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Title: Muscle Force Estimation and Fatigue Detection Based on sEMG Signals
Keywords: surface electromyography, muscle force, muscle fatigue, continuous wavelet transform, lower extremities rehabilitation robotics, feature extraction
Issue Date: 20-Aug-2013
Citation: BAI FENGJUN (2013-08-20). Muscle Force Estimation and Fatigue Detection Based on sEMG Signals. ScholarBank@NUS Repository.
Abstract: Three relationships, surface electromyography (sEMG) amplitude-force relationship, mean frequency (MF) and force relationship, and the relationship between frequency parameters and signal energy distribution (MF-power relationship) are first investigated. These relationships are nonlinear from both time-domain method and time-frequency analysis method with high regression correlation coefficients R2 values. Based on these relationships, muscle force estimation methods are proposed and evaluated using sEMG signals from healthy subjects and stroke patients. Another force prediction method is developed based on continuous wavelet transform (CWT) and artificial neural networks (ANN) for stroke patients especially. A novel muscle fatigue detection approach is explored using a time-frequency analysis method from sEMG signals during various muscle contraction conditions for both the healthy and stroke subjects. Quantified fatigue levels are obtained indicating the fatigue changes in muscles. These fatigue levels are quantified to the muscle maximal capacity based on linear regression and statistical analysis. These proposed methods are finally implemented and tested using the on-line sEMG signals in real-time.
Appears in Collections:Ph.D Theses (Open)

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