Towards Active Muscle Pattern Analysis for Dynamic Hand Motions via sEMG
Surface Electromyographys (sEMG) as a widespread human-computer interaction method can reflect the activity of human muscles. When the human forearm finishes different hand motions, there will be strong sEMG signals in different regions of the skin surface. This paper investigates the mapping relationship between sEMG signal patterns and the dynamic hand motions. Four different hand motions are studied based on the extracted signal with mean absolute value (MAV) features and the shape-preserving piecewise cubic interpolation method. In the experiments, a 16-channel electrode sleeve is used to collect 9-subject EMG signals. According to the distribution of electrodes in the forearm, the forearm surface is divided into 8 different muscle regions. The preliminary experimental results show that different hand motions can cause different distribution of sEMG signals in different regions. It confirms that different subjects show similar patterns for the same motions. The experimental results can be applied as new sEMG features with a higher computational speed.
KeywordssEMG MAV Shape-preserving piecewise cubic interpolation Local maximum Muscle regions
The authors would like to acknowledge the support from the Natural Science Foundation of China under Grant No. 51575412, 51575338 and 51575407, the EU Seventh Framework Programme (FP7)-ICT under Grant No. 611391, the Grants of National Defense Pre-Research Foundation of Wuhan University of Science and Technology (GF201705) and the Research Project of State Key Lab of Digital Manufacturing Equipment & Technology of China under Grant No. DMETKF2017003. And this paper is funded by Wuhan University of Science and Technology graduate students’ short-term study abroad special funds.
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