Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers

Research Article

Segmentation by Data Point Classification Applied to Forearm Surface EMG

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  • @INPROCEEDINGS{10.1007/978-3-319-33681-7_13,
        author={Jonathan Lin and Ali-Akbar Samadani and Dana Kulić},
        title={Segmentation by Data Point Classification Applied to Forearm Surface EMG},
        proceedings={Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers},
        proceedings_a={SMARTCITY360},
        year={2016},
        month={6},
        keywords={Motion segmentation Surface electromyography Classifiers Pattern recognition},
        doi={10.1007/978-3-319-33681-7_13}
    }
    
  • Jonathan Lin
    Ali-Akbar Samadani
    Dana Kulić
    Year: 2016
    Segmentation by Data Point Classification Applied to Forearm Surface EMG
    SMARTCITY360
    Springer
    DOI: 10.1007/978-3-319-33681-7_13
Jonathan Lin1,*, Ali-Akbar Samadani2,*, Dana Kulić1,*
  • 1: University of Waterloo
  • 2: University of Toronto
*Contact email: jf2lin@uwaterloo.ca, ali.samadani@utoronto.ca, dkulic@uwaterloo.ca

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.