dead end path.
• the labyrinth has several dead end paths.
• a degree of difficulty can be selected, the number of dead end paths in the labyrinth increasing with the degree of difficulty.
• the course is a set of points to be connected.
• the points are to be connected in a predetermined order.
• each point can contain information such as a number and / or a letter, defining in particular the order in which the points are to be connected.
• the set of points to be connected can contain only one type of information, for example only points containing numbers or only points containing letters, or different types of information, for example points containing numbers and points containing letters.
• Points can contain other types of information such as mathematical operations, colors, shapes. Instructions, for example audio or written, can if necessary be broadcast to guide the individual in his journey.
• the selection of a degree of difficulty can define the number of points to be connected and / or the type of information contained in the points to be connected and / or the number of types. of different information contained in the set of points to be connected, the number of points to be connected and / or the number of types of information increasing with the degree of difficulty.
• the presentation of the course may include its display on the support.
• the presentation comprises the display of the route, in particular a labyrinth or a set of points, on a screen, the screen preferably being tactile.
• a trace of the trajectory of the finger and / or the accessory can optionally be displayed on the support.
• the display of the track tends to increase the degree of difficulty by increasing the amount of visual information to be understood.
• the invention also relates, according to another of its aspects, to a system for acquiring and analyzing fine motor skills for implementing the method according to the invention, comprising: a support suitable for displaying the route , a timing means for measuring a duration of completion of at least part of the route, a detection means, detecting the positions of the finger and / or of the accessory when performing at least part of the route, a memory in which the positions of the finger and / or the accessory and / or the duration of at least part of the route can be recorded, a processing and analysis means for generating a score representative of motor skills fine from at least the duration of completion of at least part of the route and a statistical measurement, characteristic of a quantity of information, of a disorder or of a chaos contained in the recording of positions of the finger and / or the accessory, itself generated from r of a random variable representative of the positions of the finger and / or the accessory.
• the support may comprise a screen, preferably comprising a touch interface making it possible to follow the movement of the finger and / or of the accessory during the production. of the course.
• the support can be a tablet, a cell phone, or an interactive whiteboard among other possibilities.
• the accessory used by the individual to complete at least part of the course may be a tablet-operated tool, such as a stylus, or any suitable writing tool such as a pencil, chalk, pen or felt-tip pen.
• the accessory may include a sensor, for example capacitive, optical, thermal, pressure, ultrasonic.
• the timing means may be an algorithm executed by computer means, this algorithm preferably being included in the program executed by the processing and analysis means, determining the duration of completion of at least part of the route from the recordings of the successive positions of the finger and / or the accessory.
• the processing means preferably comprising an internal clock, it is possible to determine the duration of completion of at least part of the route, knowing the sampling frequency at which the successive positions are recorded.
• the timing means may alternatively be separate from the processing and analysis means, being for example a specialized electronic circuit or a program executed during the acquisition independently of a program executed for the processing and analysis of the records.
• the timing device can be included in the support.
• the detection means can be a sensor, in particular a capacitive, optical, thermal, pressure or ultrasonic sensor.
• the detection means can be included in the holder. Alternatively, it is separate from the support.
• the detection means can be integrated into the support, in particular integrated into a camera, a tablet, a mobile phone, or an interactive whiteboard.
• the detection means may be a camera observing the support, for example a color camera, for example of the Kinect type.
• the detection means can also detect the speed and / or the acceleration and / or the pressure of the finger and / or the accessory on the support and / or the inclination of the finger and / or the accessory with respect to the support.
• the processing means can be included in the support and / or in the detection means.
• the processing and analysis means may include a processor.
• processing algorithms make it possible to automatically generate the score from the recordings.
• the processing and analysis means may include supervised or unsupervised learning methods performing the analysis of the score generated for the individual.
• the processing and analysis means can be placed at a distance from the detection means and / or the timing means and / or the support and / or the memory.
• the processing and analysis means are, for example, a computer, a tablet, a cell phone, an interactive whiteboard, a smart watch that can for example be connected to a tablet, an on-board camera, or a computer program located in the cloud.
• the processing and analysis of the recordings can be performed in real time or in deferred mode.
• the memory preferably comprises a database, the database possibly comprising scores representative of the fine motor skills of a set of individuals, preferably of any age, records of successive positions, durations of completion of at at least part of at least one course, personal information relating to the set of individuals and / or to the individual, or values and / or algorithms intended for the analysis of the score.
• the memory can be included in the medium and / or in the processing and analysis means and / or in the detection means, being for example a RAM bar, a hard disk, a USB key, an SD card or a circuit. integrated for example on a processor. Preferably, access to this data is secure, protected for example by a password.
• FIG 1 Figure 1 schematically shows a device according to the invention
• FIG 2 is a block diagram of various elements of a device according to the invention.
• FIG 3 Figure 3 illustrates different steps of a process according to the invention
• FIG 4 shows a comparison of scores representative of fine motor skills using a classification method
• FIG 5 shows another comparison of scores using a classification method
• Figure 6 illustrates an example of a route in progress
• Figure 7 shows examples of routes with different degrees of difficulty.
• FIG. 1 shows a system for acquisition and analysis 1 of fine motor skills according to the invention, comprising a touchscreen tablet 2, for example a Wacom type tablet, and an accessory 11, the accessory 11 being here a stylus.
• the accessory 11 comprises, for example, a tip, an individual being able to position and move the accessory more precisely on the tablet 2.
• the tablet 2 makes it possible to display a route and to detect the successive positions of the finger and / or of the accessory 11 on the screen when performing at least part of the route.
• the accessory 11 can also help to detect successive positions, for example by including a capacitive, optical, thermal, ultrasonic and / or pressure sensor.
• the tablet 2 comprises a processing and analysis means composed in particular of a processor, analyzing the successive positions and being able to determine a duration of completion of at least part of the route, from the acquisition of the successive positions and of 'an internal clock.
• the tablet 2 executes a program generating and analyzing a score representative of the fine motor skills of the individual. This score can be compared to a set of scores taken from a database, the database possibly being located in a memory of the tablet. The score and / or the successive positions and / or the duration of completion of at least part of the course can be recorded in the memory.
• the acquisition and analysis system 1 comprises a display medium 10, a detection means 12, a processing and analysis means 13, a memory 14 and a timing means 15, as shown. in figure 2.
• the display medium 10, the detection means 12, the processing and analysis means 13, the timing means 15 and the memory 14 may or may not be distinct from each other.
• the whole can be grouped together within a single device, for example the touchscreen tablet shown in Figure 1.
• the support 10 can alternatively be a sheet of paper, a non-touch screen, or a board.
• the detection means 12 may comprise an optical, capacitive, thermal, ultrasonic and / or pressure sensor, which may be located in or under the support 10 and / or be located at a distance from the support, the detection means 12 being for example a touch screen or a camera.
• the timing means 15 can be an algorithm, preferably included in the processing and analysis means 13, determining the duration of completion of at least part of the route from the recordings of the successive positions of the finger and / or of the accessory.
• the processing and analysis means 13 preferably includes an internal clock and the duration of completion of at least part of the path can be determined from the sampling frequency at which the successive positions are recorded.
• the processing and analysis means 13 can be any type of processor, in particular be identical to those found in computers, tablets, cell phones, smart watches, or on-board cameras, among others.
• the memory 14 can contain a database containing recorded scores and / or a set of data making it possible to carry out the processing and the analysis, for example a threshold value beyond which an alert is triggered, programs computer, personal information relating to the individual allowing for example to refine the analysis, such as his age, his level of education, his medical history. Access to this personal information is preferably secure, for example by being protected by a password or a handwritten signature of the individual, of a healthcare professional or of any other person having a right of access to these. personal informations.
• the memory 14 is preferably included in the processing and analysis means 13.
• the processing and analysis means 13 and / or the memory 14 can be located at a distance from the support 10 and the detection means 12.
• the initial step 101 involves the presentation of the course 26 to the individual on the support 10, inviting the individual to perform a free movement 20 with the finger and / or the accessory on the support 10.
• the duration of completion of at least part of the route as well as the successive positions MM, Mi of the finger and / or of the accessory on the support 10 when performing at least part of the route are the subject of acquisitions 102, 103.
• the acquisition 102 of the duration of completion of at least part of the path, and / or the acquisition 103 of the successive positions of the finger and / or of the accessory on the support 10 can be carried out as soon as the finger and / or the accessory are detected on the support 10.
• the acquisition 103 is preferably carried out with a constant sampling frequency.
• the execution time can be determined by computer from the number of acquisitions and the sampling frequency.
• the score representative of fine motor skills can be generated in step 107 thanks to the knowledge of the duration of production and to the analysis 105 of the recordings of the positions comprising the generation of at least one random variable describing the successive positions of the finger. and / or the accessory according to a predefined statistical model, from which a statistical measurement is calculated.
• the successive positions of the finger and / or the accessory can be described by a series of random variables.
• the statistical model is a mixture of Gaussians, the statistical measure preferably being differential entropy.
• the recordings of the successive positions MM, Mi associated with the movement 20 of the individual on the support 10 when carrying out at least part of the route can be represented by a set of temporal coordinates (x (t), y (t)), the random variable consisting of at least a part, better still of all of these temporal coordinates.
• the random variable can also include temporal coordinates resulting from the realization of one or more routes.
• a density function associated with the generated random variable can be determined from the statistical model, which can be described by the following formula in the case of a mixture of Gaussians:
• the statistical measure can be calculated using the following formula:
• N 2 being the dimension of the temporal coordinates
• the statistical measure being a differential entropy
• the statistical model a mixture of Gaussians.
• Differential entropy is related to the variance of Gaussians, quantifying the dispersion of records from successive positions.
• the slower the individual's movement for example because of a reflection, a hesitation, a motor problem, and / or the more the individual goes backwards, for example having a gesture disordered, the more the Gaussians are spread out, then comprising a high variance and therefore a high differential entropy.
• the statistical model can be a Hidden Markov Model (HMM).
• HMM Hidden Markov Model
• the hidden Markov model is notably characterized by a number of states S.
• the states can each be characteristic of a portion of the movement performed by the individual, a state grouping for example a set of successive positions, the portions preferably being distinct.
• the portions can extend over substantially similar durations, the number of states S then depending on the duration of completion of at least part of the route.
• the portions can extend over different durations, the number of states being, for example, predefined.
• the states can also each be characteristic of completing at least part of the route, with the individual completing several routes, for example between 2 and 40 routes, better between 10 and 30, even better around 20.
• the states can still be characteristic of portions of the movement performed by the individual when performing several routes.
• An intermediate statistical measure is preferably calculated for each of the S states.
• the statistical measure is preferably defined from intermediate statistical measures characteristic of a state.
• Intermediate statistical measurements can be derived from the analysis of portions comprising the generation of an intermediate random variable describing, for example, the successive positions included in each portion according to an intermediate statistical model, for example a mixture of Gaussians.
• Intermediate measures can be differential entropies.
• Intermediate scores representative of a portion of the movement performed by the individual to complete the course, can be generated from the intermediate statistical measurements and the durations over which said portions extend.
• the score can be a combination of the intermediate scores.
• the score can be a weighted sum of at least the statistical measurement representative of the successive positions of the finger and / or the accessory and of the duration of the course.
• the score can alternatively be a vector comprising at least the statistical measurement representative of the successive positions of the finger and / or of the accessory and the duration of the course.
• the method according to the invention comprises in particular a step 108 of analyzing the score.
• the score can for example be compared automatically with scores included for example in a database.
• the score can be subjected to a method of classification, supervised or not, for example 2D clustering methods as shown in the examples of Figures 4 and 5.
• the score is compared with a predefined threshold value, being for example an average score calculated from a set of scores resulting, for example, from the implementation of a method according to the invention.
• the score can be recorded in step 109.
• the score can enrich a database that can be used for analysis step 108 by comparison, and / or participate in the definition of an average score.
• the method may also include the automatic generation of an alert 110 to warn of the detection of atypical behavior.
• an alert can be triggered in order to draw attention to scores substantially similar to those of group A in figures 4 and 5, these scores being isolated from the other groups and corresponding to a high duration of the course and high differential entropy.
• FIG. 4 represents an example of comparison 108 of scores in the form of normalized vectors, the values of the duration of realization and of the statistical measurement being between 0 and 1.
• the comparison 108 is carried out in this example by means of a method 2D clustering, defining eight different groups, based on 258 scores of different individuals, in particular of all ages. Group A contains in particular three scores that are far removed from the other scores. An alert can be generated 110 concerning these three individuals, for example with the aim of carrying out new routes, or medical tests.
• FIG. 5 represents another example of comparison 108 of twenty-seven scores of individuals having substantially the same age by means of a 2D clustering method from normalized vectors comprising the duration of the course and the statistical measurement.
• the scores are classified into three groups. Group A, with a single score, may correspond to atypical behavior relating to fine motor skills.
• Completing at least part of the route can easily be done remotely and thus facilitates the implementation of the method according to the invention. Indeed, the individual can complete the course alone, that is to say without the supervision of a professional, especially in health.
• the records, and / or the score and / or the analysis can be sent a posteriori, for example by Internet, to a server which carries out the data processing.
• the method may include step 104 of recording additional parameters and step 106 of analyzing them, in order to obtain a score.
• the parameters possibly being for example the speeds of the finger and / or of the accessory, the accelerations of the finger and / or of the accessory, the pressures of the finger and / or of the 'accessory on the support 10, and / or the tremors of the finger and / or of the accessory and / or the inclinations of the finger and / or of the accessory with respect to the support when carrying out the course.
• This list is not exhaustive.
• These parameters can be acquired thanks to a detection means, or be determined thanks to a computer routine, for example determining an average or calculating a number of round trips.
• a Wacom type tablet can detect and acquire the pressures and inclinations of the accessory relative to the tablet.
• FIG. 6 illustrates an example of a route 26 that can be presented to an individual on a support, inviting him to perform a free movement 20 with his finger and / or an accessory 11.
• the successive positions Mi, MM are recorded with a pitch At separating two successive positions preferably constant.
• An area 24 and / or a graphic element 23 to be selected can represent the position of the finger and / or the accessory on the support 10, making it possible in particular to visualize the position of the individual on the course.
• Zone 24 gives the individual a larger space, making it easier to select for the purpose of completing the course.
• the zone 24 can for example disappear when the individual selects the zone 24 and / or the graphic element 23.
• the acquisition 102 of the duration and / or the acquisition 103 of the successive positions and / or the acquisition 104 of the others. parameters can be performed only when the individual selects zone 24 and / or graphic element 23.
• a trace 20 representing the movement of the finger and / or the accessory 11 on the support 10 can be displayed, making it possible to visualize the progress of the individual on the course.
• the trace 20 can represent the exact movement of the finger and / or the accessory on the support 10, or it can be represented by means of segments.
• a trace showing a solution of the course can also be presented to the individual at the end of the exercise, if necessary.
• the path 26 can be a labyrinth, comprising at least one dead end path 25 preferably defining a single path connecting a starting point 21 to an ending point 22.
• the path can be delimited by walls which may or may not be rectilinear, the labyrinth taking for example the shape of an intestine.
• FIG. 7 illustrates three degrees of difficulty of “easy”, “medium” and “difficult”, the number of dead-end paths increasing as a function of the degree of difficulty.
• the path 26 can be generated randomly, for example by means of a computer program, for example included in the processing and analysis means 13. This path 26 can be recorded, for example in the memory 14, which can be presented several times. times, for example to several individuals in order to compare their score, and / or to the same individual, for example at different times, these different times possibly corresponding in particular to an evaluation before therapeutic treatment and to an evaluation after therapeutic treatment in order to assess the evolution of the individual's motor skills and of certain cognitive functions during said treatment and / or the effectiveness of said treatment.
• the support may be a sheet of paper or any other material on which the path can be printed or drawn, the sheet or the material being for example positioned on a touch screen, or a camera capable of filming the movement of the finger and / or accessory on the sheet of paper or material. The movement can then be broken down in order to define successive positions, in particular by means of a computer program.
• the acquisition of successive positions can be carried out by digitizing a trace left by the individual on the support when carrying out at least part of the route, for example with a pen or pencil on a sheet of paper. paper, the duration of completion of at least part of the route can be acquired separately, for example with a stopwatch.
• the steps of acquiring successive positions and / or other parameters can be followed by pre-processing steps in order to standardize the records, typically between 0 and 1, or to identify missing records, for example. These pre-processing steps notably facilitate the generation and analysis of the score.
• ADHD attention deficit hyperactivity disorder
• H. Cornhill et al. Parkinsonism reduces coordination of fingers, wrist, and arm in fine motor control, H.L. Teulings,

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• 2020
  • 2020-01-15 FR FR2000383A patent/FR3106234A1/fr active Pending
• 2021
  • 2021-01-13 EP EP21701061.0A patent/EP4091176A1/de active Pending
  • 2021-01-13 WO PCT/EP2021/050586 patent/WO2021144307A1/fr unknown
  • 2021-01-13 US US17/792,994 patent/US20230047530A1/en active Pending

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