CN204654934U - A kind of healing hand function assessment and training system - Google Patents
A kind of healing hand function assessment and training system Download PDFInfo
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- CN204654934U CN204654934U CN201520034027.2U CN201520034027U CN204654934U CN 204654934 U CN204654934 U CN 204654934U CN 201520034027 U CN201520034027 U CN 201520034027U CN 204654934 U CN204654934 U CN 204654934U
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Abstract
A kind of healing hand function assessment of the utility model and training system, include biological electromyographic signal collection, the amplification of biological electromyographic signal, the filtration of abiotic electromyographic signal, AD sampling, biological electromyographic signal analysis and treament, driver output, collect patient's the biological electromyographic signal of four fingers, thumb, to patient's muscular strength information, assess, carry out suitable hand functional training according to assessment.
Description
Technical field
The utility model belongs to medical instruments field, especially a kind of healing hand function assessment and training system.
Background technology
Hand functional training refers to that functional activation exercise reaches and improves the grip of hand and pinch the training process of power. Be applicable to the hand muscular strength descender that a variety of causes causes, as hand injury, burn, spinal cord injury, cerebral apoplexy, brain trauma, upper limb fracture etc. The most of passive exercise of hand functional training or partial joint training, be namely for patient's sufferer situation, set numerical value, drive brought into motion at present; Respectively for the local training of four fingers, thumb, wrist; These two training methods can allow patient train hand to be trained, but do not carry out corresponding treatment for the different patient's condition of patient's different phase, can not suit the remedy to the case completely; Not according to training that the joint of human body associates, association joint can be caused synchronously not recover, even because of no association training, minus effect may be caused. And a kind of healing hand function assessment of the utility model and training system, solve above two problems, it had both collected finger, the biological electromyographic signal of thumb, to patient's muscular strength information, and assessed, then carried out suitable hand functional training according to assessment.
Utility model content
The utility model is a kind of healing hand function assessment and training system, adopts following technical scheme:
A kind of healing hand function assessment and training system, comprise biological electromyographic signal collection, the amplification of biological electromyographic signal, the filtration of abiotic electromyographic signal, AD sampling, biological electromyographic signal analysis and treament, driver output.
Biological electromyographic signal of the present utility model is collected as biological surface electromyographic signal, and electrode slice is placed on muscle skin surface, and extensor, musculus flexor to four fingers, thumb, wrist carry out Data Collection, wherein have zero potential, and zero potential is with reference to the utmost point.
Biological electromyographic signal is enlarged into by amplifying circuit amplifies faint biological electromyographic signal, and wherein amplifying circuit is differential amplifier circuit; Difference channel is symmetry and the negative feedback of circuit parameter, effectively stablizes quiescent point, suppresses common-mode signal as notable feature to amplify difference mode signal; Multiplication factor is 5000-10000 times of protozoa electromyographic signal size.
Abiotic electromyographic signal is filtered into filter circuit, by low pass filter, high-pass filter, notch filter, filters invalid noise interferences, screens useful biological electromyographic signal; Low pass filter is the biological electromyographic signal that 1000HZ below can be obtained; High-pass filter is the biological electromyographic signal that 10HZ more than can be obtained; Notch filter filters the signal of 50HZ;
AD sampling be collect three of low pass filters, high-pass filter, notch filter can collection of biological electromyographic signal, biological electromyographic signal 10HZ-1000HZ between can be chosen to be by low pass filter, high-pass filter, the signal of telecommunication of 50HZ can be filtered out by notch filter, low pass filter, high-pass filter, notch filter three operations are simultaneously chosen the biological electromyographic signal between biological electromyographic signal analysis and treament, 10HZ-1000HZ and reject the 50HZ signal of telecommunication.
Biological electromyographic signal analysis and treament is that the biological electromyographic signal that the AD of collection samples is sent to control centre, carry out data analysis and process, be treated to data signal, patient current biological electromyographic signal can be judged according to AD sampling analysis and be inclined to biological electromyographic signal, it is the motion that current muscular strength situation and muscular strength can carry out, patient can be understood in real time by this and suffer from condition, actual effect ground training targetedly simultaneously.
Biological flesh point signal be EMG=(An-Ar)/(Am-Ar) wherein, An is that the corresponding muscle in affected part joint is in the amplitude of the electromyographic signal producing in current kinetic after rectifying and wave-filtering, and the amplitude of the electromyographic signal of the Ar muscle that to be affected part joint corresponding when rest after rectifying and wave-filtering, Am is the amplitude peak of the electromyographic signal that produces during maximum spontaneous contractions of the corresponding muscle of affected part after rectifying and wave-filtering.
Driver output is according to receiving Data Bio electromyographic signal, carrying out driver output, i.e. patient's healing hand function training; A kind of healing hand function assessment is furnished with game with training system, according to different training positions and different exercise for power, is furnished with different game; Belong to is portable rehabilitation equipment simultaneously.
The utility model beneficial effect is:
A kind of healing hand function assessment and training system, comprise biological electromyographic signal collection, the amplification of biological electromyographic signal, the filtration of abiotic electromyographic signal, AD sampling, biological electromyographic signal analysis and treament, driver output.
Its innovative point has:
1. carry out real-time collection of biological electromyographic signal, assessment according to patient profiles, and carry out the hand functional training of actual effect;
2. dynamic and intelligent control, realizes interactive back-to-back running;
3. mancarried device, installation, carrying convenience.
Accompanying drawing explanation
The assessment of Fig. 1 healing hand function and training system flow chart
Fig. 2 pre-amplification circuit
Fig. 3 second amplifying circuit
Fig. 4 low-pass filter circuit figure
Fig. 5 high-pass filtering circuit figure
Fig. 6 notch filter circuit diagram
The assessment of Fig. 7 healing hand function and training system device flow chart
The assessment of Fig. 8 healing hand function and training system drive unit
Wherein: 1. tester; 20. 4 finger electromyographic electrodes; 21. thumb electromyographic electrodes; 30. 4 finger myoelectricity filtering and amplifying circuits; 31. thumb myoelectricity filtering and amplifying circuits; 4. data collecting card; 5. control centre; 6. drive unit; 901. wrist driving mechanisms; 902. four finger driving mechanisms; 903. thumb drives mechanisms.
Detailed description of the invention
Below, by reference to the accompanying drawings and detailed description of the invention, the utility model is described further:
Refer to a kind of healing hand function assessment of Fig. 1, Fig. 7, Fig. 8 the utility model and training system, tested the muscular strength signal in four fingers, thumb two joints by electrode slice, because biological electromyographic signal is faint and bioelectrical signals exists interfering signal, so electromyographic signal amplification and electromyographic signal filtration will be carried out, and collection signal, export to control centre and carry out data analysis and processing, control centre sends the instruction of drive unit motion.
Four fingers, the collection of thumb electromyographic signal are carried out to tester 1, be divided into 2 parts, one of them is four finger electromyographic electrodes 20, another one is thumb electromyographic electrode 21, the data of testing are surface electromyogram signal acquisition, four finger myoelectricity pole pieces 20 are placed on four finger muscle groups, thumb electromyographic electrode 21 is placed on thumb muscles group; Muscle extensor, musculus flexor two numerical value are gathered.
Filtering processing is amplified and carried out to the biological electromyographic signal of collecting, and described amplifying circuit is difference channel, and elementary amplification 5-1200 doubly, most preferably is 100 doubly, and secondary amplification 5-300 doubly, elects 10 most as doubly; Filter circuit is low pass filter, high-pass filter, notch filter, wherein low pass is to collect the signal of telecommunication of below 1000HZ, high-pass filter is the signal of telecommunication of collecting more than 10HZ, notch filter is the signal of telecommunication of rejecting 50HZ, filter circuit is gathered the 10-1000HZ signal of telecommunication by low pass filter, high-pass filter, notch filter, most wherein preferably is the 10-500HZ signal of telecommunication.
Before sampling, most preferably surface electromyogram signal (EMG signal) amplifies 10000 doubly, carries out bandpass filtering, trap filtration treatment is carried out to 50Hz power frequency within the scope of 10-500Hz,
Frequency with 2000Hz is sampled to EMG signal, calculation process is carried out to EMG signal with moving integration algorithm.
Formula below employing obtains standardized electromyographic signal EMG, EMG=(An-Ar)/(Am-Ar) wherein, An is that the corresponding muscle in affected part joint is in the amplitude of the electromyographic signal producing in current kinetic after rectifying and wave-filtering, and the amplitude of the electromyographic signal of the Ar muscle that to be affected part joint corresponding when rest after rectifying and wave-filtering, Am is the amplitude peak of the electromyographic signal that produces during maximum spontaneous contractions of the corresponding muscle of affected part after rectifying and wave-filtering.
Data acquisition 4, tunable output core 5, analysis and treament is carried out by computer terminal, send four fingers, thumb carries out frequency and the muscular strength size of active movement and sets, drive unit 6 according to control core 5 instruction carry out drive unit output, four finger driving mechanisms 902, thumb drives mechanism 903 exports tester 1 according to instruction and carries out active training, wrist driving mechanism 901 respectively with four finger driving mechanisms 902, thumb drives mechanism 903 links, four finger driving mechanisms and thumb drives mechanism kinematic are according to when the active training that bioelectrical signals carries out being detected, angle and dynamics that wrist driving mechanism 901 is adjusted to suitable are simultaneously carried out active movement.
To one skilled in the art, can according to technical scheme described above and design, make other various corresponding changes and deformation, and within these all changes and deformation all should belong to the protection domain of the utility model claim.
Claims (12)
1. healing hand function assessment and a training system, is characterized in that, comprises biological electromyographic signal collection, the amplification of biological electromyographic signal, the filtration of abiotic electromyographic signal, AD sampling, biological electromyographic signal analysis and treament and the driver output of connection successively.
2. a kind of healing hand function assessment according to claim 1 and training system, it is characterized in that, it is electrode slice that described biological electromyographic signal is collected used carrier.
3. a kind of healing hand function assessment according to claim 2 and training system, is characterized in that, it is reference potential that described biological electromyographic signal collection includes zero potential.
4. a kind of healing hand function assessment and the training system according to claim 2-3, it is characterized in that, extensor, musculus flexor that described biological electromyographic signal is collected as four fingers, thumb carry out Data Collection.
5. a kind of healing hand function assessment according to claim 1 and training system, it is characterized in that, described biological electromyographic signal is enlarged into differential amplifier circuit.
6. a kind of healing hand function assessment according to claim 5 and training system, it is characterized in that, described biological electromyographic signal is enlarged into 5000-10000 doubly.
7. a kind of healing hand function assessment according to claim 1 and training system, it is characterized in that, described abiotic electromyographic signal is filtered into by low pass filter, high-pass filter, notch filter, filters invalid noise interferences, screens useful biological electromyographic signal; Low pass filter is the biological electromyographic signal that 1000HZ below can be obtained; High-pass filter is the biological electromyographic signal that 10HZ more than can be obtained; Notch filter filters the power frequency component of 50HZ.
8. a kind of healing hand function assessment according to claim 1 and training system, it is characterized in that, described AD is sampled as the biological electromyographic signal between 10HZ-1000HZ.
9. a kind of healing hand function assessment according to claim 8 and training system, is characterized in that, the biological electromyographic signal between described 10HZ-1000HZ is for rejecting the 50HZ signal of telecommunication.
10. a kind of healing hand function assessment according to claim 1 and training system, it is characterized in that, described biological electromyographic signal be EMG=(An-Ar)/(Am-Ar) wherein, An is that the corresponding muscle in affected part joint is in the amplitude of the electromyographic signal producing in current kinetic after rectifying and wave-filtering, and the amplitude of the electromyographic signal of the Ar muscle that to be affected part joint corresponding when rest after rectifying and wave-filtering, Am is the amplitude peak of the electromyographic signal that produces during maximum spontaneous contractions of the corresponding muscle of affected part after rectifying and wave-filtering.
11. a kind of healing hand function assessment according to claim 1 and training systems, is characterized in that, described drive unit is that four finger driving mechanisms 902, thumb drives mechanism 903 carry out active training according to instruction output tester 1.
12. a kind of healing hand function assessment according to claim 11 and training systems, it is characterized in that, described drive unit is that wrist driving mechanism 901 links with four finger driving mechanisms 902, thumb drives mechanism 903 respectively, when four finger driving mechanisms and thumb drives mechanism kinematic, angle and dynamics that wrist driving mechanism 901 is adjusted to suitable are simultaneously carried out active movement.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106166069A (en) * | 2016-07-05 | 2016-11-30 | 上海丞电电子科技有限公司 | The microsensor of a kind of myoelectricity collection amplification and sensor-based training method |
| CN106984028A (en) * | 2017-04-29 | 2017-07-28 | 浙江大学台州研究院 | A kind of mechanism of upper extremity exercise control assessment and rehabilitation training |
| CN107736889A (en) * | 2017-09-08 | 2018-02-27 | 燕山大学 | A kind of human body coordination detection device and method |
| WO2018063111A3 (en) * | 2016-09-21 | 2018-07-26 | Soylu Abdullah Ruhi | Kinesiological surface electromyography device |
-
2015
- 2015-01-19 CN CN201520034027.2U patent/CN204654934U/en active Active
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106166069A (en) * | 2016-07-05 | 2016-11-30 | 上海丞电电子科技有限公司 | The microsensor of a kind of myoelectricity collection amplification and sensor-based training method |
| WO2018063111A3 (en) * | 2016-09-21 | 2018-07-26 | Soylu Abdullah Ruhi | Kinesiological surface electromyography device |
| CN106984028A (en) * | 2017-04-29 | 2017-07-28 | 浙江大学台州研究院 | A kind of mechanism of upper extremity exercise control assessment and rehabilitation training |
| CN107736889A (en) * | 2017-09-08 | 2018-02-27 | 燕山大学 | A kind of human body coordination detection device and method |
| CN107736889B (en) * | 2017-09-08 | 2021-01-08 | 燕山大学 | Detection method of human body coordination detection device |
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