Abstract
To realize gras** force control in surface electromyography (sEMG) based prosthesis, this paper investigated the information of motion intention in sEMG signals. An eight-channel Myo armband was used to collect the sEMG signals of nine subjects. A series of grab weights were set up in the experiments. The raw signals were pre-processed by low-pass filtering using 4th Butterworth filter, data normalization, and short-time Fourier transform extracting the muscle activation region. Three classifiers, including k-nearest neighbour, decision tree, and multi-layer perceptron model were trained. The k-nearest neighbour model achieved high robustness and prediction accuracy up to 99%. It can be concluded that by decomposing the motion intention of sEMG signals, the gras** force can be accurately predicted in advance, which can significantly benefit real-time human-prosthetic interaction.
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Wang, Y., Zhang, Z., Su, Z., Qian, J. (2021). Human Gras** Force Prediction Based on Surface Electromyography Signals. In: Fei, M., Chen, L., Ma, S., Li, X. (eds) Intelligent Life System Modelling, Image Processing and Analysis. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1467. Springer, Singapore. https://doi.org/10.1007/978-981-16-7207-1_25
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DOI: https://doi.org/10.1007/978-981-16-7207-1_25
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