Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.
- long-term myoelectric signal
- pattern recognition
- prosthesis control
- user adaptation
ASJC Scopus subject areas
- Biomedical Engineering
- Cellular and Molecular Neuroscience
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- Department of Electronic & Electrical Engineering - Reader in Robotics Engineering
- Centre for Biosensors, Bioelectronics and Biodevices (C3Bio)
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Autonomous Robotics (CENTAUR)
Person: Research & Teaching