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Study on Detecting and Processing Techniques for Dual-Phase Motion Fatigue Information Induced by Neuromuscular Electrical Stimulation

Author: ZhangXi
Tutor: MingDong
School: Tianjin University
Course: Biomedical Engineering
Keywords: Neuromuscular electrical stimulation Muscle fatigue Surface EMG Spectrum analysis AR model Wavelet Transform
CLC: R87
Type: Master's thesis
Year: 2010
Downloads: 43
Quote: 1
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Abstract


Currently , the limit of neuromuscular electrical stimulation (Neuromuscular electrical stimulation, NMES) widely used in one of the main factors is by its induced muscle fatigue. As a physiological phenomenon inevitable rehabilitation , rehabilitation of muscle fatigue will directly affect the stimulation effect . Through research on how to detect and evaluate the degree of muscle fatigue , further to overcome the negative impact of muscle fatigue , has important significance for the neuromuscular system of cognitive the disability rehabilitation engineering and clinical physical therapy evaluation . Able to muscle the various activities as surface electromyography (Surface electromyography, sEMG) record , so sEMG characteristic parameters can be used for the characterization of NMES induced muscle fatigue parameters. The study design NMES induced stationary phase and mobile phase under the conditions of the two movements of the lower extremity muscle fatigue test platform and the experimental program . Acquisition sEMG by two points peak threshold detection algorithms filter out NMES interference EMD filter out low frequency drift , adaptive notch filter to reduce the pre-processing of 50Hz and its harmonics interference , by detecting changes in knee angle NMES induced static phase fatigue information and the mobile phase , while the extraction of frequency domain features sEMG static phase movement , median frequency , average frequency and AR model indicators ; surface electromyography continuous wavelet transform analysis of the state of the mobile phase movement : the wavelet the coefficients divided into the high frequency band and low frequency band in two parts , to estimate the amplitude of the EMG signals of the respective bands , to the rms value for the analysis of the trend of these parameters in a fatigue process . The research results show that the NMES induced static phase movement muscle fatigue process conditions , the median frequency , average frequency and AR model indicators can be used as the amount of fatigue reliability parameters ; NMES induced fatigue EMG wavelet analysis of the dynamic phase movement the amplitude of the low-frequency band of the wavelet coefficients to characterize the degree of muscle fatigue . This article by NMES induced static , moving the two movements of the conditions of lower extremity muscle fatigue information detection experiments were knee angle detection and surface EMG spectral analysis , evaluation the NMES induce muscle fatigue signal characteristics , in particular respectively estimated by the wavelet coefficients , the amplitude of the low-frequency band of muscle strength and muscle fatigue induced muscle fatigue accurate evaluation and feedback to find a feasible method for the the NMES to use the process .

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