WO2011003218A1 - Procédé et système d'identification de mouvement d'accélération - Google Patents

Procédé et système d'identification de mouvement d'accélération Download PDF

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Publication number
WO2011003218A1
WO2011003218A1 PCT/CN2009/000770 CN2009000770W WO2011003218A1 WO 2011003218 A1 WO2011003218 A1 WO 2011003218A1 CN 2009000770 W CN2009000770 W CN 2009000770W WO 2011003218 A1 WO2011003218 A1 WO 2011003218A1
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WO
WIPO (PCT)
Prior art keywords
data
motion
acceleration
module
action
Prior art date
Application number
PCT/CN2009/000770
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English (en)
Chinese (zh)
Inventor
韩铮
Original Assignee
Han Zheng
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 Han Zheng filed Critical Han Zheng
Priority to PCT/CN2009/000770 priority Critical patent/WO2011003218A1/fr
Priority to CN200980161332.5A priority patent/CN102667672B/zh
Publication of WO2011003218A1 publication Critical patent/WO2011003218A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • G01P15/18—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration in two or more dimensions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
    • G01C21/185—Compensation of inertial measurements, e.g. for temperature effects for gravity
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P7/00—Measuring speed by integrating acceleration
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches

Definitions

  • the present invention relates to an acceleration motion recognition system and method thereof, and more particularly to an acceleration motion recognition system based on non-gyro technology and a method thereof. Background technique
  • motion recognition devices can use optical sensing positioning methods and motion acceleration sensing positioning algorithms respectively. Although these two methods can achieve basic motion positioning and recognition, they all have certain technical limitations. Motion recognition based on optical sensing positioning method
  • the system uses a magnetic base station that generates 1/50 of the magnetic field strength to form an orthogonal magnetic field coordinate system.
  • the control device moves in this magnetic field coordinate system, it can reflect the relative coordinates of the object in the magnetic field coordinate system in real time. Spatial attitude, with high positioning accuracy and real-time.
  • the disadvantage of this method is that the larger the system is not convenient to carry, and the effective range of the magnetic field is subject to the transmission power intensity of the magnetic field generating base station, and is not suitable for ordinary computer users.
  • Motion recognition based on motion acceleration sensor and gyroscope
  • FIG. 2 is a structural diagram of the data acquisition and transmission module.
  • the module is mainly composed of three parts: a three-axis acceleration sensor, a micro-processor and a data transmission module. Each module communicates via a bus.
  • the functions of each sub-module are as follows:
  • the triaxial acceleration sensor uses a fixed frequency F acceleration data in the range of N(g) (g represents the magnitude of the standard gravitational acceleration, that is, the motion acceleration data in the range of negative N times gravitational acceleration to positive N times gravitational acceleration) And updating the fixed memory address space in the acceleration sensor at a fixed frequency F, each set of data consisting of the acceleration value of the X ⁇ Y ⁇ Z axis and the temperature of the sample point, and storing each set of data in the three-axis acceleration sensor In the data storage unit.
  • F acceleration data in the range of N(g) (g represents the magnitude of the standard gravitational acceleration, that is, the motion acceleration data in the range of negative N times gravitational acceleration to positive N times gravitational acceleration)
  • F the fixed frequency
  • the low-power single-chip microcomputer or ARM processor is used to communicate with the acceleration sensor module through the bus interface.
  • the micro control unit also reads the data storage unit of the acceleration sensor at a fixed frequency F, encrypts each set of data according to the public key and encryption algorithm in the chip, and stores the encrypted data in the data of the microcontroller. The unit is cached. And send the encrypted data to the data transmission module.
  • Data transmission module
  • the wired interface can use various protocols such as USB, serial port, parallel port, and fire wire.
  • the wireless interface utilizes a RF baseband chip to support 100Kbps of data transmission over long distances and low power consumption. Bluetooth, infrared, Zigbee, and other simple transport protocols are available.
  • the motion display module calculates the motion speed and the trajectory of the complete motion and the initial motion obtained from the stationary-motion detection module and the gravity acceleration separation module respectively, and calculates the motion velocity and the trajectory in the Cartesian coordinate system, and sends the motion to the display driver.
  • the movement of the human body is displayed on the computer screen.
  • FIG. 4 shows the working flow chart of the static-motion detection module.
  • the driver module encapsulates the continuous frame data into a group and sends it to the stationary-motion detection module.
  • the stationary-motion detecting module detects that the state is the pre-stationary state
  • the determination condition i is entered. If the variance of the set of data is greater than the threshold ⁇ , the system detects that the state ⁇ enters the pre-motion state. At this time, the previous group of frame data is stored in the buffer together with the data of the pre-motion state of the group, and the data of the pre-motion state is bypassed by the gravity acceleration separation module, and directly sent to the action display module, and the action display module is notified to start the motion.
  • the initial state is drawn, that is, when the human body starts to move, the system can display the initial direction and speed of the motion without waiting for the motion to complete.
  • the three-axis accelerometers used are different, and the values of ⁇ and ⁇ are also different.
  • Bosch's BMA150 sensor where ⁇ and ⁇ are taken separately 0.25 (ra/s 2 ) 2 and 2m/s 2 )
  • the static-motion detection module makes a final judgment on the length of the data in the buffer, that is, whether the detected data length is greater than the frequency F in the T time (ie Action
  • the continuation time is greater than T, for example, 0.5 can take 0.5 seconds) the size of the sampled data, and it is determined whether there is a set of data in the data.
  • the modulus of the difference between the mean value and the standard gravity acceleration is greater than the acceleration amplitude ⁇ of the effective motion.
  • ⁇ is an empirical value derived from experiments on human motion. Unlike the three-axis accelerometer, the value of ⁇ is different. For example, Bosch's BMA150 sensor, where ⁇ takes lm/s 2 )
  • the entire stationary-motion The detection module is designed based on the analysis of the characteristics of the motion data in the experiment. It can detect the human motion with a duration longer than T, and can transmit the high frequency vibration of the outside world, the noise of the human blood vessels and the heartbeat, and some unconscious jitter of the human body. Filtered out. At the same time, when entering the pre-motion state, the action display module is notified to prepare to draw the initial motion direction, and a high real-time performance is achieved.
  • Gravity acceleration separation module is an empirical value derived from experiments on human motion. Unlike the three-axis accelerometer, the value of ⁇ is different. For example, Bosch's BMA150 sensor, where ⁇ takes lm/s 2 )
  • the entire stationary-motion The detection module is designed based on the analysis of
  • the system uses the static state data of each group of complete motion movements starting and ending (the scalar value is similar to the gravitational acceleration g, at which time only the earth's gravity acts on the acceleration sensor), and the system space attitude of the beginning and end of the motion is calculated. Marked as Posture-Start and Posture_End respectively, the system assumes that the transformation of the two poses in the whole motion is the minimum angle at which the user rotates along a corresponding rotation axis, that is, the human body is doing the least work. The same motion effect is achieved by the method.
  • the accelerometer is converted from the initial attitude to the final final attitude state. .
  • This can be considered as a whole process, and the transformation is performed around the " j axis. So the whole rotation process can be differentiated into a series of small angles around the ⁇ '
  • the algorithm is as follows:
  • the attitude of the accelerometer is represented by the coordinate system fixed on the accelerometer, assuming that the initial pose is 7 ⁇ and the end pose is . See Figure 6 for a schematic diagram.
  • the acceleration vector measured at the initial moment is T N2009/000770
  • the acceleration vector measured at the end time is lxN ' a yN a. N
  • the transformation matrix of the termination pose ⁇ to the initial pose is the shell IJ:
  • the motion acceleration is two ⁇ one.
  • a ' is the information under the attitude ⁇ , and needs to be converted to the initial attitude r fl .
  • the acceleration information in the initial attitude is -
  • the complete motion data from the stationary-motion detection module is used twice in the gravity acceleration separation module.
  • Calculate the minimum rotation angle of the motion and the corresponding rotation axis direction obtain the attitude transformation matrix of each data sampling point and calculate the component of the gravity acceleration in the X/Y/Z axis by ⁇ , from the complete motion data from the static-motion detection module.
  • a sample point data [a x , a y , a z ] is separated to obtain the acceleration data of the actual complete human motion process.
  • the acceleration data in the actual complete human motion process is used to calculate the motion speed after one integral, and then After one integral, the motion trajectory is calculated.
  • the present invention can be used to collect operational end pose information, The calculated posture information is mapped into the virtual scene, and dynamic real-time collision detection is performed with the virtual scene to realize real-time tactile interaction.
  • the invention can realize the mouse control function in the two-dimensional plane and the three-dimensional stereoscopic horizon, which can completely replace the operation function of the existing traditional mouse, and is more suitable for the application requirements of the future three-dimensional desktop environment.
  • the real-time pre-action display module performs simple de-gravity acceleration processing on the previous set of still data and the current group of motion data after the static motion detecting module enters the pre-motion state (ie, assumes that the initial state of motion, the gravitational acceleration is on the three-axis X
  • the distribution of /Y/Z is constant, that is, the acceleration data value of X/Y/Z in the front stationary state is used as the component of the gravitational acceleration g on the three axes, and the received acceleration data is subtracted from the three axes of the gravitational acceleration g. Component), calculate the speed, the trajectory to get the direction of the starting motion, and send the data to the display driver to start the drawing.
  • the system architecture and algorithm solution designed by the present invention face the developer.
  • the controller, etc. thus the present invention has a good commercial application prospect.
  • the minimum angle of rotation of a complete motion from the beginning to the end is calculated by the motion angle mini-optimization algorithm, and the vector direction of the solved axis is utilized. And the size of the corner, the rotation component is separated into each sampling point, the attitude of each point and the gravity acceleration are mapped accordingly, the influence of gravity acceleration on the signal in different postures is removed, the accurate motion acceleration information is obtained, and the motion is calculated. Speed and trajectory.
  • the static data before the start of the motion is used as the initial and final two sets of static attitudes, and the acceleration data of the shorter fixed time is removed from the gravity using the data of the attitude. Acceleration, the motion acceleration value, motion velocity and trajectory during this time are obtained, and matched with a limited number of motion commands in the pattern matching database to obtain a determined motion direction. After calculating the motion directions of the two groups, the system divides the change of direction and corrects the velocity and trajectory of each point in the complete motion data to obtain motion information. The acceleration signal is matched and filtered, and the continuous data is used according to the corresponding condition of the judgment condition I/II in FIG.
  • Game action recognition device 4
  • the condition I triggers the detection of the motion start state
  • the condition II triggers the motion end state detection, thereby avoiding the human hand.
  • the present invention can be used for the recognition of two-dimensional and three-dimensional motions in computer games or home game console games.
  • the game motion recognition device having the acceleration motion recognition technology of the non-gyro technology reconstructs the action made by the user on the computer by calculation and maps with the control command in the game.
  • the user can wear the device above the back of the hand, and when the user pans left and right, the playing cards held in the hand of the game can be selected, when the user wishes When playing or selecting a designated playing card, the user only needs to quickly move the device-worn hand forward or backward to allow the computer to recognize the corresponding operation.
  • the game motion recognition device separates the valid data in the swing process by the stationary-motion detection module, and obtains the complete trajectory, speed and other related information of the user waving the racket through the system calculation.
  • the motion pattern matching module calculates the direction, strength, and rotation of the shot based on the motion speed and the trajectory information.
  • mice are mechanically or optically resolved on a two-dimensional plane to give the user a translation in a horizontal plane.
  • Mouse setting with acceleration motion recognition technology with non-gyro technology It has the function of precise positioning in three-dimensional space.
  • the mouse cursor moves with it.
  • the device and system can convert the three-dimensional motion into a two-dimensional plane projection.
  • the left and right mouse click actions the user can quickly move the palm or move up and down in a direction perpendicular to the two-dimensional plane. You can achieve the corresponding click function.
  • the mouse with the acceleration motion recognition technology of non-gyro technology is not limited by the contact surface like a conventional mechanical or optical mouse.
  • a mouse having the technology of the present invention can control the mouse cursor of a computer desktop on any surface, even in the air.
  • Robot motion control input device :

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Abstract

L'invention porte sur un procédé d'identification de mouvement d'accélération et sur un système associé, ledit procédé d'identification de mouvement consistant : à recueillir des signaux de données réels d'un mouvement à l'aide d'un capteur tridimensionnel d'accélération, à confirmer des données de repos au démarrage et à la fin du mouvement, à séparer l'accélération par gravité des données recueillies par le capteur tridimensionnel d'accélération à l'aide de l'arithmétique de séparation de l'accélération gravitationnelle de façon à obtenir des données d'accélération du mouvement sur la base des signaux de données réels recueillis par les données tridimensionnelles et de repos au démarrage et à la fin du mouvement, et à calculer la vitesse et le trajet du mouvement en fonction des données d'accélération du mouvement.
PCT/CN2009/000770 2009-07-07 2009-07-07 Procédé et système d'identification de mouvement d'accélération WO2011003218A1 (fr)

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PCT/CN2009/000770 WO2011003218A1 (fr) 2009-07-07 2009-07-07 Procédé et système d'identification de mouvement d'accélération
CN200980161332.5A CN102667672B (zh) 2009-07-07 2009-07-07 一种加速度动作识别系统及其方法

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US8989441B2 (en) 2013-02-07 2015-03-24 Zepp Labs, Inc. Data acquisition method and device for motion recognition, motion recognition system and computer readable storage medium
CN104898831A (zh) * 2015-05-08 2015-09-09 中国科学院自动化研究所北仑科学艺术实验中心 人体动作采集和动作识别系统及其控制方法
CN105664454A (zh) * 2016-04-11 2016-06-15 深圳市酷浪云计算有限公司 实现运动设备速度测量的方法和装置
CN106491138A (zh) * 2016-10-26 2017-03-15 歌尔科技有限公司 一种运动状态检测方法及装置
CN107036568A (zh) * 2017-06-01 2017-08-11 中国计量大学 空间大尺寸轨迹检测装置及方法

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US10441197B2 (en) * 2014-05-30 2019-10-15 Nitto Denko Corporation Device and method for classifying the activity and/or counting steps of a user
EP4027215A1 (fr) * 2014-09-04 2022-07-13 Leomo, Inc. Dispositif terminal d'informations, système de capture de mouvement et procédé de capture de mouvement
CN105630195B (zh) * 2014-10-28 2019-12-27 欧姆龙健康医疗事业株式会社 运动识别装置、便携式运动检测设备及其运动识别方法
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CN105866473B (zh) * 2016-02-24 2019-06-04 安徽华米信息科技有限公司 马达振动加速度的测量方法及装置
CN106371587A (zh) * 2016-08-28 2017-02-01 深圳市爱华兴模具有限公司 一种简单有效的手势识别方法
CN107976559A (zh) * 2017-10-11 2018-05-01 常州信息职业技术学院 基于静止点检测的滤除重力加速度方法
CN107865662A (zh) * 2017-10-19 2018-04-03 张滇 一种识别肢体动作的方法及装置
CN109758154B (zh) * 2019-01-09 2022-02-25 北京卡路里信息技术有限公司 一种运动状态确定方法、装置、设备及存储介质
CN110721473B (zh) * 2019-10-10 2022-10-04 深圳市瑞立视多媒体科技有限公司 物体抛出方法、装置、设备及计算机可读存储介质
CN111449681B (zh) * 2020-04-08 2023-09-08 深圳开立生物医疗科技股份有限公司 一种剪切波成像方法、装置、设备及可读存储介质
CN112075940A (zh) * 2020-09-21 2020-12-15 哈尔滨工业大学 一种基于双向长短时记忆神经网络的震颤检测系统
CN112767782B (zh) * 2021-01-19 2022-08-19 武汉理工大学 一种用于实时检测教师情绪的智能教鞭系统
CN114041723A (zh) * 2021-11-18 2022-02-15 苏州精源创智能科技有限公司 一种吸尘器运动传感器模块及其检测吸尘器运动状态的方法
CN114176576B (zh) * 2021-12-11 2024-05-24 江苏智恒文化科技有限公司 基于加速度识别人体运动状态的方法

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103685401A (zh) * 2012-09-17 2014-03-26 联想(北京)有限公司 信息交换方法、终端设备和信息交换系统
US8989441B2 (en) 2013-02-07 2015-03-24 Zepp Labs, Inc. Data acquisition method and device for motion recognition, motion recognition system and computer readable storage medium
CN104898831A (zh) * 2015-05-08 2015-09-09 中国科学院自动化研究所北仑科学艺术实验中心 人体动作采集和动作识别系统及其控制方法
CN105664454A (zh) * 2016-04-11 2016-06-15 深圳市酷浪云计算有限公司 实现运动设备速度测量的方法和装置
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CN106491138A (zh) * 2016-10-26 2017-03-15 歌尔科技有限公司 一种运动状态检测方法及装置
CN107036568A (zh) * 2017-06-01 2017-08-11 中国计量大学 空间大尺寸轨迹检测装置及方法

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