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| An Algorithm for Detecting the Onset of Muscle Contraction Based on Generalized Likelihood Ratio Test |
1 Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2 School of Public Hhealth, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China |
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Abstract The surface electromyography (sEMG) of stump in the amputee is often applied to control the action of myoelectric prosthesis. According to the sEMG signals with low Signal to Noise Ratio (SNR) recorded from the stump muscle, a generalized likelihood ratio (GLR) method was proposed to detect the onset of muscle contraction, where a decision threshold was related with the SNR of sEMG signals, an off-line simulation method was used to determine the relationship between them. For the simulated sEMG signals with a given SNR, the different thresholds were tested, the optimal threshold could be obtained when the detection accuracy was optimized. As a result, the fitted curve was achieved to describe the relationship of the SNR and the decision threshold. Then, the sEMG signals are analyzed on-line by the GLR test for the onset detection of muscle contractions, while the decision threshold corresponding with the SNR was chosen based on the fitted curve. Compared with the classical algorithms, with the simulated sEMG traces, the error mean and standard deviation for estimating the muscle contraction onset were reduced at least 35% and 43% respectively; based on the real EMG signals, the error mean and standard deviation of the onset estimate were separately not less than 29% and 23%. Therefore, the proposed algorithm based on GLR test for the onset detection of muscle contraction was more accurate than other methods, while the SNR of sEMG signals was low.
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