CN107961013A - Portable upper extremity exercise coordination detection system - Google Patents

Abstract

The present invention discloses a kind of upper limb harmony movement detection systems, including a left side can be worn on respectively, the computer of sensor group and a carrying upper extremity exercise harmony training module on right finesse, the upper extremity exercise harmony training module includes portable guiding action control interface and background processing module, the background processing module includes the data analysis module of upper extremity exercise harmony, the system is used for the index of correlation for studying the movement of human upper limb harmony, contribute to the sports coordination of detection upper limb, the problems such as available for detection childhood inborn sexual organ dysplasia and preventing the harmony relevant disease of the elderly.

Description

Portable upper limb movement coordination detection system

technical field

The invention specifically relates to a portable upper limb movement coordination detection system, which is used for the study of human body coordination movement in the direction of skill learning, behavior science, and movement control. The technical field being evaluated.

Background technique

Coordinated movement of the upper limbs of the human body means that under the control of the central nervous system, muscle groups related to specific movements or actions work together with a certain time-space relationship to produce smooth, accurate and controlled upper limb movements. It is characterized by upper body movements performed with appropriate speed, distance, direction, rhythm and strength. The study of upper limb coordinated movement is not only a basic theoretical issue in many disciplines such as skill learning, behavioral science, and motor control, but also has great application space in the fields of human motor skill development, robot motion science, and bionics.

The improvement of detection tools for upper limb coordinated movement will promote the development of coordinated movement theory, and the development of theory will greatly promote the progress of detection tools. In the closed-loop control theory of human body movement, it is pointed out that during the movement of human limbs, the movement effector realizes the movement after receiving the movement plan instruction issued by the central control system and transmits the feedback information of the movement to the central control system. An adjustment command is issued for the current trajectory, which optimizes the movement of the limbs and forms a closed-loop control. If the feedback information from the effector is insufficient or the central system does not process the feedback information properly, it will cause the trajectory deviation of the limb movement and form a coordination disorder.

The study and analysis of human motion began with the study of the gait of the lower limbs. As early as 1836, the researcher Weber conducted research on the law of limb swing of human walking and running. In the 1860s, the emergence of video cameras enabled the scientific and quantitative study of human body movements. At first, the kinematics research of the human body was analyzed by observing the phenomena and summarizing the research laws.

Due to the relative complexity of the movement structure and control mechanism of the upper limbs, human research on the movement of the upper limbs lags behind that of the lower limbs. In 1965, scientists in the former Soviet Union were the first to record daily life movements of the upper limbs. They designed a 7-DOF orthosis using a voltmeter to measure the joint angles of the upper limb during motion. Through the measurement of 7 kinds of daily life actions, the range of motion of each joint angle of the upper limbs, the maximum angular velocity and the maximum angular acceleration were determined. Later researchers Safaee_Rad, Cooper, and Romolly measured the kinematic parameters of the entire arm, such as angular range, angular acceleration, and maximum angular velocity.

The in-depth research on the mechanism of upper limb coordinated movement control also promotes the improvement of detection methods and methods. There are two scientists who have made outstanding contributions, one of which is the famous physiologist Bernstein of the former Soviet Union, and the other outstanding figure is Saltzman of the United States. Their research theory is a milestone in the study of upper limb coordinated movement. Bernstein proposed the theory of "Motion Redundancy and Coordinated Control", also known as the theory of motion structure. Later, he proposed the control theory of Movement Synergy. He pointed out that the collective movement patterns of joints or muscles constitute the movement synergy of the limbs. Human movement is composed of these motor coordination elements. Different coordinating elements are combined to form different movement synergy, and the movement organs combine these coordinating elements and movement synergy according to the motor function. The control center (nervous system) of human motion uses motion structures and coordination elements to reduce control parameters, thus transforming the redundancy problem of motion into simple instructions that can be accepted by the underlying actuators—bones and muscles. In the process, the movement system makes use of the coordination laws and physiological constraints of the human body's physiological mechanism. Bernstein's theory indicates that the "coordination element" research is the research direction and scope of the upper limbs' coordinated movement control mechanism. aspect is of epoch-making significance.

At present, there are three important development directions in the training of upper limb coordination movement: one is in sports, through a series of movement training to improve the upper limb coordination movement of athletes, but the effect of coordination movement training mainly depends on the judgment of the coach , and there is no special coordination motion training detection equipment to give intuitive detection results. The second is in terms of rehabilitation treatment, patients with cerebral palsy or physical disabilities are treated with relevant auxiliary coordination movement training equipment. H is in terms of children's healthy growth, early coordination exercise training for children with developmental disorders of coordination movement will help improve children's coordination movement status. The effects of these coordinated movement trainings require relevant monitoring tools to evaluate them in order to improve training programming and improve assistive medical equipment.

When the human upper limbs are doing action tasks, the active muscle groups make the movement timing of the upper limbs correct, the movement direction accurate and the movement speed appropriate, and make the execution of the movements balanced and stable with a certain rhythm. Coordinated movement of the upper limbs and the interactive inhibition ability of the upper limbs (it reflects the ability of the subject to prevent or inhibit the nerve impulse of the upper limb muscles when doing action tasks), the upper body strength (it reflects the control of muscle relaxation and contraction) , the endurance of the upper limbs (it reflects the degree of influence of the subjects when they are doing fine movements when they are tired), the state of mind (it reflects the degree of concentration of the subjects when they do motor tasks), the function of proprioception (it reflects the subjects' When the upper limbs are in a certain position, the muscles and joints feel the tension) and so on. By training the subject's muscle strength, endurance, movement proficiency, body and center of gravity balance, movement rhythm, etc., the patient's upper limb coordination movement status can be improved.

Based on the above analysis, the present invention is used to study the relevant indicators of the coordinated movement of the upper limbs of the human body, so as to detect the coordinated movement disorder caused by congenital dysplasia, etc., and the system can be used to detect and prevent the coordinated movement related diseases of the elderly and children , and can provide auxiliary help for coordinated movement training.

Contents of the invention

Purpose of the invention: The purpose of the present invention is to provide a portable upper limb movement coordination detection system, which can detect the coordination movement disorder caused by congenital dysplasia by studying the relevant indicators of the human upper limb coordination movement. and prevention of coordination-related diseases in the elderly and children, and hope that the system can provide auxiliary assistance for coordination training.

Technical solution: A portable upper limb movement coordination detection system, including sensor groups that can be worn on the left and right wrists respectively and a computer equipped with an upper limb movement coordination training module, the upper limb movement coordination training module includes a portable guide Action control interface and background processing module, the background processing module includes a data analysis module of upper limb movement coordination;

The portable guiding action control interface is used to display guiding actions and real-time motion waveforms of left and right upper limbs;

The sensor group is composed of an acceleration sensor, a gyroscope sensor and a geomagnetic sensor. The acceleration sensor, the gyroscope sensor and the geomagnetic sensor respectively collect the components of the angular velocity, acceleration and magnetic intensity data corresponding to the current posture of the upper limbs on the three sensitive axes. The Bluetooth module sends the monitoring data to the data analysis module of upper limb movement coordination

The upper limb movement coordination data analysis module adopts the upper limb movement data fusion algorithm to process and receive the sensor group data to generate real-time movement waveform, and then according to the coordination movement analysis algorithm, obtains the accuracy and coordination of the upper limb movement coordination Happening.

Wherein, the guiding action is composed of 48 pictures of different movement positions, and the 48 pictures of different movement positions trigger the switching of pictures through a timer.

The specific process of the data fusion algorithm for upper limb movement is to use a weighting algorithm to first fuse the axial data collected by the three sensor groups to obtain the fusion vector Rsτ(n), and then use normalization processing to process the fusion vector to obtain normalization The fusion vector Rτ(n) is optimized to generate a real-time motion waveform;

The calculation formula of the weighting algorithm is as follows:

Rsτ(n)＝[R _stx ,R _{sty ,R stz ]} ^T

Among them, [R _accx ,R _accy ,R _accz ] ^T is the three-axis vector output by the acceleration sensor, [R _gyrox ,R _gyroy ,R _gyroz ,] ^T is the three-axis vector output by the gyro sensor, [R _hmcx ,R _hmcy , R _hmcz ] ^T is the three-axis vector output by the geomagnetic sensor; 1 is the weighting coefficient of the acceleration sensor, W _ag is the weighting coefficient of the gyroscope sensor, W _ah is the weighting coefficient of the geomagnetic sensor, and the values of W _ag and W _ah are based on experience Determined between 5-20;

The calculation formula of described normalization method is as follows:

The accuracy of the coordinated movement of the upper limbs is evaluated by the error mean value K of the left and right hands of the tested person in each movement cycle:

in, and is the moment when the left and right hands cross the balance zero axis for the ith time respectively, is the moment when the demonstration action crosses the i-th balance zero axis, and n is the total number of times the demonstration action crosses the balance zero axis.

The coordination situation of the coordinated movement of the upper limbs is evaluated by the movement speed difference V and the phase difference λ in the X-axis of the left and right hands of the tested person in each movement cycle;

in, and are the motion displacements of the left and right hands in the i-th cycle of motion, respectively, t _Li and t _Ri are the actual motion cycle time of the left and right hands in the i-th motion cycle, and n is the number of motion cycles of the task action.

in, and is the phase when the left and right hands cross the balance zero axis for the ith time respectively, and n is the total number of demonstration actions crossing the balance zero axis.

Beneficial effect:

1. The traditional detection scheme relies on professional detection personnel, the detection results are not accurate enough, the operation is complicated, the detection time is long, etc., so the detection is difficult. However, the upper limb coordinated movement detection system provided by the present invention uses a wearable sensor group to The subjects were tested, and the real-time movement waveforms of the left and right upper limbs and the evaluation indicators of accuracy and coordination were given. The evaluation indicators were intuitive and clear, and relatively accurate evaluation results were given in the form of data.

2. The invention is easy to operate and convenient to detect. It can save upper limb movement data and detection index results. The saved historical data is beneficial to compare the detection results with others in the horizontal direction, understand the difference in the coordinated movement indexes between yourself and others, and compare the results in the vertical direction. After a period of exercise training, the improvement of the coordination movement index.

Description of drawings

Fig. 1 is the structural representation of this detection system;

Figure 2 is an overall hierarchical structure diagram of the system;

Fig. 3 is the design structural drawing of motion sensing unit module of the present invention;

Fig. 4 is a schematic diagram of the guidance action control interface of the present invention;

Figure 5 is a diagram of the realization mechanism of the data synchronization design of the left and right upper limbs;

6 is a schematic diagram of information interaction between the motion sensing unit and the PC terminal;

Detailed ways

The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

As shown in Figure 1, the portable upper limb movement coordination detection system includes two parts, including a sensor group that can be worn on the left and right wrists respectively and a computer equipped with an upper limb movement coordination training module. The training module includes a portable guided action control interface and a background processing module, and the background processing module includes a data analysis module for upper limb movement coordination;

The portable guiding action control interface is used to display guiding actions and real-time motion waveforms of left and right upper limbs;

The sensor group is composed of an acceleration sensor, a gyroscope sensor and a geomagnetic sensor. The acceleration sensor, the gyroscope sensor and the geomagnetic sensor respectively collect the components of the angular velocity, acceleration and magnetic intensity data corresponding to the current posture of the upper limbs on the three sensitive axes. The Bluetooth module sends the monitoring data to the data analysis module of upper limb movement coordination;

The upper limb movement coordination data analysis module uses the upper limb movement data fusion algorithm to process and receive the sensor group data to obtain the real-time movement waveform, and then according to the coordination movement analysis algorithm, obtains the accuracy and coordination of the upper limb movement coordination Happening.

In terms of hierarchical structure, as shown in Figure 2, the upper limb coordinated movement detection system is mainly composed of a motion perception unit design layer, a communication protocol customization layer, and a PC terminal system application layer. The overall design of the detection system is based on the upper limb coordinated movement closed-loop Designed based on control theory, the hands of the detection experiment follow the guiding action to perform rotational movement as the action task, and the hand movement data of the subject is recorded in real time through the wearable sensor group, and the customized communication protocol is used to transmit the data to the PC through Bluetooth Data preprocessing is carried out on the terminal, and finally, the parameters of the coordination movement finger are analyzed by the exercise data of the subjects, and the coordination movement detection of the user is realized.

The first layer is the motion perception unit design layer, which is the perception unit of the detection system. It can collect the data of the upper limb movement trajectory of the subject. The movement perception unit is composed of three complementary sensors to realize the accurate recording of the upper limb movement trajectory, detection The data collected by the system consists of two motion sensing units (one on each of the left and right forearms) and a data aggregation node. The motion sensing unit is used to collect the data of the moving parts of the forearm and perform basic processing on these data. The data aggregation node is located on the PC. , the turned-on dual-channel Bluetooth adapter receives the motion data from the left and right forearms and sends them to the PC for summary processing. During the process of completing the action task, the three sensors inside the motion perception unit module perceive the movement of the upper limbs in real time. Track data, then time align the data collected by the three sensors, then digitally filter the basic processed data, and finally pack the data and transmit it to the PC through the Bluetooth channel.

The second layer is the data communication protocol customization layer. The motion sensing unit needs to transmit the collected motion data to the PC after basic processing;

The third layer is the system application layer of the PC terminal, which mainly completes the design of the interactive platform. After the motion data collected by the motion sensing units worn on the left and right forearms is transmitted to the buffer area of the PC, the data also needs to be preprocessed. The interactive platform mainly completes the verification and analysis of collected data, data fusion processing, motion display, guidance action display, data storage and various button control design.

As shown in Figure 3, the gyroscope sensor (ITG-3200), acceleration sensor (LIS3DH) and geomagnetic sensor (HMC5883) form a sensor group, and the collected motion data is preprocessed by the processor STM32F103 (Cortex-M3). Bluetooth sends data to the PC terminal, and the bottom layer uses STM32F103 to write the driver code of each device based on the ST library, FIR digital filtering [8] and data transmission code, and writes the control interface on the PC terminal and handles the transmission of the wearable motion sensing unit. The motion data of the upper limbs can be displayed in real time on the PC.

As shown in Figure 4, the portable guided action control interface includes 2 parts. The right side of the interface is to display the guided action, and the subject follows the guided action to exercise. The described guided action is composed of 48 pictures of different exercise positions. The 48 pictures of different movement sites are switched by a timer, which facilitates the setting of the number of upper limb movement tasks and the adjustment of the experimental plan; the upper and lower frame areas on the left side of the interface can display the movement waveforms of the left and right upper limbs in real time. Below the interface are some control buttons, click the "Exit Test" button, and the interface will be closed automatically. Click the "Pause Transmission" button, and the motion sensing unit will stop sending data at this time, and the control interface will be in a paused state. Click the "Synchronous Transmission" button, the movement paradigm on the right and the movement perception units of the left and right upper limbs will work together, and the experiment time will also start timing work. Click the "Data Analysis" button, and the analysis results of the upper limb movement data will be given.

The implementation mechanism of the left and right upper limb data synchronization design is shown in Figure 5. The function of the motion data synchronization design is to simultaneously realize the collection of left and right forearm motion information, the start of the execution of the motion task, and the synchronous storage of the upper limb motion data when the demonstration action is started. Among them, TIM2 is a timer in the core chip STM32 of the motion sensing unit. When the timer is turned on, the motion sensing unit starts to collect motion data. Timer2.Start() is the clock for the execution of demonstration actions on the PC side. After turning on this clock, the demonstration action area will start the action guidance, and at the same time start the data storage of the upper limb movement. The "Synchronize Start" button on the interface mainly performs the above tasks to realize the synchronization operation. The interface design is mainly based on the Winform platform. The motion data collected by the motion sensing unit is transmitted to the PC through the Bluetooth device, and the application program obtains the data by reading the serial port. The specific functions of all controls on the interface are written in C#, and the interface design is as simple and easy to operate as possible.

The information interaction between the sensor group and the PC terminal is shown in Figure 6. After the control command is issued through the interactive interface, such as "S", the sample is sent and the motion data collection and transmission of the motion sensing unit is started. Of course, this requires a series of hardware support and programming, specifically: in terms of hardware support, the MCU of the motion sensing unit sends data to the Bluetooth module through the serial port, and then Bluetooth also sends data to the PC, and the Bluetooth adapter on the PC Receive data to the corresponding serial port. Under the Winform interface of the PC terminal, there is a button dedicated to sending instructions to the motion sensing unit, and there is also a special area to introduce the data and prompt information of the motion sensing unit; with software support, the motion sensing unit starts to collect the upper limbs. Motion data needs to be supported by the TIM2 timer of STM32. We receive instructions from the PC through serial port interrupts. The so-called serial port interrupt means that an interrupt will be generated regardless of whether the serial port is sending data, receiving data or data overflow. For example, a PC sends a motion sensing unit data acquisition start command "S". After receiving this command, the corresponding serial port on the MCU of the motion sensing unit generates a serial port interrupt and executes the corresponding function. The TIM2 timer is enabled, so that the motion The sensing unit starts data collection. There is also an NVIC in the core Cortex-M3 core of STM32, which can control whether the interrupt signal here triggers the execution of the interrupt processing function.

Since the data obtained by the three sensors of the perception module unit come from one target, as long as the observation time of each sensor is the same, it can be considered that the time is aligned. Using the method of interpolation, the observation data of the high data rate sensor in the motion perception unit Extrapolate to the observation time series of low data rate sensors, so as to realize the time alignment of different sensors in the motion perception unit.

Two algorithms are used in the data analysis module of upper limb movement coordination, which are the data fusion algorithm of upper limb movement and the analysis algorithm of coordinated movement. The subjects wear a set of sensors on the left and right wrists, in order to accurately detect the left and right upper limbs of the subjects. For real-time motion information, we need to fuse the axial data generated by the acceleration sensor, gyroscope and geomagnetic sensor. The acceleration sensor detects the acceleration signal, which is sensitive to mechanical vibration and noise; the gyroscope sensor detects rotation. It has little influence on the interference of mechanical vibration, but it is easy to drift; the geomagnetic sensor detects the change of magnetic field, which is different from the interference source of the former two; considering these factors, we need to carry out the axial data collected by these three sensors Data fusion, in order to obtain more accurate data, we use a weighting algorithm for fusion, and multiply the relatively small weight coefficient on the fast-changing signal, which can weaken the impact of the sudden change signal on the whole.

The calculation formula of the weighting algorithm is as follows:

Rsτ(n)＝[R _stx ,R _{sty ,R stz ]} ^T

Among them, [R _accx ,R _accy ,R _accz ] ^T is the three-axis vector output by the acceleration sensor, [R _gyrox ,R _gyroy ,R _gyroz ,] ^T is the three-axis vector output by the gyro sensor, [R _hmcx ,R _hmcy , R _hmcz ] ^T is the three-axis vector output by the geomagnetic sensor; 1 is the weighting coefficient of the acceleration sensor, W _ag is the weighting coefficient of the gyroscope sensor, W _ah is the weighting coefficient of the geomagnetic sensor, and the values of W _ag and W _ah are based on experience Determined between 5-20;

In order to further improve the robustness of the calculation, the fusion vector Rsτ(n) is further processed by normalization processing, and the normalized fusion vector Rτ(n) reflecting the movement of the left and right upper limbs is obtained. The normalization calculation formula is as follows:

Since the sensor groups carried by the subjects' left and right wrists are the same, the data processing methods are also consistent. After the above data fusion processing, we can get relatively accurate motion data fusion vectors RτL(n) and RτR(n) of the left and right upper limbs. Axial motion data component values, the component value output by each axis is the vector value at a certain moment when the object is moving, the curve output by a certain axis in a continuous period of time is the motion vector trace of the object, The motion vector trace is the real-time motion waveform of the left and right upper limbs.

Using the real-time motion waveform and the analysis algorithm of coordinated movement can be used to analyze and evaluate the upper limb movement coordination of the subjects.

In the analysis algorithm of coordinated movement, in the analysis of the upper limb coordinated movement data of the subjects, the main inspection indicators are as follows: Index 1, to examine whether the subjects can accurately follow the action tasks and whether the rhythm when following the demonstration actions is correct. Consistent, evaluated by the average error K of the left and right upper limbs of the subject in each exercise cycle, the calculation method of the accuracy analysis value K when the subject completes the action task is as follows:

in, and is the moment when the left and right upper limbs cross the balance zero axis for the ith time respectively, is the moment when the demonstration action crosses the i-th balance zero axis, and n is the total number of times the demonstration action crosses the balance zero axis.

Index 2. Analyze the coordinated movement index of both hands when the subject is moving with both hands; that is, to evaluate the movement speed difference V and the phase difference λ in the X-axis of the left and right upper limbs of the tested person in each movement cycle;

in, and are the motion displacements of the left and right hands in the i-th cycle of motion, respectively, t _Li and t _Ri are the actual motion cycle time of the left and right hands in the i-th motion cycle, and n is the number of motion cycles of the task action.

in, and is the phase when the left and right hands cross the balance zero axis for the ith time respectively, and n is the total number of demonstration actions crossing the balance zero axis.

When evaluating the accuracy and coordination of upper limb movement, the smaller the left and right hand movement error value, the better the accuracy index of a subject's upper limb coordinated movement; the smaller the movement phase difference and speed difference between the left and right hands, the smaller the testee's upper limb The better the movement coordination.

Based on the above algorithm analysis, the following three detection modes are designed to detect the coordination of the upper limbs of the subjects. Mode 1: The subject's left and right hands follow the guiding action to perform arm flipping movements, 63 times per minute, and the test time is 1 minute; Mode 2: The subjects' left and right hands follow the guiding action to perform arm flipping movements together, doing 125 times per minute, and the test time is 1 minute. 1 minute; Mode 3: Extend the test time of items 1) and 2) to 2 to 3 minutes.

When using this system to detect whether there is a coordination movement disorder, we first need to establish the child's coordination movement accuracy norm and coordination norm, and then use the system to obtain the real-time motion waveform of the child being tested when doing the action task, In this way, the accuracy movement index and the synergy movement index are obtained, and compared with the normal model, the children's movement defects can be found.

Claims (5)

1. a portable upper limb motion coordination detection system, is characterized in that, comprises the sensor group that can wear respectively on left and right wrists and a computer that carries upper limb motion coordination training module, described upper limb motion coordination training module It includes a portable guided action control interface and a background processing module, and the background processing module includes a data analysis module for upper limb movement coordination; The portable guiding action control interface is used to display guiding actions and real-time motion waveforms of left and right upper limbs; The sensor group is composed of an acceleration sensor, a gyroscope sensor and a geomagnetic sensor. The acceleration sensor, the gyroscope sensor and the geomagnetic sensor respectively collect the components of the angular velocity, acceleration and magnetic intensity data corresponding to the current posture of the upper limbs on the three sensitive axes. The Bluetooth module sends the monitoring data to the data analysis module of upper limb movement coordination; The upper limb movement coordination data analysis module uses the upper limb movement data fusion algorithm to process the received sensor group data to generate real-time movement waveforms, and then obtains the accuracy and coordination of the upper limb coordination movement according to the coordination movement analysis algorithm . 2. A kind of portable upper limb movement coordination detection system according to claim 1, is characterized in that, described guide action is made up of the picture of 48 different movement positions, and the pictures of these 48 different movement positions are passed through timing device to trigger the switching of pictures. 3. A kind of portable upper limb movement coordination detection system according to claim 1, is characterized in that, the specific process of the data fusion algorithm of described upper limb movement is to first utilize weighting algorithm to fuse the axial data collected by three sensors Obtain the fusion vector Rsτ(n), and then use normalization to process the fusion vector to obtain the normalized fusion vector Rτ(n), thereby generating the corresponding real-time motion waveform; The calculation formula of the weighting algorithm is as follows: <mrow><msub><mi>R</mi><mrow><mi>s</mi><mi>t</mi><mi>x</mi></mrow></msub><mo>=</mo><mfrac><mrow><msub><mi>R</mi><mrow><mi>a</mi><mi>c</mi><mi>c</mi><mi>x</mi></mrow></msub><mo>+</mo><msub><mi>R</mi><mrow><mi>g</mi><mi>y</mi><mi>r</mi><mi>o</mi><mi>x</mi></mrow></msub><mo>*</mo><msub><mi>R</mi><mrow><mi>a</mi><mi>g</mi></mrow></msub><mo>+</mo><msub><mi>R</mi><mrow><mi>h</mi><mi>m</mi><mi>c</mi><mi>x</mi></mrow></msub><mo>*</mo><msub><mi>R</mi><mrow><mi>a</mi><mi>h</mi></mrow></msub></mrow><mrow><mn>1</mn><mo>+</mo><msub><mi>W</mi><mrow><mi>a</mi><mi>g</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>a</mi><mi>h</mi></mrow></msub></mrow></mfrac></mrow> <mrow><msub><mi>R</mi><mrow><mi>s</mi><mi>t</mi><mi>y</mi></mrow></msub><mo>=</mo><mfrac><mrow><msub><mi>R</mi><mrow><mi>a</mi><mi>c</mi><mi>c</mi><mi>y</mi></mrow></msub><mo>+</mo><msub><mi>R</mi><mrow><mi>g</mi><mi>y</mi><mi>r</mi><mi>o</mi><mi>y</mi></mrow></msub><mo>*</mo><msub><mi>R</mi><mrow><mi>a</mi><mi>g</mi></mrow></msub><mo>+</mo><msub><mi>R</mi><mrow><mi>h</mi><mi>m</mi><mi>c</mi><mi>y</mi></mrow></msub><mo>*</mo><msub><mi>R</mi><mrow><mi>a</mi><mi>h</mi></mrow></msub></mrow><mrow><mn>1</mn><mo>+</mo><msub><mi>W</mi><mrow><mi>a</mi><mi>g</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>a</mi><mi>h</mi></mrow></msub></mrow></mfrac></mrow> <mrow><msub><mi>R</mi><mrow><mi>s</mi><mi>t</mi><mi>z</mi></mrow></msub><mo>=</mo><mfrac><mrow><msub><mi>R</mi><mrow><mi>a</mi><mi>c</mi><mi>c</mi><mi>z</mi></mrow></msub><mo>+</mo><msub><mi>R</mi><mrow><mi>g</mi><mi>y</mi><mi>r</mi><mi>o</mi><mi>z</mi></mrow></msub><mo>*</mo><msub><mi>R</mi><mrow><mi>a</mi><mi>g</mi></mrow></msub><mo>+</mo><msub><mi>R</mi><mrow><mi>h</mi><mi>m</mi><mi>c</mi><mi>z</mi></mrow></msub><mo>*</mo><msub><mi>R</mi><mrow><mi>a</mi><mi>h</mi></mrow></msub></mrow><mrow><mn>1</mn><mo>+</mo><msub><mi>W</mi><mrow><mi>a</mi><mi>g</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>a</mi><mi>h</mi></mrow></msub></mrow></mfrac></mrow> Rsτ(n)＝[R _stx ,R _{sty ,R stz ]} ^T Among them, [R _accx ,R _accy ,R _accz ] ^T is the three-axis vector output by the acceleration sensor, [R _gyrox ,R _gyroy ,R _gyroz ,] ^T is the three-axis vector output by the gyro sensor, [R _hmcx ,R _hmcy , _{R hmcz} ] ^T is the three-axis vector output by the _geomagnetic sensor; 1 is the _weighting coefficient of the acceleration sensor _; -20; The calculation formula of described normalization method is as follows: <mrow><mi>R</mi><mi>&amp;tau;</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><mfrac><mrow><mi>R</mi><mi>s</mi><mi>&amp;tau;</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow><msqrt><mrow><mi>R</mi><mi>s</mi><mi>&amp;tau;</mi><msup><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mi>T</mi></msup><mo>*</mo><mi>R</mi><mi>s</mi><mi>&amp;tau;</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></msqrt></mfrac></mrow> 4. a kind of portable upper limb movement coordination detection system according to claim 1, is characterized in that, the accuracy of described upper limb coordination movement is evaluated by the error mean value K of the left and right hands of the measured person in each movement cycle : <mrow><mi>K</mi><mo>=</mo><mfrac><mn>1</mn><mi>n</mi></mfrac><msubsup><mi>&amp;Sigma;</mi><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><mo>|</mo><msubsup><mi>t</mi><mrow><mi>L</mi><mi>i</mi></mrow><mi>&amp;alpha;</mi></msubsup><mo>-</mo><msubsup><mi>t</mi><mi>i</mi><mi>&amp;beta;</mi></msubsup><mo>|</mo><mo>+</mo><mfrac><mn>1</mn><mi>n</mi></mfrac><msubsup><mi>&amp;Sigma;</mi><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><mo>|</mo><msubsup><mi>t</mi><mrow><mi>R</mi><mi>i</mi></mrow><mi>&amp;alpha;</mi></msubsup><mo>-</mo><msubsup><mi>t</mi><mi>i</mi><mi>&amp;beta;</mi></msubsup><mo>|</mo></mrow> in, and is the moment when the left and right hands cross the balance zero axis for the ith time respectively, is the moment when the demonstration action crosses the i-th balance zero axis, and n is the total number of times the demonstration action crosses the balance zero axis. 5. a kind of portable upper limb motion coordination detection system according to claim 1, is characterized in that, the synergy situation of described upper limb coordinated motion is through the movement speed difference V and The phase difference λ in the X-axis is evaluated; <mrow><mi>v</mi><mo>=</mo><mfrac><mn>1</mn><mi>n</mi></mfrac><msubsup><mi>&amp;Sigma;</mi><mn>1</mn><mi>n</mi></msubsup><mo>|</mo><mfrac><msubsup><mi>S</mi><mrow><mi>L</mi><mi>i</mi></mrow><mi>&amp;tau;</mi></msubsup><msub><mi>t</mi><mrow><mi>L</mi><mi>i</mi></mrow></msub></mfrac><mo>-</mo><mfrac><msubsup><mi>S</mi><mrow><mi>R</mi><mi>i</mi></mrow><mi>&amp;tau;</mi></msubsup><msub><mi>t</mi><mrow><mi>R</mi><mi>i</mi></mrow></msub></mfrac><mo>|</mo></mrow> in, and are the motion displacements of the left and right hands in the i-th cycle of motion, respectively, t _Li and t _Ri are the actual motion cycle time of the left and right hands in the i-th motion cycle, and n is the number of motion cycles of the task action. in, and is the phase when the left and right hands cross the balance zero axis for the ith time respectively, and n is the total number of demonstration actions crossing the balance zero axis.