CN108187333A - More inertia node wireless monitoring and evaluation system and methods in rehabilitation training of upper limbs - Google Patents
More inertia node wireless monitoring and evaluation system and methods in rehabilitation training of upper limbs Download PDFInfo
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Abstract
The present invention relates to more inertia node wireless monitoring and evaluation systems in a kind of rehabilitation training of upper limbs, including motion sensor, main control chip, electric power management circuit and host computer, the motion sensor and main control chip form motion capture node, carried out data transmission between the motion sensor and main control chip by IIC agreements, the motion sensor, main control chip are connected by electric power management circuit with lithium battery, and the main control chip is connected by wireless telecommunications with host computer.It is an advantage of the invention that solves the problems, such as lack of standardization and nursing staff's heavy workload of upper limb training method in existing house rehabilitation to a certain extent.
Description
Technical Field
The invention relates to a multi-inertia-node wireless monitoring and evaluating system and method in upper limb rehabilitation training, and belongs to the technical field of rehabilitation training and motion capture.
Background
At present, the techniques for capturing human body motion are generally classified into five major categories, i.e., mechanical, acoustic, electromagnetic, inertial sensor, and optical, among which the optical and inertial sensor are the two most widely used. The optical type adopts photoelectric sensor to gather the motion trail of the person to be measured, then shoots the reflection point of photoelectric sensor through the camera of a plurality of different angles and realizes the action capture, and this kind of mode has high requirement to the environment to the following shortcoming exists: the method is easily influenced by backlight when in shooting, has high cost and has high requirement on hardware for image calculation. Most of traditional inertial sensors are in a wired mode, too many signal lines can influence the movement of arms, and troubles are caused to the rehabilitation training process.
Different training modes need to be selected in different time periods for rehabilitation training, and professional equipment needs to be used for training under the professional guidance of a physiotherapist in the initial stage of cerebral apoplexy rehabilitation treatment. In the later stage of rehabilitation training, the patient has had certain autonomous motion ability, and the rehabilitation of house is just a more convenient, more economic rehabilitation training mode, for making the rehabilitation training of house more normal, just urgent need the wireless monitoring of many inertia nodes and evaluation system in the upper limbs rehabilitation training.
Disclosure of Invention
The invention aims to: aiming at the defects in the prior art, a multi-inertial-node wireless monitoring and evaluating system in upper limb rehabilitation training is provided, and a monitoring and evaluating method is provided.
In order to achieve the purpose, the invention provides a multi-inertial-node wireless monitoring and evaluating system in upper limb rehabilitation training, which comprises a motion sensor, a main control chip, a power management circuit and an upper computer, wherein the motion sensor and the main control chip form an action capturing node, the motion sensor and the main control chip carry out data transmission through an IIC protocol, the motion sensor and the main control chip are connected with a lithium battery through the power management circuit, and the main control chip is connected with the upper computer through wireless communication.
The invention uses an inertial sensor for capturing the motion of the upper limb, collects the data of a patient during rehabilitation exercise through a motion capture node, and then transmits the data by using a wireless network (ZigBee networking technology), and simultaneously provides a motion recognition method and an exercise effect evaluation method.
Preferably, there are three motion capture nodes, node 1, node 2 and node 3.
Further preferably, the node 1 is bound at the chest position of the human body through a rope belt, and the node 1 collects three-axis magnetic force data to judge the orientation of the human body; the node 2 is bound on the outer side of the upper arm of the human body through a rope belt, and the node 2 acquires three-axis acceleration data, three-axis gyroscope data and three-axis magnetic force data; the node 3 is bound on the outer side of the forearm of the human body through a rope belt, and the node 3 acquires three-axis acceleration data, three-axis gyroscope data and three-axis magnetic force data.
Preferably, the motion sensor adopts an MPU9250 nine-axis motion sensor, the main control chip adopts a CC2538ZigBee main control chip, and the upper computer adopts a MatLab upper computer.
The invention also provides a multi-inertia-node wireless monitoring and evaluating method in upper limb rehabilitation training, which comprises the following steps:
firstly, binding motion capture nodes formed by three motion sensors and a main control chip to the chest, the outer side of the upper arm and the outer side of the forearm of a human body respectively to form a node 1, a node 2 and a node 3; turning to the second step;
secondly, performing upper limb rehabilitation training after a training mode is selected on a human-computer interaction interface of the upper computer, wherein the training mode comprises two modes, namely a tracking training mode and a repeated training mode; turning to the third step;
thirdly, in the upper limb rehabilitation training process, a motion sensor of the node 1 collects triaxial magnetic force data of the chest of a human body and transmits the data to a main control chip of the node 1, a motion sensor of the node 2 collects triaxial acceleration data, triaxial gyroscope data and triaxial magnetic force data of the upper arm of the human body and transmits the three data to the main control chip of the node 2, a motion sensor of the node 3 collects triaxial acceleration data, triaxial gyroscope data and triaxial magnetic force data of the forearm of the human body and transmits the three data to the main control chip of the node 3, and the main control chips of the nodes 1, 2 and 3 transmit the data to an upper computer through wireless communication; turning to the fourth step;
and fourthly, judging the posture of the arm by the upper computer according to the received data, and then evaluating the training effect.
Therefore, in the process of upper limb rehabilitation training of a stroke patient, the patient can independently select a training mode, the motion state of the arm can be restored by displaying the motion of the upper limb in real time, the patient can better complete the rehabilitation process by the independently designed training effect evaluation method, and the monitoring and evaluation of the rehabilitation effect are realized.
Preferably, the motion capture node adopts a lithium battery power supply mode, the mode adopts a BQ24230 chip, the chip has two working modes, one mode is a mode of connecting an external power supply, the other mode is a mode of disconnecting the external power supply, when the USB interface is connected with the external power supply, the lithium battery is automatically charged through the power management circuit, and when the external power supply is disconnected, the lithium battery supplies power to the motion capture node.
Preferably, the wireless communication adopts a Z-Stack protocol Stack, the protocol adopts an MESH networking mode, the data transmission mode adopts a timed interrupt transmission mode, and the motion sensor of the motion capture node is in a low-power consumption working mode during data transmission.
Preferably, the upper computer compiles a human-computer interaction interface through a GUI (graphical user interface), controls the wireless communication module to receive the action information data sent by the action capture node, and restores the action information data to obtain the arm motion state; the human-computer interaction interface comprises four parts, namely user login, upper limb action real-time dynamic display, rehabilitation training mode selection and training effect evaluation.
Preferably, after the training mode is selected in the second step, when the training mode is a tracking training mode, the human-computer interaction interface displays a dynamic track of standard actions of rehabilitation training, so that the patient can train according to the standard actions, and meanwhile, the upper computer compares the collected arm motion state of the patient with the dynamic track of the standard actions of rehabilitation training in real time, so that the degree of completion of the actions of the patient can be evaluated conveniently; when the training mode is the repeated training mode, the rehabilitation actions to be completed are set on the human-computer interaction interface, the patient can carry out continuous rehabilitation training according to the set actions, the training actions repeated every ten times are set as a group, and the upper computer scores the completion degree of the patient after the group of actions are completed, so that the rehabilitation training score of the patient is obtained.
Preferably, in the fourth step, the method for determining the posture of the arm is as follows:
⑴ the upper computer judges the orientation of human body according to the formula (1),
or angle3= atan2（1）
Wherein,is the direction angle of the human body,the data is the three-axis magnetic data collected by the node 1 when the human body is upright in the upper limb rehabilitation training process, the geomagnetic data has no component in the vertical direction when the human body is upright,is the direction angle of the forearm of the human body,the data is geomagnetic data when the forearm performs horizontal movement, and the data is triaxial magnetic data collected by the node 3.
⑵ the upper computer processes the nine-axis data acquired by the node 2 by using quaternion and Kalman filtering algorithm to obtain quaternionAnd simultaneously obtaining the attitude angle of the upper arm according to the formula (2),
（2）
wherein,the angle of rotation of the upper arm about the z-axis,the angle of rotation of the upper arm about the x-axis,the angle of rotation of the upper arm about the y-axis,four parameters are respectively used when the upper arm posture data is expressed by a quaternion mode.
⑶ the upper computer processes the nine-axis data acquired by the node 3 by using quaternion and Kalman filtering algorithm to obtain quaternionThen the upper computer obtains the posture angle of the forearm according to the formula (3)，
（3）
Wherein,the angle of rotation of the upper arm about the z-axis,the angle of rotation of the upper arm about the x-axis,the angle of rotation of the upper arm about the y-axis,four parameters are respectively used when forearm posture data is expressed in a quaternion mode.
⑷ calculating the posture angle of the forearmTo obtain the natural state of arm's sagging, flat lifting and uplifting. The position of the arm in the front horizontal lifting is coincided with the initialized natural coordinate system, and the attitude angle data in the coincidence process isThus, it is determined whether the arm is in a natural state of sagging, flat-lifting or lifting andthe correlation is large. When in useData proximity ofWhen the arm is in a flat-lifting state,approach toWhen the arm is in a natural drooping state,approach toThe arm is in the raised state. In addition, the first and second substrates are,andthe data of the hand-held robot arm is basically kept unchanged, and whether the hand shakes can be judged according to the small amplitude change condition of the two data.
⑸ the upper computer calculates the included angle between the horizontal direction of the forearm and the horizontal direction of the node 1 according to the formula (4) to obtain the lateral lifting and forward lifting states of the arm,
（4）
wherein,is the included angle between the horizontal direction of the node 1 and the horizontal direction of the front arm. When in useIs composed ofOr is close toWhen the arm is in a forward flat-lifted stateIs composed ofOr is close toThe arms are in a side-lying and lifting state.
⑹ the upper computer calculates the included angle between the upper arm and the forearm according to the formula (5) to obtain the straight and bending state of the arm,
（5）
wherein,the included angle between the upper arm and the forearm. Determine the straightening or bending state of the armThe correlation is relatively large, and the correlation is relatively large,the size of (b) represents the size of the included angle between the upper arm and the forearm,approach toWhen the arm is in a straight state,is composed ofThe forearm and the upper arm are perpendicular to each otherThe greater the degree of flexion of the arm.
The invention has the following advantages:
1. the invention adopts a multi-node motion capture technology, avoids the problem of incomplete information acquisition in the current commonly used single-node motion capture technology, and ensures that the motion monitoring is more accurate;
2. the invention adopts the zigbee wireless networking technology, avoids the problems of complexity, redundancy and inconvenient use of the traditional wired equipment, and also avoids the problem of unfavorable data fusion in the traditional wireless network point-to-point communication;
3. the invention is applied to the upper limb recovery training of the apoplexy patient in the field of rehabilitation medical treatment, realizes remote monitoring and evaluation, standardizes the training requirements of home rehabilitation, and reduces the workload of medical care personnel.
In a word, the invention solves the problems of non-standard upper limb training mode and large workload of nursing staff in the existing home rehabilitation to a certain extent.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a diagram of a human arm node distribution according to the present invention.
Fig. 2 is a block diagram of the working principle of the present invention.
Fig. 3 is a circuit diagram of a CC2538 in a ZigBee main control chip according to the present invention.
Fig. 4 is a circuit diagram of the motion sensor of the present invention.
Fig. 5 is a circuit diagram of the node power supply chip BQ24230 of the present invention.
FIG. 6 is a flowchart illustrating operation of the human-computer interface according to the present invention.
Detailed Description
Example one
The embodiment provides a multi-inertial-node wireless monitoring and evaluating system in upper limb rehabilitation training, which is structurally shown in fig. 2 to 5 and comprises an MPU250 nine-axis motion sensor, a CC2538ZigBee main control chip, a power management circuit and a MatLab upper computer. The CC2538ZigBee main control chip is connected with a MatLab upper computer through wireless communication, the MPU250 nine-axis motion sensor and the CC2538ZigBee main control chip form an action capturing node, data transmission is carried out between the MPU250 nine-axis motion sensor and the CC2538ZigBee main control chip through an IIC protocol, the clock rate of the data transmission is 400khz, the sampling frequency is 100hz, and the MPU250 nine-axis motion sensor and the CC2538ZigBee main control chip are powered by lithium batteries through a power management circuit. The three motion capture nodes are respectively a node 1, a node 2 and a node 3, the node 1 is bound at the position of the chest of a human body through a rope, the node 1 is positioned at the chest, and the human body is static during rehabilitation training, so that the node 1 can only collect three-axis magnetic force data to judge the orientation of the human body; the node 2 is bound on the outer side of the upper arm of the human body through a rope belt, and the node 2 acquires three-axis acceleration data, three-axis gyroscope data and three-axis magnetic force data; the node 3 is bound on the outer side of the forearm of the human body through a rope belt, and the node 3 acquires three-axis acceleration data, three-axis gyroscope data and three-axis magnetic data (see figure 1).
And the data collected by the motion capture node is transmitted to the upper computer through the routing node and the data receiving node. The data receiving node mainly comprises a CC2538 and a peripheral circuit thereof, collects data sent by the three motion capture nodes, is connected with a computer through UART communication and transmits the data to a MatLab upper computer. The MatLab upper computer compiles a man-machine interaction interface through the GUI, controls a UART (Universal Asynchronous Receiver/Transmitter) to receive action information data, and restores the action information to obtain the arm motion state. During upper limb rehabilitation training of a stroke patient, the patient can independently select a training mode, the upper limb action is displayed in real time to restore the arm motion state, and the patient can better know the rehabilitation process through an independently designed training effect evaluation method.
As shown in fig. 6, the method for wirelessly monitoring and evaluating multiple inertial nodes in upper limb rehabilitation training in this embodiment includes the following steps:
firstly, motion capture nodes formed by three MPU250 nine-axis motion sensors and a CC2538ZigBee main control chip are respectively bound on the chest, the outer side of the upper arm and the outer side of the forearm of a human body to form a node 1, a node 2 and a node 3. The motion capture node adopts a lithium battery power supply mode, the mode adopts a BQ24230 chip, the chip has two working modes, one mode is a mode of connecting an external power supply, the other mode is an external power supply disconnection mode, when the USB interface is connected with the external power supply, the lithium battery is automatically charged through the power management circuit, and when the external power supply is disconnected, the lithium battery supplies power to the motion capture node.
And secondly, performing upper limb rehabilitation training after a training mode is selected on a man-machine interaction interface of the MatLab upper computer, wherein the training mode comprises two modes, namely a tracking training mode and a repeated training mode. The MatLab upper computer compiles a human-computer interaction interface through a GUI (graphical user interface), controls the wireless communication module to receive the action information data sent by the action capture node, and restores the action information data to obtain the arm motion state; the human-computer interaction interface comprises four parts, namely user login, training history, upper limb action real-time dynamic display, rehabilitation training mode selection and training effect evaluation.
The user logs in, and the stroke patient can inquire the rehabilitation process after logging in; training history, training program of next stage. The rehabilitation training process plan of the patient is designed by a rehabilitation doctor, training items and training effect evaluation scores which are done before are recorded, and the patient can reasonably select the rehabilitation training which needs to be carried out at this time in actual use.
The upper limb movement is dynamically displayed in real time, after the movement capture nodes are worn correctly, data can be uploaded to a terminal computer in real time, arm postures are obtained after processing through a multi-node movement fusion algorithm, the arm postures can be displayed by utilizing an arm model on a human-computer interaction interface, the prototype of the arm model is a 5-freedom-degree mechanical arm, the position relation between a human body and an arm and the position relation between an upper arm and a forearm can be simulated as shown in figure 1. The main rehabilitation training actions include:
A. natural sagging → side lift,
B. natural droop → front horizontal lift
C. Bending of arm
D. Front horizontal lift → side horizontal lift
E. Side lift → front lift
F. Side horizontal lifting → top lifting
G. Front horizontal lift → upper lift
When the training movements are carried out, each posture has specific posture characteristics, including static characteristics and dynamic characteristics, the characteristics are also main indexes for training evaluation, the static characteristics mainly include positions of movement starting and stopping points, included angles of forearms and upper arms when the arms do not need to bend and the like, and the dynamic characteristics include whether the tracks of the arms are uniform or not and shake or not when the arms move, whether the tracks are different greatly or not when the arms repeatedly move and the like.
And thirdly, in the upper limb rehabilitation training process, the motion sensor of the node 1 collects the triaxial magnetic force data of the chest of the human body and transmits the data to the main control chip of the node 1, the motion sensor of the node 2 collects the triaxial acceleration data, the triaxial gyroscope data and the triaxial magnetic force data of the upper arm of the human body and transmits the three data to the main control chip of the node 2, the motion sensor of the node 3 collects the triaxial acceleration data, the triaxial gyroscope data and the triaxial magnetic force data of the forearm of the human body and transmits the three data to the main control chip of the node 3, and the main control chips of the nodes 1, 2 and 3 transmit the data to an upper computer through wireless communication. The Z-Stack protocol Stack is adopted in wireless communication, the MESH networking mode is adopted in the protocol, data transmission is powerfully guaranteed, the timing interrupt sending mode is adopted in the data sending mode, the sampling frequency is controlled, the motion sensor of the motion capture node is in a low-power-consumption working mode during data sending, and the continuous working time of the node is effectively prolonged.
And fourthly, judging the posture of the arm by the upper computer according to the received data, and then evaluating the training effect.
The method for judging the posture of the arm comprises the following steps:
⑴ the upper computer judges the orientation of human body according to the formula (1),
or angle3= atan2（1）
Wherein,is the direction angle of the human body,the data is the three-axis magnetic data collected by the node 1 when the human body is upright in the upper limb rehabilitation training process, the geomagnetic data has no component in the vertical direction when the human body is upright,is the direction angle of the forearm of the human body,the data is geomagnetic data when the forearm performs horizontal movement, and the data is triaxial magnetic data collected by the node 3.
⑵ the upper computer processes the nine-axis data collected by the node 2 by quaternion and Kalman filtering algorithm to obtain the attitude angle of the upper arm, and the digital motion controller of MPU9250 is used to obtain the nine-axis data and quaternion of the node 2. The attitude angle of node 2 is obtained by:
wherein the resulting range of arctan and arcsin isThis does not cover all orientations, and the range of values for the theta angle is satisfiedTherefore, it is necessary to replace arctan with atan2 to obtain the following formula:
wherein,the angle of rotation of the upper arm about the z-axis,the angle of rotation of the upper arm about the x-axis,the angle of rotation of the upper arm about the y-axis,four parameters are respectively used when the upper arm posture data is expressed by a quaternion mode.
⑶ the upper computer processes the nine-axis data collected by the node 3 by quaternion and Kalman filtering algorithm to obtain the attitude angle of the forearm, and the digital motion controller of MPU9250 is used to obtain the nine-axis data and quaternion of the node 3. The attitude angle of node 3 is obtained by:
wherein the resulting range of arctan and arcsin isThis does not cover all orientations, and the range of values for the theta angle is satisfiedTherefore, it is necessary to replace arctan with atan3 to obtain the following formula:
wherein,the angle of rotation of the upper arm about the z-axis,the angle of rotation of the upper arm about the x-axis,the angle of rotation of the upper arm about the y-axis,four parameters are respectively used when forearm posture data is expressed in a quaternion mode.
⑷ calculating the posture angle of the forearmTo obtain the natural state of arm's sagging, flat lifting and uplifting. Because the position of the arm in the front horizontal lifting is coincident with the initialized natural coordinate system, the attitude angle data in coincidence isDue to the factThis determines whether the arm is in a natural state of drooping, flat-lifting or lifting andthe correlation is large. When in useData proximity ofWhen the arm is in a flat-lifting state,approach toWhen the arm is in a natural drooping state,approach toThe arm is in the raised state. In addition, the first and second substrates are,andthe data of the hand-held robot arm is basically kept unchanged, and whether the hand shakes can be judged according to the small amplitude change condition of the two data.
⑸ the upper computer calculates the included angle between the horizontal direction of the forearm and the horizontal direction of the node 1 according to the following formula to obtain the lateral lifting and forward lifting states of the arm:
wherein,is the included angle between the horizontal direction of the node 1 and the horizontal direction of the front arm. By passingCan judge the side-lift and front-lift states of the arm whenIs composed ofOr is close toWhen the arm is in a forward flat-lifted stateIs composed ofOr is close toThe arms are in a side-lying and lifting state.
⑹ the upper computer calculates the included angle between the upper arm and the forearm to get the straightening and bending state of the arm according to the following formula:
wherein,the included angle between the upper arm and the forearm. Determine the straightening or bending state of the armThe correlation is relatively large, and the correlation is relatively large,size of (2) representsThe included angle between the arm and the forearm. When in useApproach toWhen the arm is in a straightened stateIs composed ofWhen the forearm is perpendicular to the upper arm,the greater the degree of flexion of the arm.
The rehabilitation training mode has two types, when the training mode is a tracking training mode, the human-computer interaction interface displays the dynamic track of the designed rehabilitation training standard action, so that the patient can train according to the standard action, and meanwhile, the upper computer compares the collected arm motion state of the patient with the dynamic track of the rehabilitation training standard action in real time, so that the completion degree of the action of the patient can be evaluated conveniently. For example, when the side flat lifting training is selected, the rehabilitation action required by a patient is that the arm is lifted to a horizontal state from a natural drooping state through the body side direction at a constant speed, a man-machine interaction interface of an upper computer can display a standard dynamic track of the side flat lifting action and the angle at which the arm needs to be lifted at each moment, the upper computer can compare the arm action with the standard action in real time, when the training evaluation function evaluates that the action completion degree of the patient is qualified, the training is continued, when the action completion degree of the patient is unqualified, the system can remind the patient of the nonstandard action, the patient adjusts the arm according to the feedback information, and the rehabilitation training is continued after the adjustment is completed.
When the training mode is the repeated training mode, the rehabilitation actions to be completed are set on the human-computer interaction interface, the patient can carry out continuous rehabilitation training according to the set actions, the training actions repeated every ten times are set as a group, and the upper computer scores the completion degree of the patient after the group of actions are completed, so that the rehabilitation training score of the patient is obtained. For example, when the lateral lifting training is carried out, the patient needs to complete ten lateral lifting actions, the nonstandard actions cannot be corrected in real time in the training process, the training evaluation function can record the motion condition of the patient, and a comprehensive score is given when the training is finished, so that the training effect is reflected.
In addition, when the two types of training are carried out, object holding training or object holding training can be selected, and the patient can select small objects with different weights to carry out training according to the self condition or the requirements of nursing doctors, so that the rehabilitation training effect can be better achieved.
And the upper computer evaluates the training effect and displays the evaluation result in a training effect evaluation part of the human-computer interaction interface. The training effect evaluation is a main means for judging whether the rehabilitation training of the patient is effective or not, and can enable the patient and a rehabilitation physiotherapist to receive training feedback more intuitively. The evaluation is mainly based on the contact ratio of the arm posture of the patient and the standard posture, the time and the times for completing the rehabilitation training action, the continuity of the arm movement and the shaking condition. The system can comprehensively score according to the aspects and realize the training effect evaluation function.
In addition to the above embodiments, the present invention may have other embodiments. All technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.
Claims (10)
1. The utility model provides a many inertia nodes wireless monitoring and evaluation system in upper limbs rehabilitation training which characterized in that: the motion sensor and the master control chip form a motion capture node, data transmission is carried out between the motion sensor and the master control chip through an IIC protocol, the motion sensor and the master control chip are connected with a lithium battery through the power management circuit, and the master control chip is connected with the upper computer through wireless communication.
2. The system for wirelessly monitoring and evaluating the multiple inertial nodes in the upper limb rehabilitation training according to claim 1, wherein: the motion capture nodes are three, namely node 1, node 2 and node 3.
3. The system for wirelessly monitoring and evaluating the multiple inertial nodes in the upper limb rehabilitation training according to claim 2, wherein: the node 1 is bound at the chest position of a human body through a rope belt, and the node 1 collects three-axis magnetic force data to judge the orientation of the human body; the node 2 is bound on the outer side of the upper arm of the human body through a rope belt, and the node 2 acquires three-axis acceleration data, three-axis gyroscope data and three-axis magnetic force data; the node 3 is bound on the outer side of the forearm of the human body through a rope belt, and the node 3 acquires three-axis acceleration data, three-axis gyroscope data and three-axis magnetic force data.
4. The system for wirelessly monitoring and evaluating the multiple inertial nodes in the upper limb rehabilitation training according to claim 1, wherein: the motion sensor adopts an MPU9250 nine-axis motion sensor, the main control chip adopts a CC2538ZigBee main control chip, and the upper computer adopts a MatLab upper computer.
5. The wireless monitoring and evaluation method for multiple inertial nodes in upper limb rehabilitation training according to any one of claims 1 to 4, characterized by comprising the following steps:
firstly, binding motion capture nodes formed by three motion sensors and a main control chip to the chest, the outer side of the upper arm and the outer side of the forearm of a human body respectively to form a node 1, a node 2 and a node 3; turning to the second step;
secondly, performing upper limb rehabilitation training after a training mode is selected on a human-computer interaction interface of the upper computer, wherein the training mode comprises two modes, namely a tracking training mode and a repeated training mode; turning to the third step;
thirdly, in the upper limb rehabilitation training process, a motion sensor of the node 1 collects triaxial magnetic force data of the chest of a human body and transmits the data to a main control chip of the node 1, a motion sensor of the node 2 collects triaxial acceleration data, triaxial gyroscope data and triaxial magnetic force data of the upper arm of the human body and transmits the three data to the main control chip of the node 2, a motion sensor of the node 3 collects triaxial acceleration data, triaxial gyroscope data and triaxial magnetic force data of the forearm of the human body and transmits the three data to the main control chip of the node 3, and the main control chips of the nodes 1, 2 and 3 transmit the data to an upper computer through wireless communication; turning to the fourth step;
and fourthly, judging the posture of the arm by the upper computer according to the received data, and then evaluating the training effect.
6. The method for wirelessly monitoring and evaluating the multiple inertial nodes in the upper limb rehabilitation training according to claim 5, wherein: the motion capture node adopts a lithium battery power supply mode, the mode adopts a BQ24230 chip, the chip has two working modes, one mode is a mode of connecting an external power supply, the other mode is an external power supply disconnection mode, when the USB interface is connected with the external power supply, the lithium battery is automatically charged through the power management circuit, and when the external power supply is disconnected, the lithium battery supplies power to the motion capture node.
7. The method for wirelessly monitoring and evaluating the multiple inertial nodes in the upper limb rehabilitation training according to claim 5, wherein: the wireless communication adopts a Z-Stack protocol Stack, the protocol adopts an MESH networking mode, a data transmission mode adopts a timed interrupt transmission mode, and a motion sensor of the motion capture node is in a low-power consumption working mode during data transmission.
8. The method for wirelessly monitoring and evaluating the multiple inertial nodes in the upper limb rehabilitation training according to claim 5, wherein: the upper computer compiles a human-computer interaction interface through a GUI (graphical user interface), controls the wireless communication module to receive the action information data sent by the action capture node and restores the action information data to obtain the arm motion state; the human-computer interaction interface comprises four parts, namely user login, upper limb action real-time dynamic display, rehabilitation training mode selection and training effect evaluation.
9. The method for wirelessly monitoring and evaluating the multiple inertial nodes in the upper limb rehabilitation training according to claim 5, wherein: after the training mode is selected, when the training mode is a tracking training mode, a human-computer interaction interface displays a dynamic track of standard actions of rehabilitation training, so that a patient can train according to the standard actions, and meanwhile, an upper computer compares the collected arm motion state of the patient with the dynamic track of the standard actions of rehabilitation training in real time, so that the degree of completion of the actions of the patient can be evaluated conveniently; when the training mode is the repeated training mode, the rehabilitation actions to be completed are set on the human-computer interaction interface, the patient can carry out continuous rehabilitation training according to the set actions, the training actions repeated every ten times are set as a group, and the upper computer scores the completion degree of the patient after the group of actions are completed, so that the rehabilitation training score of the patient is obtained.
10. The method for wirelessly monitoring and evaluating the multiple inertial nodes in the upper limb rehabilitation training according to claim 5, wherein: in the fourth step, the method for judging the posture of the arm comprises the following steps:
⑴ the upper computer judges the orientation of human body according to the formula (1),
or angle3= atan2（1）
Wherein,is the direction angle of the human body,is geomagnetic data when the human body stands upright in the upper limb rehabilitation training process,is the direction angle of the forearm of the human body,geomagnetic data when the forearm is subjected to horizontal movement;
⑵ the upper computer processes the nine-axis data acquired by the node 2 by using quaternion and Kalman filtering algorithm to obtain quaternionAnd simultaneously obtaining the attitude angle of the upper arm according to the formula (2),
（2）
wherein,the angle of rotation of the upper arm about the z-axis,the angle of rotation of the upper arm about the x-axis,the angle of rotation of the upper arm about the y-axis,four parameters when the upper arm posture data are expressed in a quaternion mode are respectively represented;
⑶ the upper computer processes the nine-axis data acquired by the node 3 by using quaternion and Kalman filtering algorithm to obtain quaternionThen the upper computer obtains the posture angle of the forearm according to the formula (3)，
（3）
Wherein,the angle of rotation of the upper arm about the z-axis,the angle of rotation of the upper arm about the x-axis,the angle of rotation of the upper arm about the y-axis,four parameters when the forearm posture data is expressed by a quaternion mode are respectively represented;
⑷ calculating the posture angle of the forearmTo obtain the natural states of arm droop, horizontal lifting and uplifting;
⑸ the upper computer calculates the included angle between the horizontal direction of the forearm and the horizontal direction of the node 1 according to the formula (4) to obtain the lateral lifting and forward lifting states of the arm,
（4）
wherein,is the included angle between the horizontal direction of the node 1 and the horizontal direction of the front arm;
⑹ the upper computer calculates the included angle between the upper arm and the forearm according to the formula (5) to obtain the straight and bending state of the arm,
（5）
wherein,the included angle between the upper arm and the forearm.
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