Abstract
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.
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Notes
- 1.
Thalmic Labs Inc., www.thalmic.com.
- 2.
Measurand Inc., www.shapehand.com.
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Acknowledgement
The authors of this work would like to acknowledge Thalmic Labs Inc. for providing the Myo armband and the data collection codebase. The authors would also like to acknowledge Dr. Pedram Ataee and the Machine Learning team at Thalmic Labs Inc. for their assistance and insights.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lin, J.FS., Samadani, AA., Kulić, D. (2016). Segmentation by Data Point Classification Applied to Forearm Surface EMG. In: Leon-Garcia, A., et al. Smart City 360°. SmartCity 360 SmartCity 360 2016 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 166. Springer, Cham. https://doi.org/10.1007/978-3-319-33681-7_13
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