Abstract
Surface electromyography (EMG) is widely used in hand gesture recognition for human-computer interface (HCI). This paper presents a finger gesture recognition scheme at two level of plane pressing force through the fusion of wrist EMG and accelerometers (ACC). The classification algorithm is evaluated on eight healthy subjects for identifying five finger gestures at two plane pressing force level. Experimental results show that frequency domain (improved discrete Fourier, iDFT) feature is better than time domain (TD) feature for wrist EMG classification. Moreover, it indicates that the fusion of EMG and ACC achieved improved recognition performance (85.77%) for finger gestures at two level of plane pressing force when compared to that obtained using EMG (80.65%) or ACC (56.86%) solely.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 51375296, 51620105002).
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Lv, B., Sheng, X., Guo, W., Zhu, X., Ding, H. (2017). Towards Finger Gestures and Force Recognition Based on Wrist Electromyography and Accelerometers. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_36
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DOI: https://doi.org/10.1007/978-3-319-65289-4_36
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