CN108784720A - The control system and its detection method of spasm detection based on Muscle tensility sensor - Google Patents
The control system and its detection method of spasm detection based on Muscle tensility sensor Download PDFInfo
- Publication number
- CN108784720A CN108784720A CN201810213716.8A CN201810213716A CN108784720A CN 108784720 A CN108784720 A CN 108784720A CN 201810213716 A CN201810213716 A CN 201810213716A CN 108784720 A CN108784720 A CN 108784720A
- Authority
- CN
- China
- Prior art keywords
- spasm
- muscle
- muscle tensility
- module
- sensor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/22—Ergometry; Measuring muscular strength or the force of a muscular blow
- A61B5/224—Measuring muscular strength
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- Animal Behavior & Ethology (AREA)
- Surgery (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Artificial Intelligence (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Evolutionary Computation (AREA)
- Signal Processing (AREA)
- Psychiatry (AREA)
- Physiology (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Fuzzy Systems (AREA)
- Physical Education & Sports Medicine (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The present invention relates to the control system and its detection method of the spasm detection based on Muscle tensility sensor, including Muscle tensility sensing module, reception and handles the information of Muscle tensility sensing module and to the host module of Muscle tensility detection and analysis, connection host module and provide the power module of electric power, data for receiving Muscle tensility sensing module and host module and to the upper computer module of spasm tagsort and differentiation；Its detection method includes：One, the Muscle tensility image procossing of acquisition normal condition is classified after integrating；Two, the Muscle tensility image of acquisition muscle spasticity is classified after statistics；Three, it quantization assessment and is recorded in upper computer module；Four, spasm feature work is judged；Five, judge to show after comparing and obtaining a result according to quantitative criteria.The present invention using Muscle tensility sensor while, to the pathologic of spasm carry out research obtain spasm three kinds of characteristic informations and be distinguish, improve spasm detection accuracy and real-time.
Description
Technical field
The present invention relates to technical field of robot control, the control of the spasm detection specifically based on Muscle tensility sensor
System and its detection method processed.
Background technology
With the continuous development of healing robot and robotic sensor technology, patient motion is helped with healing robot
Effect ever more important of the recovery of function on clinical rehabilitation, such as spasm easily occurs for patient in rehabilitation training, and handle not
When the secondary injury that can then lead to patient, and the generation of spasm has randomness, for patient's meeting with healing robot
Heavy psychological burden is brought, spasm detection, spasm detection device seem important in real time in rehabilitation training to realize；From
The angle of Muscle tensility perception discloses the testing principle of spasm and is demarcated to its feature, when detecting according to Muscle tensility sensor
The spasm feature of calibration, judges spasm, plays spasm early warning and preventive effect.
Currently, for control system and method that the spasm of Muscle tensility sensor detects, most of sensors are not to convulsion
The feature of contraction carries out classifying or only considering that a kind of single spasm detection, the precision and accuracy for making spasm detect reduce.
As disclosed a kind of usual method of limb spasm in China Patent No. 201610205847.2 and realized this method
Device, the device by measure elbow joint angle, speed, the situation of change of acceleration, to assess upper limb spasm degree, though set
Count simple, at low cost, but it is there is no playing stricks precaution to spasm, and factor of its influence is more, such as patient activity
Limit of power, the variation etc. of environment.
By the pathologic analysis to lower limb muscles spasm, three kinds of features performance when spasm occurs is obtained：Tetanic, battle array
Contraction, cramp, the size of Muscle tensility is a constant when muscle rigidity, and muscle clonic spasm, which is the frequency of Muscle tensility, apparent wave
It is dynamic, and cramp is then tetanic and clonic spasm combination.It demarcates, is divided into herein by the feature to above-mentioned three kinds of spasm
Spasm feature and type when three kinds of channel display detections.Spasm can be examined in real time by complete control system and method
It surveys and takes precautions against, keep rehabilitation training safer.
Invention content
In order to avoid with solve above-mentioned technical problem, the present invention propose the spasm based on Muscle tensility sensor detection control
System and its detection method processed.
The technical problems to be solved by the invention are realized using following technical scheme：
The control system of spasm detection based on Muscle tensility sensor, includes being contacted with human muscle and acquiring Muscle tensility
Muscle tensility sensing module, reception and handle Muscle tensility sensing module information and to the host module of Muscle tensility detection and analysis,
Connect host module and the power module of power supply, data for receiving Muscle tensility sensing module and host module and to spasm spy
Upper computer module sign classification and differentiated.
The Muscle tensility sensing module is connected with host module by conducting wire, the side that the host module passes through wireless telecommunications
Formula is connected with upper computer module.The host computer is connected with the display module of display spasm feature.
Further, the host module is one piece of high-performance embedded controller.
Further, the Muscle tensility sensing module includes pressure measurement cell and data embedded controller.
Further, the pressure measurement cell is pressure sensor, and the pressure sensor includes varistor and letter
Number collecting unit.Then the variation of the varistor induction Muscle tensility realizes data collection by the signal gathering unit.
Further, spasm feature includes muscle rigidity, muscle clonic spasm, three kinds of cramp, each spasm feature point
Not corresponding period image is magnitude image, frequency image, amplitude-frequency image.
Further, the detection circuit figure equipped with corresponding three kinds of spasm features in the host module, in the host computer
Equipped with three kinds of channels corresponding with spasm feature, when detection, judges according to the channel in host computer and shows spasm feature.
Heretofore described Muscle tensility sensing module and host module are mounted in Muscle tensility sensor.
The detection method of the control system of spasm detection based on Muscle tensility sensor, this method include：
Step 1：The Muscle tensility image procossing of acquisition normal condition is classified after integrating.
Step 2：The Muscle tensility image of acquisition muscle spasticity is classified after statistics.
Step 3：The data of acquisition are merged and are arranged, obtain the quantization assessment standard of evaluation muscle cramp and are recorded in
In the machine module of position.
Step 4：The Muscle tensility image of acquisition patient is sent to upper computer module and judges spasm feature work.
Step 5：Institute's measured data is judged to show after comparing and obtaining a result according to the quantitative criteria of step 3.
Further, in the step 1 and step 2 the processing of Muscle tensility image integrate, statistics in host module and
It is carried out in upper computer module.
Further, the data acquired in the step 3 come from step 1 and step 2.
Further, the judgement comparison of institute's measured data carries out in upper computer module in the step 5.
Wherein Muscle tensility sensor spasm detection process be：
Host is started to work, and ADC is initialized, and ADC samples the value of FSR, Muscle tensility F is read from data buffer area, after processing
During transmitting and sending data in host computer or be back to the value that ADC samples FSR.
The diaphragm type varistor being arranged in pressure sensor because its pressure-sensitive character is at non-linear relation, therefore is difficult to reality
The accurate measurement of existing pressure value, and influenced by elastic pressure when wearing, there may be different initial pressures on varistor
Value.So this method is after the signal value that ADC samples each diaphragm type varistor, by way of given threshold, masks just
Beginning pressure disturbances, and each signal value is finally switched to the switching signal of " 0/1 " form, realize the judgement to initial ADC and FSR
And the acquisition of FSR.
After host computer starts, the data that receiving host is sent are differentiated the feature of spasm by the amplitude of Muscle tensility, frequency, really
Be set to it is any in muscle rigidity, muscle clonic spasm, cramp, and will differentiate result be stored in host computer data storage
Area is denoted as spasm feature S, and characteristic information is carried out in the display module of host computer and is shown, last host computer power cut-off.
The beneficial effects of the invention are as follows：
The present invention carries out three kinds of spies that research obtains spasm while using Muscle tensility sensor, to the pathologic of spasm
Reference ceases, and is used in combination control system completely to detect three kinds of features of spasm and is distinguish, Muscle tensility sensing module and host
Processing is transmitted to host computer after collected information is handled, calculated, the standard that host computer integrates spasm characteristic information is made
Go out real time discriminating, tetanic, clonic spasm, the feature situation of cramp, carry when three in host computer kind channel then shows spasm respectively
The high accuracy and real-time of spasm detection.
Description of the drawings
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is the block diagram of system of the present invention；
Fig. 2 is the overhaul flow chart of the present invention.
Specific implementation mode
In order to make the technical means, the creative features, the aims and the efficiencies achieved by the present invention be easy to understand, below it is right
The present invention is expanded on further.
As shown in Figure 1 to Figure 2, the control system of the spasm detection based on Muscle tensility sensor, including connect with human muscle
Touch and acquire the Muscle tensility sensing module of Muscle tensility, reception and handle the information of Muscle tensility sensing module and to Muscle tensility detection and
The host module of analysis, connection host module and the power module of power supply, for receiving Muscle tensility sensing module and host module
Data and to the upper computer module of spasm tagsort and differentiation.
The Muscle tensility sensing module is connected with host module by conducting wire, the side that the host module passes through wireless telecommunications
Formula is connected with upper computer module.The host computer is connected with the display module of display spasm feature.
The host module is one piece of high-performance embedded controller.
The Muscle tensility sensing module includes pressure measurement cell and data processing embedded controller.The pressure measurement
Unit is connected by conducting wire with data processing embedded controller, and the data processing embedded controller is by pressure measurement cell
The Muscle tensility information detected is sent to host module in real time, then host module by way of wireless telecommunications by data transfer
Into upper computer module.
The pressure measurement cell is pressure sensor, and the pressure sensor includes varistor and signal acquisition list
Member.Then the variation of the varistor induction Muscle tensility realizes data collection by the signal gathering unit.
Spasm feature includes muscle rigidity, muscle clonic spasm, three kinds of cramp, each characteristic information corresponding week
Phase image is magnitude image, frequency image, amplitude-frequency image.
Detection circuit figure equipped with corresponding three kinds of spasm features in the host module, is equipped in the host computer and feature
The corresponding three kinds of channels of information, when detection, judge according to the channel in host computer and show the feature of spasm.
Heretofore described Muscle tensility sensing module and host module are mounted in Muscle tensility sensor.
The detection method of the control system of spasm detection based on Muscle tensility sensor, this method include：
Step 1：With Muscle tensility sensing module acquire muscle normal condition under Muscle tensility image, by host with it is upper
The processing of machine is integrated, and is classified to amplitude, the frequency distribution situation of Muscle tensility, and as the feature letter for judging muscle cramp
Breath.
Step 2：With Muscle tensility sensing module acquire muscle spasticity under Muscle tensility image, according to host with it is upper
The function of machine carries out Muscle tensility the statistical classification of amplitude, frequency, and as the characteristic information for judging muscle cramp.
Step 3：According to Step 1: the collected Muscle tensility data of step 2 institute merge arrangement, evaluation muscle is obtained
The quantization assessment standard of spasm, and be recorded in host computer.
Step 4：To patient in use, the collected Muscle tensility data of Muscle tensility sensing module are carried out amplitude, frequency
Distribution, and be sent to host computer, carry out the judgement of spasm feature.
Step 5：Host computer can carry out judgement comparison according to obtained spasm characteristic information quantitative criteria to institute's measured data,
Real-time display after obtaining a result.
Wherein Muscle tensility sensor spasm detection process be：
Host is started to work, and ADC is initialized, and ADC samples the value of FSR, Muscle tensility F is read from data buffer area, after processing
During transmitting and sending data in host computer or be back to the value that ADC samples FSR.
The diaphragm type varistor being arranged in pressure sensor because its pressure-sensitive character is at non-linear relation, therefore is difficult to reality
The accurate measurement of existing pressure value, and influenced by elastic pressure when wearing, there may be different initial pressures on varistor
Value.So this method is after the signal value that ADC samples each diaphragm type varistor, by way of given threshold, masks just
Beginning pressure disturbances, and each signal value is finally switched to the switching signal of " 0/1 " form, realize the judgement to initial ADC and FSR
And the acquisition of FSR.
After host computer starts, the data that receiving host is sent are differentiated the feature of spasm by the amplitude of Muscle tensility, frequency, really
Be set to it is any in muscle rigidity, muscle clonic spasm, cramp, and will differentiate result be stored in host computer data storage
Area is denoted as spasm feature S, and characteristic information is carried out in the display module of host computer and is shown, last host computer power cut-off.
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and what is described in the above embodiment and the description is only the present invention
Principle, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these variation and
Improvement is both fallen in claimed invention.The claimed scope of the invention is by appended claims and its equivalent circle
It is fixed.
Claims (10)
1. the control system of the spasm detection based on Muscle tensility sensor, it is characterised in that：Including：
The Muscle tensility sensing module of Muscle tensility is contacted and acquired with human muscle；
Receive and handle the information of Muscle tensility sensing module and to the host module of Muscle tensility detection and analysis；
Connect host module and the power module of power supply；
Data for receiving Muscle tensility sensing module and host module and to the upper computer module of spasm tagsort and differentiation；
The Muscle tensility sensing module is connected with host module by conducting wire, the host module by way of wireless telecommunications with
Upper computer module is connected.
2. the control system of the spasm detection according to claim 1 based on Muscle tensility sensor, it is characterised in that：It is described
Host module is one piece high property embedded controller.
3. the control system of the spasm detection according to claim 1 based on Muscle tensility sensor, it is characterised in that：It is described
Muscle tensility sensing module includes pressure measurement cell and data processing embedded controller.
4. the control system of the spasm detection according to claim 3 based on Muscle tensility sensor, it is characterised in that：It is described
Pressure measurement cell is pressure sensor, and the pressure sensor includes varistor and signal gathering unit.
5. the control system of the spasm detection according to claim 1 based on Muscle tensility sensor, it is characterised in that：Spasm
Feature includes muscle rigidity, muscle clonic spasm, three kinds of cramp, each spasm feature believes that corresponding period image is width
It is worth image, frequency image, amplitude-frequency image.
6. the control system of the spasm detection according to claim 5 based on Muscle tensility sensor, it is characterised in that：It is described
It is furnished with the detection circuit figure of corresponding three kinds of spasm features in host module, corresponding with spasm feature three is equipped in the host computer
Kind of channel, when detection, judge according to the channel in host computer and show spasm feature.
7. the inspection of the control system of the spasm detection according to any one of claim 1 to 6 based on Muscle tensility sensor
Survey method, it is characterised in that：The control method includes：
Step 1：The Muscle tensility image procossing of acquisition normal condition is classified after integrating；
Step 2：The Muscle tensility image of acquisition muscle spasticity is classified after statistics；
Step 3：The data of acquisition are merged and are arranged, obtain the quantization assessment standard of evaluation muscle cramp and are recorded in host computer
In module；
Step 4：The Muscle tensility image of acquisition patient is sent to upper computer module and judges spasm feature work；
Step 5：Institute's measured data is judged to show after comparing and obtaining a result according to the quantitative criteria of step 3.
8. the detection method of the spasm detection according to claim 7 based on Muscle tensility sensor, it is characterised in that：It is described
The processing of Muscle tensility image is integrated in step 1 and step 2, statistics carries out in host module and upper computer module.
9. the detection method of the spasm detection according to claim 7 based on Muscle tensility sensor, it is characterised in that：It is described
The data acquired in step 3 come from step 1 and step 2.
10. the detection method of the spasm detection according to claim 7 based on Muscle tensility sensor, it is characterised in that：Institute
The judgement comparison for stating institute's measured data in step 5 carries out in upper computer module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810213716.8A CN108784720B (en) | 2018-03-15 | 2018-03-15 | Control system for spasm detection based on muscle tension sensor and detection method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810213716.8A CN108784720B (en) | 2018-03-15 | 2018-03-15 | Control system for spasm detection based on muscle tension sensor and detection method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108784720A true CN108784720A (en) | 2018-11-13 |
| CN108784720B CN108784720B (en) | 2020-09-25 |
Family
ID=64094812
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810213716.8A Active CN108784720B (en) | 2018-03-15 | 2018-03-15 | Control system for spasm detection based on muscle tension sensor and detection method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108784720B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110310534A (en) * | 2019-05-16 | 2019-10-08 | 徐州医科大学附属医院 | A kind of improvement Asworth Ashworth score scale medical teaching simulation teaching aid |
| CN112807002A (en) * | 2019-11-18 | 2021-05-18 | 深圳市理邦精密仪器股份有限公司 | Parameter optimization method, system, equipment and storage medium of muscle training instrument |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009154117A1 (en) * | 2008-06-20 | 2009-12-23 | 国立大学法人大阪大学 | Muscle tone measuring apparatus |
| CN101816565A (en) * | 2010-04-23 | 2010-09-01 | 哈尔滨工程大学 | Muscle spasm detection device |
| CN102908222A (en) * | 2011-08-02 | 2013-02-06 | 上海朗信医学科技有限公司 | Complete set of devices for dynamic hand spasticity orthopedics |
| US20130245511A1 (en) * | 2011-04-26 | 2013-09-19 | Virginia Commonwealth University | Spasticity reducing closed-loop force-feedback control for post-stroke gait training |
| US20140343459A1 (en) * | 2011-12-09 | 2014-11-20 | Tomei Brace Co., Ltd. | Spasticity measurement device |
| CN104352333A (en) * | 2014-10-31 | 2015-02-18 | 安阳工学院 | Rehabilitation training robot system based on parameter identification and correction |
-
2018
- 2018-03-15 CN CN201810213716.8A patent/CN108784720B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009154117A1 (en) * | 2008-06-20 | 2009-12-23 | 国立大学法人大阪大学 | Muscle tone measuring apparatus |
| CN101816565A (en) * | 2010-04-23 | 2010-09-01 | 哈尔滨工程大学 | Muscle spasm detection device |
| US20130245511A1 (en) * | 2011-04-26 | 2013-09-19 | Virginia Commonwealth University | Spasticity reducing closed-loop force-feedback control for post-stroke gait training |
| CN102908222A (en) * | 2011-08-02 | 2013-02-06 | 上海朗信医学科技有限公司 | Complete set of devices for dynamic hand spasticity orthopedics |
| US20140343459A1 (en) * | 2011-12-09 | 2014-11-20 | Tomei Brace Co., Ltd. | Spasticity measurement device |
| CN104352333A (en) * | 2014-10-31 | 2015-02-18 | 安阳工学院 | Rehabilitation training robot system based on parameter identification and correction |
Non-Patent Citations (1)
| Title |
|---|
| 马晏楠: "人体上肢肌痉挛定量评估装置研究", 《中国优秀硕士学位论文全文数据库 医药卫生科技辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110310534A (en) * | 2019-05-16 | 2019-10-08 | 徐州医科大学附属医院 | A kind of improvement Asworth Ashworth score scale medical teaching simulation teaching aid |
| CN110310534B (en) * | 2019-05-16 | 2021-07-09 | 徐州医科大学附属医院 | Simulation teaching aid for medical teaching of improved Asworth muscle tension rating scale |
| CN112807002A (en) * | 2019-11-18 | 2021-05-18 | 深圳市理邦精密仪器股份有限公司 | Parameter optimization method, system, equipment and storage medium of muscle training instrument |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108784720B (en) | 2020-09-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2017156835A1 (en) | Smart method and system for body building posture identification, assessment, warning and intensity estimation | |
| US8405510B2 (en) | System for measuring body balance signals and a method for analyzing the same | |
| CN103006187A (en) | Non-contact vital sign data monitoring system and non-contact vital sign data monitoring method | |
| CN104190068A (en) | Push-up tester | |
| CN108433735A (en) | A kind of spasm sensor based on Muscle tensility detection | |
| CN108784720A (en) | The control system and its detection method of spasm detection based on Muscle tensility sensor | |
| CN108303141A (en) | A kind of city environmental quality monitoring system | |
| KR20120098538A (en) | Apparatus for tremor measure of fingers | |
| CN109730686A (en) | Gait detection analysis instrument based on sensor array | |
| CN104545892A (en) | Human blood pressure analysis method based on electrocardiogram identification | |
| CN102379697A (en) | Detection device and calibration method for scanning and imaging pre-signal conditioning module by electrical impedance | |
| CN204542069U (en) | For the fitness test system of round race | |
| CN107519618A (en) | A kind of foot rehabilitation training equipment | |
| CN104680718A (en) | Intelligent bracelet | |
| CN201870634U (en) | All-in-one machine for human health detection | |
| KR101424124B1 (en) | Apparatus for sensing animal behavior using touch sensor | |
| CN107367582A (en) | A kind of clothes comfort level measuring system | |
| CN114652303B (en) | Intelligent monitoring system | |
| CN206044612U (en) | Infrared ultrasound wave combines human body detection device | |
| Mertes et al. | Measuring weight and location of individual bites using a sensor augmented smart plate | |
| CN206147905U (en) | System based on multi -sensor fusion fall detection | |
| CN106606358A (en) | Heart rate measuring method and apparatus | |
| CN107656638B (en) | Fatigue detection delay compensation test system and method | |
| KR100745034B1 (en) | Integrative muscle function analysis device | |
| CN112985649A (en) | Mechanical information detection system based on flexible distributed capacitive touch sensor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |