Abstract
For control of myoelectric prosthesis, research has mainly focused on sequential steady-state motions, e.g. flexion of the wrist at constant force. This may lead to less natural control of prostheses and consequently a more robot-like functionality and appearance. The aim of this work was to investigate pattern recognition for control of dynamic single and simultaneous motions.
Ten able-bodied subjects were included performing four single and four simultaneous dynamic motions with direct switches between motions. These motions could be classified with high accuracy (> 0.90).
This study was supported by a grant from the Danish Agency for Science, Technology and Innovation (grant number 10-080813) and the Danish Siemens Foundation.
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Rosenvang, J.C., Horup, R.W., Englehart, K., Jensen, W., Kamavuako, E.N. (2013). Classification of Simultaneous, Dynamic Motions with Surface EMG. In: Pons, J., Torricelli, D., Pajaro, M. (eds) Converging Clinical and Engineering Research on Neurorehabilitation. Biosystems & Biorobotics, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34546-3_8
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DOI: https://doi.org/10.1007/978-3-642-34546-3_8
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