Analyzing the trade-off between training session time and performance in myoelectric hand gesture recognition during upper limb movement

Cognolato, Matteo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis); Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich) ; Brigato, Lorenzo (Department of Computer, Control and Management Engineering, Sapienza University of Rome) ; Dicente Cid, Yashin (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Atzori, Manfredo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis); University of Geneva, Switzerland)

Although remarkable improvements have been made, the natural control of hand prostheses in everyday life is still challenging. Changes in limb position can considerably affect the robustness of pattern recognition-based myoelectric control systems, even if various strategies were proposed to mitigate this effect. In this paper, we investigate the possibility of selecting a set of training movements that is robust to limb position change, performing a trade-off between training time and accuracy. Four able-bodied subjects were recorded while following a training protocol for myoelectric hand prostheses control. The protocol is composed of 210 combinations of arm positions, forearm orientations, wrist orientations and hand grasps. To the best of our knowledge, it is among the most complete including changes in limb positions. A training reduction paradigm was used to select subsets of training movements from a group of subjects that were tested on the left-out subject’s data. The results show that a reduced training set (30 to 50 movements) allows a substantial reduction of the training time while maintaining reasonable performance, and that the trade-off between performance and training time appears to depend on the chosen classifier. Although further improvements can be made, the results show that properly selected training sets can be a viable strategy to reduce the training time while maximizing the performance of the classifier against variations in limb position.

Conference Type:
full paper
Economie et Services
Institut Informatique de gestion
Toronto, Canada, 24-28 June 2019
Toronto, Canada
24-28 June 2019
6 p.
Published in:
Proceedings of the 16th International Conference on Rehabilitation Robotics (ICORR) IEEE 2019
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 Record created 2019-10-16, last modified 2021-02-16

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