Bioelectrical Signal Analysis And Its Application In Fatigue Detection Of Motion

Bioelectricity refers to a regular electrical phenomenon that is closely related to the state of life,whether it is in a stationary state or an active state,in a living cell or tissue(human body,animal tissue).Bioelectrical signals include resting potentials and action potentials,which are essentially the transmembrane flow of ions.Electromyography,EEG,ECG,and retinal electricity are clinically common bioelectrical signals.These surface bioelectrical signals can be collected by electrodes,amplified by amplifiers,and finally recorded as electromyography,electroencephalogram,electrocardiogram,etc.,to assist scientific and medical diagnosis for relevant professionals.The main research content of this thesis is the analysis of bioelectrical signals based on EMG signals and EEG signals.The specific research work includes the following two parts:First,muscle fatigue is a common physiological phenomenon in human exercise.During the process of repeating a certain action to make the muscles gradually fatigue,the myoelectric signal will change to some extent.In order to explore the changes of the myoelectric signal during the fatigue process of the muscle In this paper,the application of non-negative matrix factorization in the model of muscle synergy and the changes of surface and frequency domain characteristics of surface EMG signals during exercise to fatigue are studied.(1)Muscle coordination is considered to be the minimum controlled unit of the central nervous system for motion control.By decomposing the muscle coordination model,the participation of each muscle during the execution of the exercise can be analyzed.The model decomposition process is to decompose a large matrix into a smaller matrix product.The matrix obtained by the common matrix decomposition method may contain a negative matrix,which is difficult to explain reasonably in the biological domain,rather than negative matrix decomposition.The other common matrix decomposition method has the advantage that the matrix obtained after decomposition has non-negative properties.Therefore,according to the theory of muscle synergy,through the non-negative matrix decomposition of the EMG signal data,it is possible to understand the muscle synergy pattern and the number of muscle synergy during exercise,and then use the coherence and correlation to study the relationship between the muscles.Explore the use of muscle groups during the execution of certain movements to better analyze fatigue.(2)Exploring the changes in the time domain and frequency domain characteristics of the surface EMG signal during the movement to fatigue.The features used include the integral myoelectric value in the time domain,the rms amplitude and the average frequency in the frequency domain.The value frequency and the average instantaneous frequency index are used to analyze the variation of the features,and the effects of muscle fatigue on gesture recognition are analyzed through the designed motion fatigue experiment.Second,EEG signals are also an important human bioelectrical signal,which is widely used in clinical diagnosis and scientific research.The hybrid brain-computer interface may be a brain-computer interface control system formed by fusing an EEG signal with another bioelectrical signal such as ocular electricity,electrocardiogram,or myoelectricity;or may be an EEG signal with another A brain-computer interface control system formed by modal EEG signal fusion(such as motion imaging and P300).This paper preliminarily discusses the advantages of the hybrid brain-computer interface system based on EEG signals and surface EMG signals in the recognition of motion recognition based on the traditional surface-based EMG-based gesture recognition system(high recognition accuracy and ability to cope with muscle fatigue).Strong),and designed the model structure block diagram.