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WO2008129442A1 - Système et procédé d'évaluation d'un motif de mouvement - Google Patents

Système et procédé d'évaluation d'un motif de mouvement Download PDF

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Publication number
WO2008129442A1
WO2008129442A1 PCT/IB2008/051405 IB2008051405W WO2008129442A1 WO 2008129442 A1 WO2008129442 A1 WO 2008129442A1 IB 2008051405 W IB2008051405 W IB 2008051405W WO 2008129442 A1 WO2008129442 A1 WO 2008129442A1
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WO
WIPO (PCT)
Prior art keywords
movement pattern
template
movement
actual
target template
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2008/051405
Other languages
English (en)
Inventor
Richard Daniel Willmann
Gerd Lanfermann
Jürgen TE VRUGT
Edwin G. J. M. Bongers
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Philips Intellectual Property and Standards GmbH
Koninklijke Philips NV
Original Assignee
Philips Intellectual Property and Standards GmbH
Koninklijke Philips Electronics NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Philips Intellectual Property and Standards GmbH, Koninklijke Philips Electronics NV filed Critical Philips Intellectual Property and Standards GmbH
Publication of WO2008129442A1 publication Critical patent/WO2008129442A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1113—Local tracking of patients, e.g. in a hospital or private home
    • A61B5/1114—Tracking parts of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
    • A61B5/1122—Determining geometric values, e.g. centre of rotation or angular range of movement of movement trajectories
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1124—Determining motor skills
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb using a particular sensing technique
    • A61B5/1127—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb using a particular sensing technique using markers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/486—Biofeedback
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0003—Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0003—Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
    • A63B24/0006—Computerised comparison for qualitative assessment of motion sequences or the course of a movement
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B69/00—Training appliances or apparatus for special sports
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/10—Athletes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2505/00—Evaluating, monitoring or diagnosing in the context of a particular type of medical care
    • A61B2505/09—Rehabilitation or training
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0003—Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
    • A63B24/0006—Computerised comparison for qualitative assessment of motion sequences or the course of a movement
    • A63B2024/0009—Computerised real time comparison with previous movements or motion sequences of the user
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0003—Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
    • A63B24/0006—Computerised comparison for qualitative assessment of motion sequences or the course of a movement
    • A63B2024/0012—Comparing movements or motion sequences with a registered reference
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/10—Positions
    • A63B2220/13—Relative positions
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/80—Special sensors, transducers or devices therefor
    • A63B2220/803—Motion sensors
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/80—Special sensors, transducers or devices therefor
    • A63B2220/806—Video cameras
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2225/00—Miscellaneous features of sport apparatus, devices or equipment
    • A63B2225/50—Wireless data transmission, e.g. by radio transmitters or telemetry

Definitions

  • the invention refers to a method of assessing a movement pattern, a system for assessing a movement pattern using the method and to uses of the system for training of a movement and for a motor impairment rehabilitation therapy.
  • Stroke is the most prominent cause of permanent disability in the industrialized countries.
  • One of the most prominent disabilities stroke survivors suffer from is half sided paralysis of the upper limbs.
  • Rehabilitation exercises are known to be efficient in regaining motor control, provided the training is intense and the patient is guided in the therapy.
  • Technical solutions for unsupervised home stroke rehabilitation require the use of sensors for acquiring the patient's posture during exercises with computer-based movement training systems.
  • a key ingredient for ensuring high motivation and compliance of the patients is timely automated feedback to their performance.
  • US 5 078 152 a method for diagnosis and training of a muscle and joint system of a human patient is known.
  • An exercise system and an arrangement for controlling parameters of an exercise movement is used.
  • a patient performance goal is displayed during the patient exercise motion and the actual patient performance relative to the performance goal is tracked and displayed. It is a drawback of the described method that it does not distinguish individual movement patterns including hesitations during the exercises from an attempt to execute the exercise.
  • a method of assessing a movement pattern comprising the steps of: continuously monitoring sensor data which characterize a movement, recognizing the movement pattern by comparing the sensor data to an actual template, comparing the recognized movement pattern to a target template. It is an advantage of the method according to the invention that complex three- dimensional movements can be processed and recognized, even if the movement pattern under assessment is pathological.
  • the actual template which may as well be referred to as a personal template is, in the sense of the invention, a variable representation of the actual movement pattern under assessment, whereas the target template is a fixed reference, a representation of a desired movement pattern, like for example, a healthy movement pattern.
  • the actual and the target template are preferably stored as data for beneficially easy comparison to the sensor data.
  • the method is advantageously used in rehabilitation exercises. Further, the method is applicable in any kind of training situation referring to certain movement patterns, like for example sports.
  • a movement pattern in the sense of the invention is a certain exercise, which may be a comparably simple movement, like raising an arm or bending a knee or elbow, or a more complex motion sequence, like throwing an object or performing a certain kind of jump.
  • the movement pattern under assessment may also be detected in a longer motion sequence.
  • motion recognition algorithms are applied, which will be described in more detail later.
  • the sensor data are preferably obtained using body-worn sensor technology to capture a person's motions. For example, commercially available inertial sensors and piezo -resistive strain sensors are used to track motions.
  • the sensor data may advantageously be transferred wirelessly.
  • the method further comprises the step of updating the actual template, using the recognized movement pattern.
  • a feedback regarding the recognized movement pattern and/or the actual template is generated.
  • the feedback may be given in any suitable manner, in particular visually and/or acoustically.
  • a person whose movement is assessed using the method according to the invention may advantageously be given the training target represented by the target template, as well as their own movement pattern as a timely feedback and encouragement in a suitable manner.
  • the person whose movement is assessed may, for example be a patient under rehabilitation therapy or an athlete in a training session.
  • the method further comprises the step of monitoring an evolution of the actual template with respect to the target template.
  • a distance of the recognized movement pattern and/or the actual template to the target template is calculated.
  • a feedback comprises a visualized display of the distance.
  • a person performing the movement under assessment is instructed according to the target template, the instructions preferably being presented on a display by an electronic data processing means.
  • the instruction can be given as written or spoken text, as video or as a rendered movie.
  • an algorithm for recognizing the movement pattern comprises Dynamic Time Warping (DTW), the target template preferably being represented by a multidimensional time series of data.
  • Dynamic Time Warping is an algorithm which may be used for determining a similarity between two sequences which may vary in time or speed. For instance, similarities in movement patterns may advantageously be detected, even if the movement in a first sequence is executed slowly and in another sequence it is executed more quickly, or even if there were accelerations and decelerations during the execution of the monitored movement.
  • DTW is advantageously applicable to any data which can be turned into a linear representation, including video and audio data.
  • DTW is a method that allows a computer to find a match between two given sequences, as for example time series of sensor data, with certain restrictions.
  • the sequences are "warped" non- linearly in the time dimension to determine a measure of their similarity independent of certain non- linear variations in the time dimension.
  • Continuity is less important in DTW than in other pattern matching algorithms which are advantageously appropriate for the recognition of pathological movement.
  • DTW is an algorithm which is advantageously suited to matching sequences with missing information. The optimization process is performed using dynamic programming.
  • an algorithm for recognizing the movement pattern comprises Hidden Markov Models (HMM), the target template preferably being encoded in form of a set of model parameters for a Markov chain.
  • HMM Hidden Markov Model
  • the Hidden Markov Model (HMM) is known in the art to be a statistical model in which the system being modeled is assumed to be a Markov process with unknown parameters.
  • the hidden parameters are advantageously determined from the observable parameters.
  • the extracted model parameters can then be used to perform further analysis, i.e. in this case for the recognition of the movement pattern.
  • the target template is a representation of a reference movement pattern.
  • the target template may be chosen from a set of standardized movement patterns.
  • the target template is recorded by a reference person, using sensors which are appropriate for motion detection and an electronic data processing means.
  • the reference person may advantageously be a person who is capable of executing the respective movement or exercise in a desired way, for example a therapist or a trainer.
  • the actual template is recorded in an initial session by the person performing the movement under assessment, preferably under the instruction of a reference person, for example the therapist or trainer.
  • Another object of the invention is a system for assessing a movement pattern using a method according to the invention as described in here before.
  • the system comprises: - one or more sensors which are appropriate for detecting a movement, an electronic data processing means, adapted to process algorithms for recognizing the movement pattern and for comparing the recognized movement pattern to the target template, a storage for storing at least the actual template and the target template.
  • the system offers the advantage that complex three-dimensional movements are recognized, even if the assessed movement pattern shows yet no similarity to the pattern according to the target template.
  • the system may beneficially be applied in rehabilitation and/or training exercises.
  • the electronic data processing means is adapted to process an algorithm for updating the actual template using a recognized movement pattern. Movements are thus reliably recognized, advantageously also over a longer period of treatment, wherein the movement pattern under assessment is changing over the course of the treatment.
  • the system preferably comprises output means for providing feedback and/or instructions.
  • the feedback and/or instructions may be given as written or spoken text, as video or as a rendered movie.
  • the system comprises a visual and/or an audible output.
  • Another object of the invention is a use of the system according to the invention for training of one or more movements of a person.
  • Another object of the invention is a use of the system according to the invention for a motor impairment rehabilitation therapy.
  • Personalized motion analysis algorithms take the changing movement patterns of a person due to training or treatment into account and allows real-time feedback and an easy-to -understand performance measure for the patient.
  • the algorithms can be generically used for a variety of sensor types, implementation details and for all applications where training or recovery of motor abilities is essential.
  • Fig. 1 illustrates schematically an embodiment of the system according to the present invention.
  • Fig. 2 illustrates in a diagram an example of generating a target template from examples for a method according to the present invention.
  • Fig. 3 illustrates embodiments of the method according to the invention in a flowchart.
  • Figs. 4a, 4b, 4c and 4d illustrate an evolution of an actual template in schematic diagrams.
  • Fig. 5 illustrates an actual template compared to a target template in a schematic diagram.
  • first, second, third and the like in the description and in the claims are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other sequences than described or illustrated herein. Moreover, the terms top, bottom, over, under and the like in the description and the claims are used for descriptive purposes and not necessarily for describing relative positions. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other orientations than described or illustrated herein.
  • Fig. 1 refers to a system 1 for assessing a movement pattern according to an embodiment of the invention.
  • the system 1 comprises an electronic data processing means 10, a storage 12 and a display screen 11.
  • the transfer of data between the electronic data processing means 10 and the storage 12 is illustrated by first arrows 13 and to the display screen 11 by a second arrow 14.
  • One or more motion detecting sensors 20 are used which capture the movements of a person P.
  • the sensors 20 can be e.g. inertial sensors, marker- based or marker-less camera systems for motion acquisition, wearable strain sensors or others.
  • a stream of sensor data from the sensor 20 to the electronic data processing means 10 is illustrated by a third arrow 15.
  • An instruction to the person P to perform an exercise is given via the display screen 11 which may as well comprise loudspeakers.
  • the instruction can be given as written or spoken text, as video or as rendered movie.
  • a target template 23 (cf. Fig. 4a), a representation of a reference movement pattern when executing the exercise is recorded, using the sensors 20, processing the data in the electronic data processing means 10 and storing it by means of the storage 12.
  • a representation of the typical movement pattern of the person P is recorded and stored in the same way when doing the exercise as an actual template 24 (cf. Fig. 4b).
  • a first algorithm is executed on the electronic data processing means 10, to recognize the movement pattern 25 (cf. Fig. 4c) of the exercise in the stream of sensor data generated from the person's movement. For the recognition, this first algorithm compares the sensor data to the actual template.
  • a second algorithm to update the actual template is running on the electronic data processing means 10, such that the actual template reflects the change of movement patterns over the course of treatment.
  • a third algorithm to compare the personal template to the target template is running on the electronic data processing means 10. Feedback on the deviation of the actual template and/or the latest recognized movement pattern is output via the display screen 11.
  • a reference person for example a therapist or trainer defines the exercise and the target template.
  • the reference person wears the sensors 20 to capture reference movement patterns by executing the exercise several times, while the electronic data processing means 10 records the corresponding sensor data. From this sensor data, the target template is generated.
  • the generation of the target template 23 is illustrated in a diagram, showing sensor data values on the ordinate axis 21 and a number of sampling points on the axis of abscissae N. Sensor data 22 are recorded during multiple executions of the same exercise (the thin lines). The target template 23 is then generated from the sensor data 22 of the past exercises.
  • a representation of the target template 23 can either be a time series of the sensor data 22, or in a more abstract form, depending on the chosen movement recognition algorithm.
  • Possible algorithms include Dynamic Time Warping (DTW), where the target template 23 is given by a multidimensional time-series of the sensor data 22, or Hidden Markov Models (HMM), where the target template 23 is encoded in a more abstract way in form of a set of model parameters for a Markov chain.
  • DTW Dynamic Time Warping
  • HMM Hidden Markov Models
  • the target template 23 for a drinking motion is generated from examples 22.
  • the actual template (not shown) of the person is generated in an initial session. Later on, the person can use the system 1 alone.
  • the sensor data is analyzed by the movement recognition algorithm, which searches the stream of sensor data for the pattern stored in the actual template.
  • the target template 23 and the actual template may largely differ, for example if a heavily impaired person is executing the exercise.
  • any algorithm is looking for the target template 23 instead of the actual template would fail to recognize the attempts to execute the exercise, which might lead to a low compliance of the person.
  • the movement recognition algorithm triggers a feedback for the person, e.g. in form of a jingle or on-screen feedback.
  • the recognized movement pattern is used to update the actual template (e.g. by generating the actual template from the last five or the last ten repetitions of the exercise, as illustrated in Fig. 2) to ensure that the movement recognition algorithm faithfully reflects the changing capabilities of the person.
  • a flow chart illustrating the method and system according to the invention is depicted.
  • feedback on the person's movement pattern is given, using the actual template.
  • step 100 the target template and the actual template are loaded from the storage 12 (Fig. 1).
  • Step 101 represents the loading and display of instructions regarding the exercise and from step 102 on, the sensor data is continuously monitored.
  • the actual template is used to recognize the movement pattern of the exercise (step 104). Any time an attempt to execute the exercise is recognized, a visual or audible feedback is given in step 103 and the actual template is updated by the recognized movement pattern in step 105.
  • the updated actual pattern is now used by the recognition algorithm in steps 102 and 104.
  • a deviation of the updated actual pattern from the target pattern is calculated and given as a feedback in step 106.
  • Figs. 4a, 4b, 4c and 4d an evolution of the actual template 24 is illustrated.
  • the target template 23 (Fig. 4a) and the actual template 24 (Fig. 4b) show a marked difference.
  • the data of the recognized movement pattern 25 (Fig. 4c) is used to update the actual template 24 from the old actual template (Fig. 4d, lower curve) to the new actual template (Fig. 4d, top curve), the new actual template 24 coming closer to the target template 23.
  • a possible update mechanism can be training the actual template 24 according to algorithms used for DTW or HMM, where only the recorded sensor data of the last executions (e.g. last 5 or 10) of the exercise are used.
  • the person can receive feedback on the deviation from the desired reference movement by calculating and visualizing the distance between the target template 23, representing the reference movement pattern and the actual template 24, representing the actual movement pattern of the person. Visualization can be achieved by showing two rendered Figures where one shows the motion encoded in the target template 23 and the other in the actual template 24, or by an alignment of time series in case of DTW (see Fig. 5).
  • a time series graph is depicted, comparing the actual template 24 or recognized movement pattern data 25 to the target template 23. In this case, the differences are minimal.

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Abstract

L'invention porte sur un procédé d'évaluation d'un motif de mouvement, sur un système pour évaluer un motif de mouvement à l'aide du procédé et sur des utilisations du système pour s'entraîner à un mouvement et pour une thérapie de rééducation d'une déficience motrice. Des algorithmes d'analyse de mouvement personnalisés prennent en compte les motifs de mouvement changeant d'une personne dus à un entraînement ou à un traitement et permettent un retour d'informations en temps réel et une mesure de la performance facile à comprendre pour le patient. Les algorithmes peuvent être utilisés de façon générique pour une diversité de types de capteur, de détails de mise en œuvre et pour toutes les applications où un entraînement ou une récupération de capacités motrices est essentielle.
PCT/IB2008/051405 2007-04-20 2008-04-14 Système et procédé d'évaluation d'un motif de mouvement Ceased WO2008129442A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP07106628 2007-04-20
EP07106628.6 2007-04-20

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WO2008129442A1 true WO2008129442A1 (fr) 2008-10-30

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Cited By (12)

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WO2011013099A1 (fr) * 2009-07-31 2011-02-03 Koninklijke Philips Electronics N.V. Procédé et système pour attribuer un programme d’entraînement à un sujet
NL2004660C2 (en) * 2010-05-04 2011-11-07 Technologies88 B V Device and method for motion capture and analysis.
CN104809325A (zh) * 2014-01-26 2015-07-29 国际商业机器公司 用于检测事件日志和过程模型之间的区别的方法和装置
EP2636021A4 (fr) * 2010-11-04 2016-06-15 Mordechai Shani Analyse et surveillance assistées par ordinateur d'anomalies de mobilité chez des patients humains
WO2016097655A1 (fr) 2014-12-18 2016-06-23 Universite Grenoble Alpes Systeme et procede de controle du mouvement cyclique d'un segment corporel d'un individu
CN106344031A (zh) * 2016-08-29 2017-01-25 常州市钱璟康复股份有限公司 一种基于声音反馈的步态训练与评估系统
US9589207B2 (en) 2013-11-21 2017-03-07 Mo' Motion Ventures Jump shot and athletic activity analysis system
JPWO2018042525A1 (ja) * 2016-08-30 2019-04-11 富士通株式会社 情報処理装置、情報処理システム、情報処理方法、および情報処理プログラム
JPWO2018127947A1 (ja) * 2017-01-04 2019-11-07 富士通株式会社 情報処理装置、情報処理システム及び情報処理方法
US10664690B2 (en) 2013-11-21 2020-05-26 Mo' Motion Ventures Jump shot and athletic activity analysis system
WO2020193945A1 (fr) * 2019-03-28 2020-10-01 270 Vision Ltd Système et procédé pour améliorer l'amplitude de mouvement d'un patient
US11269410B1 (en) 2019-06-14 2022-03-08 Apple Inc. Method and device for performance-based progression of virtual content

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