Abstract
The purpose of this research is to identify the force estimation model from an ElectroMyoGraphy (EMG) signal, and develop a power assist apparatus considering physical features. In this paper, the angle and the force estimation model based on the Nonlinear Auto-Regressive eXogenous (NARX) model is identfied from the EMG signal in volar/dorsal flexion of the wrist and the wrist angle or the force, and estimated the wrist angle or the force. In addition, the model that it matched in a human motion cycle is built by downsampling the EMG signal, the wrist angle and the force. Results of identifying the NARX model, the wrist angle and the force is estimated with high precision with even the different sampling time.
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Ohno, Y., Inoue, J., Iwase, M., Hatakeyama, S. (2018). Motion and Force Estimation Based on the NARX with an EMG Signal. In: Duy, V., Dao, T., Zelinka, I., Kim, S., Phuong, T. (eds) AETA 2017 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2017. Lecture Notes in Electrical Engineering, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-319-69814-4_29
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DOI: https://doi.org/10.1007/978-3-319-69814-4_29
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Online ISBN: 978-3-319-69814-4
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