Abstract
The present paper proposes a method for estimating joint angular velocities from multi-channel surface electromyogram (sEMG) signals. This method uses a selective desensitization neural network (SDNN) as a function approximator that learns the relation between integrated sEMG signals and instantaneous joint angular velocities. A comparison experiment with a Kalman filter model shows that this method can estimate wrist angular velocities in real time with high accuracy, especially during rapid motion.
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Acknowledgment
This work was supported partly by JSPS KAKENHI grant numbers 22300079 and 24700593 and by Tateishi Science and Technology Foundation grant number 2157011.
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Horie, K., Suemitsu, A., Tanno, T., Morita, M. (2016). Direct Estimation of Wrist Joint Angular Velocities from Surface EMGs by Using an SDNN Function Approximator. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_4
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DOI: https://doi.org/10.1007/978-3-319-46681-1_4
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