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Title
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Enhanced Performance for Multi-Forearm Movement Decoding Using Hybrid IMU–sEMG Interface
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Sub-Title |
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Subject |
Surface electromyography, pattern recognition, inertial measurement units, support vector machine, linear discriminant analysis
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Author |
W. Shahzad, Y. Ayaz, M. J. Khan, N. Naseer, and M.
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Publish Year |
2019 |
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Supervisor |
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Diss#. |
https://doi.org/10.3389/fnbot.2019.00043 |
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Text Language |
English |
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Library Section |
Research Article |
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Abstract |
Control of active prosthetic hands using surface electromyography (sEMG) signals is an active research area; despite the advances in sEMG pattern recognition and classification techniques, none of the commercially available prosthetic hands provide the user with an intuitive control. One of the major reasons for this disparity between academia and industry is the variation of sEMG signals in a dynamic environment as opposed to the controlled laboratory conditions. This research investigated the effects of sEMG signal variation on the performance of a hand motion classifier due to arm position variation and also explored the effect of static position and dynamic movement strategies for classifier training. A wearable system is used to measure the electrical activity of the muscles and the position of the forearm while performing six classes of hand motions. The system is made position aware (POS) using inertial measurement units (IMUs) for different arm movement gestures. The hand gestu
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