Abstract
The long-term electromyography (EMG) signal can make significant effect on prosthesis control based on pattern recognition. In this paper, we collected myoelectric signals lasting twelve days and compared the performance of six different features in time and frequency domains. They showed the same tendency and all led to degradation of recognition accuracy over time. Quantification method was used to measure changes in EMG feature space. It showed that distinctness of classes was increased after the long experiment and variability of patterns between days was larger than that within one day. The results of the study can help to investigate practical use of pattern recognition based prostheses.
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He, J., Zhang, D., Sheng, X., Zhu, X. (2013). Effects of Long-Term Myoelectric Signals on Pattern Recognition. In: Lee, J., Lee, M.C., Liu, H., Ryu, JH. (eds) Intelligent Robotics and Applications. ICIRA 2013. Lecture Notes in Computer Science(), vol 8102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40852-6_40
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DOI: https://doi.org/10.1007/978-3-642-40852-6_40
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