Recent advances in wearable technologies have led to the development of new modalities for human-machine interaction such as gesture-based interaction via surface electromyograph (EMG). An important challenge when performing EMG gesture recognition is to temporally segment the individual gestures from continuously recorded time-series data. This paper proposes an approach for EMG data segmentation, by formulating the segmentation problem as a classification task, where a classifier is used to label each data point as either a segment point or a non-segment point. The proposed EMG segmentation approach is used to recognize 9 hand gestures from forearm EMG data of 10 participants and a balanced accuracy of 83% is achieved.
CITATION STYLE
Lin, J. F. S., Samadani, A. A., & Kulić, D. (2016). Segmentation by data point classification applied to forearm surface EMG. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 166, pp. 153–165). Springer Verlag. https://doi.org/10.1007/978-3-319-33681-7_13
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