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 present invention relates to more inertia node wireless monitoring and evaluation system and methods in a kind of rehabilitation training of upper limbs, belong to
Rehabilitation training, motion capture technical field.
Background technology
At present, the technology for capturing human action is generally divided into mechanical, acoustics formula, electromagnetic type, inertial sensor formula, light
Five major class such as formula, wherein optical profile type and inertial sensor formula are most widely used two kinds.Optical profile type is using photoelectric sensing
Device acquires the movement locus of personnel to be measured, then shot by the camera of multiple and different angles the reflective spot of photoelectric sensor come
Realize motion capture, requirement of this mode to environment is high, and has the following disadvantages：It is easily influenced during shooting by backlight,
Of high cost, image calculation is to hardware requirement height.Traditional inertial sensor is mostly wireline mode, and excessive signal wire can influence hand
The movement of arm causes trouble to rehabilitation training.
Section needs to select different training methods in different times for rehabilitation training, in the most elementary of rehabilitation of stroke patients treatment
Section is needed under the specialized guidance of Physical Therapist, is trained using the equipment of profession, the advantages of this rehabilitation training be it is more professional,
Effect is best, but a disadvantage is that costly, the resource of occupancy is more.In the later stage of rehabilitation training, patient has had certain
Autokinetic movement ability, house rehabilitation just be can yet be regarded as a kind of more convenient, more economical rehabilitation training mode, to make house rehabilitation training
A kind of more specification, it is felt to be desirable to more inertia node wireless monitoring and evaluation systems in rehabilitation training of upper limbs.
Invention content
It is an object of the invention to：In view of the defects existing in the prior art, it proposes mostly used in a kind of rehabilitation training of upper limbs
Property node wireless monitoring and evaluation system, while monitoring and evaluation method is given, this method is realized using motion sensor
Then the capture of upper limks movements realizes the wireless data transmission of multinode, finally using in host computer using wireless networking technology
The powerful mathematical operational ability of embedded software realizes that multinode blending algorithm obtains the motion state of arm, while in man-machine friendship
The mutual interface evaluation of training method of a variety of rehabilitation training patterns and science.
In order to reach object above, the present invention provides in a kind of rehabilitation training of upper limbs more inertia node wireless monitoring with
Evaluation system, including motion sensor, main control chip, electric power management circuit and host computer, the motion sensor and master control core
Piece forms motion capture node, is carried out data transmission between the motion sensor and main control chip by IIC agreements, the fortune
Dynamic sensor, main control chip are connected by electric power management circuit with lithium battery, the main control chip by wireless telecommunications with it is upper
Machine is connected.
The present invention captures inertial sensor for upper limks movements, and patient is acquired in rehabilitation exercise by motion capture node
When data, recycle wireless network（ZigBee networking technologys）Carry out data transmission, at the same propose action identification method with
Movement effects evaluation method.
Preferably, there are three the motion capture nodes, respectively node 1, node 2 and node 3.
It is further preferred that the node 1 is bound to the chest position of human body by rope band, the node 1 acquires three axis
Magnetic data, to judge the direction of human body；The node 2 is bound to by rope band on the outside of the upper arm of human body, and the node 2 is adopted
Collect 3-axis acceleration data, three-axis gyroscope data and three axis magnetic datas；Before the node 3 is bound to human body by rope band
On the outside of arm, the node 3 acquires 3-axis acceleration data, three-axis gyroscope data and three axis magnetic datas.
Preferably, the motion sensor uses nine axis movement sensors of MPU9250, and the main control chip uses CC2538
ZigBee main control chips, the host computer use MatLab host computers.
The present invention also provides a kind of more inertia node wireless monitoring and evaluation methods in rehabilitation training of upper limbs, this method
Include the following steps：
The first step, the chest that the motion capture node that three motion sensors and main control chip are formed is bound to human body respectively,
On the outside of upper arm and on the outside of forearm, node 1, node 2 and node 3 are formed；Go to second step；
Second step carries out rehabilitation training of upper limbs after the human-computer interaction interface selection training mode of host computer, and training mode includes
Two kinds, one kind is track training pattern, and another kind is repetition training pattern；Go to third step；
Third walks, during rehabilitation training of upper limbs, and the three axis magnetic datas in the motion sensor acquisition human body chest of node 1 are simultaneously
Transfer data to the main control chip of node 1, the 3-axis acceleration data of the motion sensor acquisition human body upper arm of node 2, three
Axis gyro data and three axis magnetic datas and the main control chip that above-mentioned three kinds of data are transferred to node 2, the movement of node 3 pass
3-axis acceleration data, three-axis gyroscope data and the three axis magnetic datas of sensor acquisition human body forearm and by above-mentioned three kinds of data
The main control chip of node 3 is transferred to, the main control chip of node 1,2,3 transfers data to host computer by wireless telecommunications；It goes to
4th step；
4th step, host computer judge the posture of arm according to the data of reception, are then trained effect assessment.
In this way, during stroke patient carries out rehabilitation training of upper limbs, patient can independently select training mode, upper main drive
Arm motion state can be restored by making real-time display, patient be allowed preferably to complete by the training effect evaluation method of autonomous Design
Rehabilitation process realizes the monitoring and evaluation of rehabilitation efficacy.
Preferably, the motion capture node uses lithium battery power supply mode, and which uses BQ24230 chips, the core
Piece is there are two types of operating mode, and one kind is connection external power supply pattern, and another kind is external power supply Disconnected mode, when USB interface connects
When connecing external power supply, lithium battery is by electric power management circuit automatic charging, and when external power supply disconnects, lithium battery is motion capture
Node is powered.
Preferably, the wireless telecommunications use Z-Stack protocol stacks, which uses MESH networking modes, and data are sent
Mode uses Interruption sending method, and when data are sent, the motion sensor of motion capture node is in low-power consumption Working mould
Formula.
Preferably, the host computer writes human-computer interaction interface by GUI, and control wireless communication module receives motion capture
The action message data that node is sent, and action message data convert is obtained into arm motion state；Human-computer interaction interface includes
User logs in, upper limks movements Real time dynamic display, and rehabilitation training model selection and training effect evaluate four parts.
Preferably, after second step selection training mode, when training mode is track training pattern, human-computer interaction interface is shown
Show the dynamic trajectory of rehabilitation training standard operation, patient is allowed to follow standard operation and is trained, while host computer is by the disease of acquisition
Human arm motion state is compared in real time with rehabilitation training standard operation dynamic trajectory, in order to evaluate the completion of patient's action
Degree；When training mode be repetition training pattern when, human-computer interaction interface setting need complete rehabilitation action, allow patient according to
The action of setting carries out continuous rehabilitation training, and sets the training actions of every ten repetitions as one group, after completion set
Host computer gives a mark to the completeness of patient, obtains the rehabilitation training achievement of patient.
Preferably, in the 4th step, the method for judging arm posture is as follows：
(i) host computer according to（1）Formula judges the direction of human body,
Or angle3=atan2（1）
Wherein,For the deflection of human body,For the ground magnetic number during rehabilitation training of upper limbs during person upright
It is the three axis magnetic datas that node 1 acquires according to, the data, during person upright, geomagnetic data is no component in vertical direction,For the deflection of human body forearm,Geomagnetic data during horizontal movement is carried out for forearm, which adopts for node 3
Three axis magnetic datas of collection.
(ii) host computer using quaternary number and Kalman filtering algorithm to nine number of axle that node 2 acquires according to being handled to obtain four
First number, while basis（2）Formula obtains the attitude angle of upper arm,
（2）
Wherein,For the angle that upper arm is rotated around z-axis,For the angle that upper arm is rotated around x-axis,It is rotated for upper arm around y-axis
Angle,Four parameters when respectively upper arm attitude data is indicated with quaternary number mode.
(iii) host computer using quaternary number and Kalman filtering algorithm to nine number of axle that node 3 acquires according to being handled to obtain four
First number, then host computer according to（3）Formula obtains the attitude angle of forearm,
（3）
Wherein,For the angle that upper arm is rotated around z-axis,For the angle that upper arm is rotated around x-axis,It is rotated for upper arm around y-axis
Angle,Four parameters when respectively forearm attitude data is indicated with quaternary number mode.
(iv) the attitude angle of forearm is calculatedTo obtain the state for naturally drooping, putting down act and upper act of arm.Arm is preceding
Flat to be overlapped the location of when lifting with the natural system of coordinates of initialization, attitude angle data during coincidence is, therefore judge arm
Whether be naturally droop, it is flat lift or upper act state withCorrelation is larger.WhenData approachWhen arm lift shape in flat
State,It is closeWhen arm in naturally drooping state,It is closeWhen arm present act state.In addition,With's
Data are held essentially constant and can judge whether arm is shaken according to the situation of change by a small margin of the two data.
(v) host computer according to（4）Formula calculates the angle of forearm horizontal direction and 1 horizontal direction of node to obtain the side of arm
The state with preceding act is lifted,
（4）
Wherein,Angle for 1 horizontal direction of node and forearm horizontal direction.WhenForIt is or closeWhen
Arm is in front raise state, whenForIt is or closeWhen arm be in side raise state.
(vi) host computer according to（5）Formula calculate the angle of upper arm and forearm with obtain arm stretch and flexuosity,
（5）
Wherein,For upper arm and the angle of forearm.Judge arm stretch or flexuosity withCorrelation is larger,Size illustrate corner dimension between upper arm and forearm,It is closeWhen arm in straight configuration,ForWhen forearm and upper arm it is vertical, andThe bending degree of more big then arm is bigger.
Advantages of the present invention is as follows：
1. the present invention is avoided using multinode motion capture technology existing for currently used single node motion capture technology
The incomplete problem of information collection, the monitoring for making action are more accurate；
2. the present invention using zigbee wireless networking technologies, avoids conventional wired devices complexity, redundancy, inconvenient to use asks
Topic, the networking technology also avoid the problem of being unfavorable for data fusion existing for the point to-point communication of conventional wireless site；
3. the upper limb that the present invention is applied to stroke patient in rehabilitation medical field resumes training, long-range monitoring and evaluation is realized,
The specification training requirement of house rehabilitation reduces the workload of medical staff.
In short, the present invention solves the lack of standardization and shield of upper limb training method in existing house rehabilitation to a certain extent
Manage the problem of person works' amount is big.
Description of the drawings
The present invention is further illustrated below in conjunction with the accompanying drawings.
Fig. 1 is human arm Node distribution figure in the present invention.
Fig. 2 is the operation principle block diagram of the present invention.
The circuit diagram that Fig. 3 is CC2538 in ZigBee main control chips in the present invention.
Fig. 4 is the circuit diagram of motion sensor in the present invention.
Fig. 5 is the circuit diagram of interior joint power supply chip BQ24230 of the present invention.
Fig. 6 is the human-computer interaction interface operational flowchart of the present invention.
Specific embodiment
Embodiment one
More inertia node wireless monitoring and evaluation systems in a kind of rehabilitation training of upper limbs are present embodiments provided, structure is as schemed
Shown in 2 to 5, including nine axis movement sensors of MPU250, CC2538 ZigBee main control chips, electric power management circuit and MatLab
Host computer.CC2538 ZigBee main control chips are connected by wireless telecommunications with MatLab host computers, nine axis motion-sensings of MPU250
Device and CC2538 ZigBee main control chips form motion capture node, nine axis movement sensors of MPU250 and CC2538 ZigBee
Carried out data transmission between main control chip by IIC agreements, the clock rate of data transmission is 400khz, and sample frequency is
Nine axis movement sensor of 100hz, MPU250, CC2538 ZigBee main control chips are carried out by lithium battery by electric power management circuit
Power supply.There are three motion capture nodes, respectively node 1, and node 2 and node 3, node 1 are bound to the chest of human body by rope band
Mouth position, node 1 is located at chest, and when carrying out rehabilitation training, human body is stationary, therefore node 1 can only acquire three axis
Magnetic data, to judge the direction of human body；Node 2 is bound to by rope band on the outside of the upper arm of human body, and node 2 acquires three axis and adds
Speed data, three-axis gyroscope data and three axis magnetic datas；Node 3 is bound to by rope band on the outside of the forearm of human body, node 3
Acquire 3-axis acceleration data, three-axis gyroscope data and three axis magnetic datas（See Fig. 1）.
The data of motion capture node acquisition are delivered to host computer by routing node and data reception node.Data receiver
Node is mainly made of CC2538 and its peripheral circuit, and receiving node collects the data that three motion capture nodes are sent, and passes through
UART communications are connected with computer and data are reached MatLab host computers.MatLab host computers write human-computer interaction circle by GUI
Face controls UART（Universal Asynchronous Receiver/Transmitter, universal asynchronous receiving-transmitting transmitter）It connects
Action message data are received, action message is restored, obtains arm motion state.Rehabilitation training of upper limbs is carried out in stroke patient
In, patient can independently select training mode, and upper limks movements real-time display can restore arm motion state, pass through autonomous Design
Training effect evaluation method patient is allowed to be better understood by rehabilitation process.
More inertia node wireless monitoring and evaluation methods in the rehabilitation training of upper limbs of the present embodiment, as shown in fig. 6, the party
Method includes the following steps：
The first step, the motion capture section for forming three nine axis movement sensors of MPU250 and CC2538 ZigBee main control chips
Point is bound to respectively on the outside of the chest of human body, upper arm outside and forearm, forms node 1, node 2 and node 3.Motion capture node
Using lithium battery power supply mode, which uses BQ24230 chips, and for the chip there are two types of operating mode, one kind is that connection is external
Electric source modes, another kind are external power supply Disconnected modes, and when USB interface connects external power supply, lithium battery passes through power management
Circuit automatic charging, when external power supply disconnects, lithium battery is powered for motion capture node.
Second step carries out rehabilitation training of upper limbs after the human-computer interaction interface selection training mode of MatLab host computers, instructs
Practice pattern and include two kinds, one kind is track training pattern, and another kind is repetition training pattern.MatLab host computers are compiled by GUI
Human-computer interaction interface is write, control wireless communication module receives the action message data that motion capture node is sent, and action is believed
Breath data convert obtains arm motion state；Human-computer interaction interface is logged in including user, training history, the real-time dynamic of upper limks movements
It has been shown that, rehabilitation training model selection and training effect evaluate four parts.
User logs in, and stroke patient can inquire the rehabilitation process of oneself after logging in；Training history, the instruction of next stage
Practice plan.The rehabilitation training schedule of patient is designed by physiatrician, and records the training program done before this and training
Effect assessment achievement, in actual use patient can reasonably select this rehabilitation training for carrying out of needs.
Upper limks movements Real time dynamic display, after correctly motion capture node is worn, data can upload to terminal electricity in real time
Brain after blending algorithm processing is acted by multinode, obtains arm posture, can utilize arm models will on human-computer interaction interface
Arm posture shows that the prototype of arm models is the mechanical arm of a 5DOF, can simulate human body and arm, on
The position relationship of arm and forearm is shown in Fig. 1.Main rehabilitation exercise motion includes：
A. → side raise is naturally drooped,
B. → front raise is naturally drooped
C. arm bending
D. front raise → side raise
E. side raise → front raise
F. side raise → upper act
G. front raise → upper act
When carrying out these training actions, each posture has its specific posture feature, including static nature and behavioral characteristics,
These features are also to be trained the leading indicator of evaluation, and static nature mainly includes the position of action terminal, and arm is not required to
Angle of forearm and upper arm etc. when being bent, behavioral characteristics include arm motion when track whether at the uniform velocity, if shake, repeatedly weight
Whether difference is larger etc. for track when moving again.
Third walks, during rehabilitation training of upper limbs, the three axis magnetic force numbers in the motion sensor acquisition human body chest of node 1
According to and transfer data to the main control chip of node 1, the 3-axis acceleration number of the motion sensor acquisition human body upper arm of node 2
The main control chip of node 2 is transferred to according to, three-axis gyroscope data and three axis magnetic datas and by above-mentioned three kinds of data, node 3
3-axis acceleration data, three-axis gyroscope data and the three axis magnetic datas of motion sensor acquisition human body forearm and by above-mentioned three
Kind of data are transferred to the main control chip of node 3, and the main control chip of node 1,2,3 is transferred data to upper by wireless telecommunications
Machine.Wireless telecommunications use Z-Stack protocol stacks, which uses MESH networking modes, the sound assurance transmission of data, number
According to sending method using Interruption sending method, and sample frequency is controlled with this, the motion capture node when data are sent
Motion sensor is in low power mode of operation, effectively raises the continuous working period of node.
4th step, host computer judge the posture of arm according to the data of reception, are then trained effect assessment.
The method for judging arm posture is as follows：
(i) host computer according to（1）Formula judges the direction of human body,
Or angle3=atan2（1）
Wherein,For the deflection of human body,For the ground magnetic number during rehabilitation training of upper limbs during person upright
It is the three axis magnetic datas that node 1 acquires according to, the data, during person upright, geomagnetic data is no component in vertical direction,For the deflection of human body forearm,Geomagnetic data during horizontal movement is carried out for forearm, which is node 3
Three axis magnetic datas of acquisition.
(ii) host computer using quaternary number and Kalman filtering algorithm to nine number of axle that node 2 acquires according to being handled to obtain
The attitude angle of arm.Nine number of axle of node 2 are obtained using the digital motion controller of MPU9250 according to this and quaternary number.The attitude angle of node 2 is obtained by following formula：
Wherein, the range of results of arctan and arcsin is, this can not cover all directions, for θ angles
Value range met, it is therefore desirable to arctan is replaced with atan2, obtains following formula：
Wherein,For the angle that upper arm is rotated around z-axis,For the angle that upper arm is rotated around x-axis,It is rotated for upper arm around y-axis
Angle,Four parameters when respectively upper arm attitude data is indicated with quaternary number mode.
(iii) host computer using quaternary number and Kalman filtering algorithm to nine number of axle that node 3 acquires according to being handled before obtaining
The attitude angle of arm.Nine number of axle of node 3 are obtained using the digital motion controller of MPU9250 according to this and quaternary number.The attitude angle of node 3 is obtained by following formula：
Wherein, the range of results of arctan and arcsin is, this can not cover all directions, for θ angles
Value range met, it is therefore desirable to arctan is replaced with atan3, obtains following formula：
Wherein,For the angle that upper arm is rotated around z-axis,For the angle that upper arm is rotated around x-axis,It is rotated for upper arm around y-axis
Angle,Four parameters when respectively forearm attitude data is indicated with quaternary number mode.
(iv) the attitude angle of forearm is calculatedTo obtain the state for naturally drooping, putting down act and upper act of arm.Due to arm
It is overlapped the location of in front raise with the natural system of coordinates of initialization, attitude angle data during coincidence is, therefore judge
Arm whether be naturally droop, it is flat lift or upper act state withCorrelation is larger.WhenData approachWhen arm lifted in flat
State,It is closeWhen arm in naturally drooping state,It is closeWhen arm present act state.In addition,With
Data be held essentially constant and can judge whether arm is shaken according to the situation of change by a small margin of the two data.
(v) host computer calculates forearm horizontal direction and is lifted with the angle of 1 horizontal direction of node with obtaining the side of arm according to the following formula
With the state of preceding act：
Wherein,Angle for 1 horizontal direction of node and forearm horizontal direction.Pass throughIt can judge arm
Side is lifted and preceding act state, whenForIt is or closeWhen arm in front raise state, whenForOr
It is closeWhen arm be in side raise state.
(vi) host computer calculate the angle of upper arm and forearm according to the following formula with obtain arm stretch and flexuosity：
Wherein,For upper arm and the angle of forearm.Judge arm stretch or flexuosity withCorrelation is larger,Size illustrate corner dimension between upper arm and forearm.WhenIt is closeWhen arm in straight configuration,
WhenForWhen forearm and upper arm it is vertical,The bending degree of more big then arm is bigger.
There are two types of rehabilitation training patterns, and when training mode is track training pattern, human-computer interaction interface, which is shown, to be designed
Rehabilitation training standard operation dynamic trajectory, patient is allowed to follow standard operation and is trained, while host computer is by the disease of acquisition
Human arm motion state is compared in real time with rehabilitation training standard operation dynamic trajectory, in order to evaluate the completion of patient's action
Degree.Such as when selecting side raise training, it is arm by naturally drooping state through body side that patient, which needs the rehabilitation done action,
To at the uniform velocity lifting to horizontality, the human-computer interaction interface of host computer can show the standard dynamic trajectory of side raise action and every
The angle that a moment arm needs lift, host computer can compare arm action and standard operation in real time, when its evaluation of training function
Evaluate patient act completeness qualification when, training continue, when patient act completeness it is unqualified when, system can remind where
Act nonstandard, patient is adjusted arm according to feedack, continues rehabilitation training after the completion of adjustment.
When training mode is repetition training pattern, the rehabilitation completed action is needed in human-computer interaction interface setting, allows disease
People carries out continuous rehabilitation training according to the action of setting, and the training action for setting every ten repetitions completes one group as one group
Host computer gives a mark to the completeness of patient after action, obtains the rehabilitation training achievement of patient.Such as carry out side raise training
When, patient needs to complete the action of ten side raises, in training process will not the non-type action of real time correction, evaluation of training function
The motion conditions of patient can be recorded, a composite score are provided at the end of training, so as to reflect training effect.
In addition, when carrying out above two training, it can select to hold object training or non-object of holding is trained, patient is according to itself feelings
The requirement of condition or care physician, the wisp that different weight is held in selection are trained, and can preferably reach rehabilitation training
Effect.
Host computer evaluates training effect, and by evaluation result include human-computer interaction interface " training effect is commented
Valency " part.Training effect evaluation is for judging that patient carries out the whether effective main means of rehabilitation training, it can be more intuitive
Patient and rehabilitation physical therapy doctor is allowed to receive training feedback.The Main Basiss of evaluation are the weights of patient's arm posture and standard posture
It is right, complete time and the number of rehabilitation exercise motion, continuity and shake situation during arm motion.System can basis
These aspects carry out comprehensive marking, realize training effect Function of Evaluation.
In addition to the implementation, the present invention can also have other embodiment.It is all to use equivalent substitution or equivalent transformation shape
Into technical solution, all fall within the present invention claims protection domain.
Claims (10)
1. a kind of more inertia node wireless monitoring and evaluation systems in rehabilitation training of upper limbs, it is characterised in that：It is passed including movement
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 lead to
It crosses electric power management circuit with lithium battery to be connected, the main control chip is connected by wireless telecommunications with host computer.
2. more inertia node wireless monitoring and evaluation systems in a kind of rehabilitation training of upper limbs according to claim 1, special
Sign is：There are three the motion capture nodes, respectively node 1, node 2 and node 3.
3. more inertia node wireless monitoring and evaluation systems in a kind of rehabilitation training of upper limbs according to claim 2, special
Sign is：The node 1 is bound to the chest position of human body by rope band, and the node 1 acquires three axis magnetic datas, to sentence
The direction of disconnected human body；The node 2 is bound to by rope band on the outside of the upper arm of human body, and the node 2 acquires 3-axis acceleration number
According to, three-axis gyroscope data and three axis magnetic datas；The node 3 is bound to by rope band on the outside of the forearm of human body, the section
3 acquisition 3-axis acceleration data of point, three-axis gyroscope data and three axis magnetic datas.
4. more inertia node wireless monitoring and evaluation systems in a kind of rehabilitation training of upper limbs according to claim 1, special
Sign is：The motion sensor uses nine axis movement sensors of MPU9250, and the main control chip uses CC2538 ZigBee
Main control chip, the host computer use MatLab host computers.
5. according to more inertia node wireless monitoring and evaluations in a kind of any one of Claims 1-4 rehabilitation training of upper limbs
Method, which is characterized in that include the following steps：
The first step, the chest that the motion capture node that three motion sensors and main control chip are formed is bound to human body respectively,
On the outside of upper arm and on the outside of forearm, node 1, node 2 and node 3 are formed；Go to second step；
Second step carries out rehabilitation training of upper limbs after the human-computer interaction interface selection training mode of host computer, and training mode includes
Two kinds, one kind is track training pattern, and another kind is repetition training pattern；Go to third step；
Third walks, during rehabilitation training of upper limbs, and the three axis magnetic datas in the motion sensor acquisition human body chest of node 1 are simultaneously
Transfer data to the main control chip of node 1, the 3-axis acceleration data of the motion sensor acquisition human body upper arm of node 2, three
Axis gyro data and three axis magnetic datas and the main control chip that above-mentioned three kinds of data are transferred to node 2, the movement of node 3 pass
3-axis acceleration data, three-axis gyroscope data and the three axis magnetic datas of sensor acquisition human body forearm and by above-mentioned three kinds of data
The main control chip of node 3 is transferred to, the main control chip of node 1,2,3 transfers data to host computer by wireless telecommunications；It goes to
4th step；
4th step, host computer judge the posture of arm according to the data of reception, are then trained effect assessment.
6. a kind of more inertia node wireless monitoring and evaluation methods in rehabilitation training of upper limbs according to claim 5, special
Sign is：The motion capture node uses lithium battery power supply mode, and which uses BQ24230 chips, and there are two types of the chips
Operating mode, one kind are connection external power supply patterns, and another kind is external power supply Disconnected mode, when USB interface connects external electrical
During source, lithium battery is by electric power management circuit automatic charging, and when external power supply disconnects, lithium battery is supplied for motion capture node
Electricity.
7. a kind of more inertia node wireless monitoring and evaluation methods in rehabilitation training of upper limbs according to claim 5, special
Sign is：The wireless telecommunications use Z-Stack protocol stacks, which uses MESH networking modes, and data sender's formula uses
Interruption sending method, when data are sent, the motion sensor of motion capture node is in low power mode of operation.
8. a kind of more inertia node wireless monitoring and evaluation methods in rehabilitation training of upper limbs according to claim 5, special
Sign is：The host computer writes human-computer interaction interface by GUI, and control wireless communication module receives motion capture node and sends
Action message data, and action message data convert is obtained into arm motion state；Human-computer interaction interface is logged in including user,
Upper limks movements Real time dynamic display, rehabilitation training model selection and training effect evaluate four parts.
9. a kind of more inertia node wireless monitoring and evaluation methods in rehabilitation training of upper limbs according to claim 5, special
Sign is：After selecting training mode, when training mode is track training pattern, human-computer interaction interface shows rehabilitation training standard
The dynamic trajectory of action allows patient to follow standard operation and is trained, while host computer is by patient's arm motion state of acquisition
It is compared in real time with rehabilitation training standard operation dynamic trajectory, in order to evaluate the completeness of patient's action；Work as training mode
During for repetition training pattern, need the rehabilitation completed action in human-computer interaction interface setting, allow patient according to the action of setting into
The continuous rehabilitation training of row, and the training actions of every ten repetitions is set as one group, host computer is to patient after completion set
Completeness give a mark, obtain the rehabilitation training achievement of patient.
10. a kind of more inertia node wireless monitoring and evaluation methods in rehabilitation training of upper limbs according to claim 5, special
Sign is：In 4th step, the method for judging arm posture is as follows：
(i) host computer according to（1）Formula judges the direction of human body,
Or angle3=atan2（1）
Wherein,For the deflection of human body,For the geomagnetic data during rehabilitation training of upper limbs during person upright,For the deflection of human body forearm,Geomagnetic data during horizontal movement is carried out for forearm；
(ii) host computer using quaternary number and Kalman filtering algorithm to nine number of axle that node 2 acquires according to being handled to obtain quaternary number, while basis（2）Formula obtains the attitude angle of upper arm,
（2）
Wherein,For the angle that upper arm is rotated around z-axis,For the angle that upper arm is rotated around x-axis,It is rotated for upper arm around y-axis
Angle,Four parameters when respectively upper arm attitude data is indicated with quaternary number mode；
(iii) host computer using quaternary number and Kalman filtering algorithm to nine number of axle that node 3 acquires according to being handled to obtain quaternary number, then host computer according to（3）Formula obtains the attitude angle of forearm,
（3）
Wherein,For the angle that upper arm is rotated around z-axis,For the angle that upper arm is rotated around x-axis,It is rotated for upper arm around y-axis
Angle,Four parameters when respectively forearm attitude data is indicated with quaternary number mode；
(iv) the attitude angle of forearm is calculatedTo obtain the state for naturally drooping, putting down act and upper act of arm；
(v) host computer according to（4）Formula calculate the angle of forearm horizontal direction and 1 horizontal direction of node with obtain the side of arm lift and
The state of preceding act,
（4）
Wherein,Angle for 1 horizontal direction of node and forearm horizontal direction；
(vi) host computer according to（5）Formula calculate the angle of upper arm and forearm with obtain arm stretch and flexuosity,
（5）
Wherein,For upper arm and the angle of forearm.
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