Abstract
The estimation of joint angle finds wide application in fields such as robotics, prosthetics, ergonomics, etc. Various estimation techniques have been discussed in the literature. The estimation of joint angle using Surface Electromyogram (sEMG) signals has gained importance due to its ability to recognize continuous human motion. This requires feature extraction from the acquired sEMG signals in order to develop an estimation model. In this paper, an attempt has been made to extract a few significant time domain features from sEMG signals acquired from the biceps brachii for four different subjects. The feature signals obtained using sliding window technique is further used to develop an estimation model using a suitable training algorithm. The trained model is eventually used to control the elbow joint angle.
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Acknowledgements
The technical support and laboratory facilities provided by the parent institution for completing this project are gratefully acknowledged. All the staffs who guided to the best of their knowledge are gratefully acknowledged. The authors of all the journals listed in the reference section are also acknowledged gratefully.
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Rajalakshmy, P., Jacob, E., Joclyn Sharon, T. (2019). Estimation of Elbow Joint Angle from Surface Electromyogram Signals Using ANFIS. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_25
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DOI: https://doi.org/10.1007/978-3-030-04061-1_25
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