Abstract
To reduce the bad effect of electrode shifts on myoelectric pattern recognition, this paper presents an adaptive electrode calibration method based on core activation regions of muscles. In the proposed method, the high-density surface electromyography (HD-sEMG) matrix collected during hand gesture execution is decomposed into source signal matrix and mixed coefficient matrix by fast independent component analysis algorithm firstly. The mixed coefficient vector whose source signal has the largest two-norm energy is selected as the major pattern, and core activation region of muscles is extracted by traversing the major pattern periodically using a sliding window. The electrode calibration is realized by aligning the core activation regions in unsupervised way. Gestural HD-sEMG data collection experiments with known and unknown electrode shifts are carried out on 9 gestures and 11 participants. A CNN+LSTM-based network is constructed and two network training strategies are adopted for the recognition task. The experimental results demonstrate the effectiveness of the proposed method in mitigating the bad effect of electrode shifts on gesture recognition accuracy and the potentials in reducing user training burden of myoelectric control systems. With the proposed electrode calibration method, the overall gesture recognition accuracies increase about (5.72~7.69)%. In specific, the average recognition accuracy increases (13.32~17.30)% when using only one batch of data in data diversity strategy, and increases (12.01~13.75)% when using only one repetition of each gesture in model update strategy. The proposed electrode calibration algorithm can be extended and applied to improve the robustness of myoelectric control system.
Highlights
M YOELECTRIC control is a technique that interprets physical movements or intention as machine command by surface Electromyography signals, which has Manuscript received July 19, 2020; revised September 3, 2020; accepted September 13, 2020
In a previous work on high-density surface electromyography (HD-surface Electromyography (sEMG))-based muscle force estimation, we reported that the location of the core activation region of muscles can be achieved by applying matrix decomposition algorithms such as nonnegative matrix factorization (NMF) [21] and independent component analysis (ICA) [22] etc. on HD-sEMG signals
With the assumption that the core activation region of muscle is relatively stationary and consistent for the same gesture, the bad effect of electrode shifts on the performance of myoelectric pattern recognition is expected to be solved by aligning the core activation regions extracted from different repetitions
Summary
M YOELECTRIC control is a technique that interprets physical movements or intention as machine command by surface Electromyography (sEMG) signals, which has Manuscript received July 19, 2020; revised September 3, 2020; accepted September 13, 2020. In the works of [7] and [8], in order to train classifier with strong generalization, various electrode shifts were made manually during the gesture data collection experiment to ensure that diverse muscle activation states can be captured and learned. With the assumption that the core activation region of muscle is relatively stationary and consistent for the same gesture, the bad effect of electrode shifts on the performance of myoelectric pattern recognition is expected to be solved by aligning the core activation regions extracted from different repetitions. Taking advantages of HD-sEMG technology, we propose an adaptive electrode calibration method based on core activation region of muscle for the realization of robust gesture recognition in this paper. The feasibility and effectiveness of the proposed electrode calibration method in mitigating the bad effect of electrode shifts on gesture recognition accuracy and the potentials in reducing the user training burden of myoelectric control systems are verified through two types of hand gesture recognition experiments
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