CN108784720B - Control system for spasm detection based on muscle tension sensor and detection method thereof - Google Patents
Control system for spasm detection based on muscle tension sensor and detection method thereof Download PDFInfo
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- CN108784720B CN108784720B CN201810213716.8A CN201810213716A CN108784720B CN 108784720 B CN108784720 B CN 108784720B CN 201810213716 A CN201810213716 A CN 201810213716A CN 108784720 B CN108784720 B CN 108784720B
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- 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
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- 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
Abstract
The invention relates to a control system for spasm detection based on a muscle tension sensor and a detection method thereof, wherein the control system comprises a muscle tension sensing module, a host module for receiving and processing information of the muscle tension sensing module and detecting and analyzing muscle tension, a power supply module connected with the host module and providing power, and an upper computer module for receiving data of the muscle tension sensing module and the host module and classifying and judging spasm characteristics; the detection method comprises the following steps: firstly, collecting muscle tension images in a normal state, processing, integrating and then classifying; secondly, collecting muscle tension images of the muscle spasticity, and classifying after statistics; thirdly, quantitatively evaluating and recording the evaluation result in an upper computer module; fourthly, judging the spasm characteristics; and fifthly, judging and comparing according to the quantization standard, obtaining a result and displaying the result. According to the invention, while the muscle tension sensor is utilized, the pathological state of the spasm is researched to obtain three characteristic information of the spasm and the three characteristic information are distinguished, so that the accuracy and the real-time performance of the spasm detection are improved.
Description
Technical Field
The invention relates to the technical field of robot control, in particular to a spasm detection control system based on a muscle tension sensor and a detection method thereof.
Background
With the continuous development of the rehabilitation robot and the robot sensor technology, the rehabilitation robot is more important to help the recovery of the motion function of a patient in the clinical rehabilitation, for example, the patient is easy to have spasm during the rehabilitation training, the patient can have secondary injury due to improper treatment, the spasm can be generated randomly, heavy psychological burden can be brought to the patient using the rehabilitation robot, and a spasm detection device is important for realizing real-time spasm detection during the rehabilitation training; the spasm detection principle is revealed from the perspective of muscle tension sensing, the characteristics of the spasm detection principle are calibrated, and the spasm is judged according to the spasm characteristics calibrated by the muscle tension sensor during detection, so that the spasm early warning and prevention effects are achieved.
At present, most sensors do not classify the characteristics of the spasm or only consider a single spasm detection, so that the precision and accuracy of the spasm detection are reduced.
For example, chinese patent No. 201610205847.2 discloses a general method for limb spasm and a device for implementing the method, which measures the angle, velocity and acceleration of the elbow joint to evaluate the degree of upper limb spasm, and although the device is simple in design and low in cost, it does not prevent the spasm from occurring and has many factors affecting the spasm, such as the range of the patient's ability to move and the change of the environment.
Through pathological analysis of muscle spasm of lower limbs, three characteristic expressions when the spasm occurs are obtained: the tension of the muscle is constant when the muscle is strong, the muscle clonus is the frequency of the muscle with obvious fluctuation, and the painful spasm is the combination of the strong and the clonus. The characteristics of the three spasms are calibrated, and the characteristics and the types of the spasms in detection are displayed by dividing the three channels. The spasm can be detected and prevented in real time through the complete control system and method, so that the rehabilitation training is safer.
Disclosure of Invention
In order to avoid and solve the technical problems, the invention provides a control system based on spasm detection of a muscle tension sensor and a detection method thereof.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
the control system for spasm detection based on the muscle tension sensor comprises a muscle tension sensing module, a host module, a power supply module and an upper computer module, wherein the muscle tension sensing module is in contact with human muscles and used for acquiring muscle tension, the host module is used for receiving and processing information of the muscle tension sensing module and detecting and analyzing the muscle tension, the power supply module is connected with the host module and used for supplying power, and the upper computer module is used for receiving data of the muscle tension sensing module and the host module and classifying and distinguishing spasm characteristics.
The muscle tension sensing module is connected with the host module through a wire, and the host module is connected with the upper computer module in a wireless communication mode. The upper computer is connected with a display module for displaying spasm characteristics.
Further, the host module is a high-performance embedded controller.
Further, the muscle tension sensing module comprises a pressure measuring unit and an embedded controller for data processing.
Further, the pressure measurement unit is a pressure sensor, and the pressure sensor comprises a piezoresistor and a signal acquisition unit. The piezoresistors sense the change of muscle tension and then realize data collection through the signal acquisition unit.
Furthermore, the spasm characteristics include three types of muscle stiffness, muscle clonus and cramps, and the period image corresponding to each spasm characteristic is an amplitude image, a frequency image and an amplitude-frequency image.
Furthermore, a detection circuit diagram corresponding to the three spasm features is arranged in the host module, three channels corresponding to the spasm features are arranged in the upper computer, and the spasm features are judged and displayed according to the channels in the upper computer during detection.
The muscle tension sensing module and the host module are arranged in the muscle tension sensor.
A method of detection for a control system based on seizure detection by a muscle tension sensor, the method comprising:
the method comprises the following steps: and collecting the muscle tension images in the normal state, processing, integrating and classifying.
Step two: muscle tension images of muscle spasticity are collected and classified after statistics.
Step three: and merging and sorting the acquired data to obtain quantitative evaluation standards for evaluating the muscle spasm and recording the quantitative evaluation standards in the upper computer module.
Step four: the muscle tension image of the patient is collected and sent to the upper computer module, and the spasm characteristics are judged.
Step five: and C, judging and comparing the measured data according to the quantization standard in the step three, and displaying after obtaining a result.
Furthermore, the processing integration and statistics of the muscle tension images in the first step and the second step are carried out in the host module and the upper computer module.
Further, the data collected in step three come from step one and step two.
And further, the judgment and comparison of the measured data in the fifth step are carried out in an upper computer module.
The process of spasm detection of the muscle tension sensor is as follows:
the host computer starts to work, the ADC is initialized, the ADC samples the value of the FSR, the muscle tension F is read from the data cache area, and after processing, the muscle tension F is transmitted and sent to the upper computer or returned to the process of sampling the value of the FSR by the ADC.
The pressure-sensitive characteristics of the film piezoresistor arranged in the pressure sensor are in a nonlinear relation, so that the accurate measurement of the pressure value is difficult to realize, and different initial pressure values can exist on the piezoresistor under the influence of the tightness pressure during wearing. Therefore, after the ADC samples the signal values of the thin film piezoresistors, the method shields the initial pressure interference by setting a threshold value, and finally converts the signal values into switching signals in the form of '0/1', thereby realizing the judgment of the initial ADC and the FSR and the acquisition of the FSR.
After the upper computer starts, the data sent by the host computer is received, the characteristics of spasm are judged according to the amplitude and the frequency of muscle tension, the characteristics of muscle stiffness, muscle clonus and painful spasm are determined, the judgment result is stored in a data storage area of the upper computer and is recorded as spasm characteristics S, characteristic information is displayed in a display module of the upper computer, and finally the upper computer finishes working.
The invention has the beneficial effects that:
the invention utilizes the muscle tension sensor, simultaneously researches the pathological state of the spasm to obtain three kinds of characteristic information of the spasm, completely detects the three kinds of characteristics of the spasm by using the control system and distinguishes the characteristics, the muscle tension sensing module and the host process the acquired information, the acquired information is transmitted to the upper computer after calculation, the upper computer synthesizes the standard of the spasm characteristic information to judge in real time, and three channels in the upper computer respectively display the characteristic conditions of the spasticity, the clonus and the cramp, thereby improving the accuracy and the real-time property of the spasm detection.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a block diagram of the system components of the present invention;
FIG. 2 is a flow chart of the detection according to the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained below.
As shown in fig. 1 to 2, the control system for detecting spasm based on the muscle tension sensor includes a muscle tension sensing module contacting with muscles of a human body and collecting muscle tension, a host module receiving and processing information of the muscle tension sensing module and detecting and analyzing muscle tension, a power supply module connected with the host module and supplying power, and an upper computer module for receiving data of the muscle tension sensing module and the host module and classifying and distinguishing spasm characteristics.
The muscle tension sensing module is connected with the host module through a wire, and the host module is connected with the upper computer module in a wireless communication mode. The upper computer is connected with a display module for displaying spasm characteristics.
The host module is a high-performance embedded controller.
The muscle tension sensing module comprises a pressure measuring unit and an embedded controller for data processing. The pressure measurement unit is connected with the embedded controller for data processing through a wire, the embedded controller for data processing sends the muscle tension information detected by the pressure measurement unit to the host module in real time, and then the host module transmits the data to the upper computer module in a wireless communication mode.
The pressure measurement unit is a pressure sensor, and the pressure sensor comprises a piezoresistor and a signal acquisition unit. The piezoresistors sense the change of muscle tension and then realize data collection through the signal acquisition unit.
The spasm characteristics comprise three types of muscle rigidity, muscle clonus and cramps, and the periodic image corresponding to each type of characteristic information is an amplitude image, a frequency image and an amplitude-frequency image.
The main machine module is provided with a detection circuit diagram corresponding to three spasm characteristics, the upper machine is provided with three channels corresponding to characteristic information, and the spasm characteristics are judged and displayed according to the channels in the upper machine during detection.
The muscle tension sensing module and the host module are arranged in the muscle tension sensor.
A method of detection for a control system based on seizure detection by a muscle tension sensor, the method comprising:
the method comprises the following steps: the muscle tension sensing module is used for collecting images of muscle tension in a normal state of muscle, and the amplitude and frequency distribution conditions of the muscle tension are classified through processing integration of the host computer and the upper computer and serve as characteristic information for judging muscle spasm.
Step two: the muscle tension sensing module is used for collecting muscle tension images in a muscle spasm state, carrying out amplitude and frequency statistical classification on the muscle tension according to the functions of the host and the upper computer, and using the muscle tension images as characteristic information for judging muscle spasm.
Step three: and combining and sorting the muscle tension data acquired in the first step and the second step to obtain quantitative evaluation standards for evaluating muscle spasm, and recording the quantitative evaluation standards in the upper computer.
Step four: when the muscle tension sensor is used for a patient, the muscle tension data collected by the muscle tension sensing module are distributed in amplitude and frequency and sent to an upper computer to judge spasm characteristics.
Step five: and the upper computer judges and compares the measured data according to the obtained spasm characteristic information quantization standard, and displays the result in real time.
The process of spasm detection of the muscle tension sensor is as follows:
the host computer starts to work, the ADC is initialized, the ADC samples the value of the FSR, the muscle tension F is read from the data cache area, and after processing, the muscle tension F is transmitted and sent to the upper computer or returned to the process of sampling the value of the FSR by the ADC.
The pressure-sensitive characteristics of the film piezoresistor arranged in the pressure sensor are in a nonlinear relation, so that the accurate measurement of the pressure value is difficult to realize, and different initial pressure values can exist on the piezoresistor under the influence of the tightness pressure during wearing. Therefore, after the ADC samples the signal values of the thin film piezoresistors, the method shields the initial pressure interference by setting a threshold value, and finally converts the signal values into switching signals in the form of '0/1', thereby realizing the judgment of the initial ADC and the FSR and the acquisition of the FSR.
After the upper computer starts, the data sent by the host computer is received, the characteristics of spasm are judged according to the amplitude and the frequency of muscle tension, the characteristics of muscle stiffness, muscle clonus and painful spasm are determined, the judgment result is stored in a data storage area of the upper computer and is recorded as spasm characteristics S, characteristic information is displayed in a display module of the upper computer, and finally the upper computer finishes working.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. Control system that spasm detected based on muscle tension sensor, its characterized in that: the method comprises the following steps:
a muscle tension sensing module which is contacted with the muscle of the human body and collects the muscle tension;
the host module receives and processes the information of the muscle tension sensing module and detects and analyzes the muscle tension;
the power supply module is connected with the host module and supplies power;
the upper computer module is used for receiving data of the muscle tension sensing module and the host computer module and classifying and distinguishing spasm features;
the muscle tension sensing module is connected with the host module through a wire, and the host module is connected with the upper computer module in a wireless communication mode; the spasm characteristics comprise three types of muscle stiffness, muscle clonus and cramps, and the periodic image corresponding to each type of spasm characteristic information is an amplitude image, a frequency image and an amplitude-frequency image.
2. The muscle tone sensor-based seizure detection control system of claim 1, wherein: the host module is a high-performance embedded controller.
3. The muscle tone sensor-based seizure detection control system of claim 1, wherein: the muscle tension sensing module comprises a pressure measuring unit and an embedded controller for data processing.
4. The muscle tone sensor-based seizure detection control system of claim 3, wherein: the pressure measurement unit is a pressure sensor, and the pressure sensor comprises a piezoresistor and a signal acquisition unit.
5. The muscle tone sensor-based seizure detection control system of claim 1, wherein: the main machine module is provided with a detection circuit diagram corresponding to three spasm features, the upper machine is provided with three channels corresponding to the spasm features, and the spasm features are judged and displayed according to the channels in the upper machine during detection.
6. The detection method of a control system based on muscle tension sensor spasm detection according to any one of claims 1 to 5, wherein: the detection method comprises the following steps:
the method comprises the following steps: collecting muscle tension images in a normal state, processing, integrating and classifying;
step two: collecting muscle tension images of muscle spasticity, and classifying after statistics;
step three: the collected data are merged and collated to obtain quantitative evaluation standards for evaluating the muscle spasm and the quantitative evaluation standards are recorded in the upper computer module;
step four: collecting a muscle tension image of a patient, sending the muscle tension image to an upper computer module, and judging spasm characteristics;
step five: and C, judging and comparing the measured data according to the quantization standard in the step three, and displaying after obtaining a result.
7. The detection method of a control system based on spasticity detection by a muscle tension sensor as claimed in claim 6, wherein: and the processing integration and statistics of the muscle tension images in the first step and the second step are carried out in the host module and the upper computer module.
8. The detection method of a control system based on spasticity detection by a muscle tension sensor as claimed in claim 6, wherein: the data collected in step three come from step one and step two.
9. The detection method of a control system based on spasticity detection by a muscle tension sensor as claimed in claim 6, wherein: and judging and comparing the measured data in the fifth step in the upper computer module.
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| 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 |
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| US9060914B2 (en) * | 2011-04-26 | 2015-06-23 | Viginia Commonwealth University | Spasticity reducing closed-loop force-feedback control for post-stroke gait training |
| JP5297516B2 (en) * | 2011-12-09 | 2013-09-25 | 東名ブレース株式会社 | Spasticity measuring device |
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| 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 |
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