Research And Development Of Muscle Fatigue Monitoring System Based On Multi-channel Dielectric Constant Testing Technology

Muscle fatigue refers to the physiological phenomenon in which the muscle undergoes a certain amount of contraction movement,and the maximum contraction force decreases,and the maximum contraction power is temporarily reduced.Muscle fatigue is an important indicator to measure the state of the muscles.If the muscles are in a state of fatigue,if they do not rest in time,they may cause severe muscle strain,which will have a serious impact on people's normal exercise or rehabilitation.The research and design of a system that can analyze and monitor the muscle fatigue status,provide reliable and effective guidance for daily exercise and patient rehabilitation,and have important value for basic medical research,clinical analysis,sports science and patient rehabilitation.At present,the indirect methods for measuring muscle fatigue state include electromyography,mechanomyography,electroencephalography,and ultrasound.Different from these major measurement techniques,this paper adopts multi-channel dielectric constant measurement technology to monitor muscle fatigue state.The method does not cause any trauma to the test body during the measurement of the muscle,and the applied sensor module is small in size,has strong anti-interference ability and the like.The main work of this thesis is as follows:(1)Design of sensor.In order to measure the dielectric constant of muscle tissue,a capacitor plate array with eight capacitor plates is designed.Under the control of the microprocessor,different capacitor plates are selected and passed through various capacitor plates.Combining the formation of spatially distinct non-parallel plate capacitors allows for more detailed and comprehensive information on the change in dielectric constant of muscle tissue;(2)Design of the signal acquisition module.A total of 52 capacitors of different shapes and sizes are formed through different combinations of non-plate capacitor arrays.These capacitors are used to measure the dielectric constant of the local muscle under different depths and areas.By using analog hardware circuit to amplify weak muscle dielectric constant signal to remove other useless interference signals,the data of 52 kinds of capacitors collected from stable muscle dielectric constant signal are sent to the data processing module.In the entire acquisition module,the STM32F334 processor is used to control the acquisition process.(3)The design and implementation of data processing algorithm.In this paper,a BP artificial neural network is designed which can process the data of 52 dimension muscle dielectric constant signal,and this network can overcome all kinds of adverse effects brought by uncertain factors such as different individuals,different positions,changes in human body state and environment,and effectively convert the collected muscle sensing data into reliable muscle fatigue data.This part of the algorithm is implemented by STM32F767 processor and related peripheral circuits(4)This paper designs the drive circuit,test circuit,MCU circuit,data storage circuit and various interface circuits for communication.(5)This paper has prepared relevant measurement and control software,which mainly includes:transplantation of UC/OS-? embedded operating system,use of FATFS file operating system,neural network algorithm programming,NAND FLASH storage,programming of USB communication interface and computer communication,and development of LCD touch screen to complete human-computer interaction interface.(6)System performance test.Based on the actual testing process of human muscle,the software and hardware performance of the system is tested and verified.In the experiment,multiple groups of dumbbells were tested before,during and after the exercise of squat.The results show that the designed system can effectively detect the muscle fatigue,and the output of the system is stable and has strong anti-interference performance.