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A Hybrid Fuzzy Temporal Transformer Method for Recognition of Finger Movements With Switching States Through sEMG Signals
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DOI：10.1109/tfuzz.2026.3695702.png)
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Abstract
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Myoelectric prosthetic hands are controlled by surface electromyographic signals from residual muscles in amputees. Recognizing continuous finger movements is crucial for improving dexterity when switching tasks. However, existing finger motion decoding algorithms are limited to recognizing discrete static gestures and are unable to cope with the continuous state switching of fine finger movements. To overcome the challenge, a hybrid fuzzy temporal transformer network is proposed. The network integrates temporal convolutional networks with transformers to extract temporal features of motor unit (MU) action potentials. It addresses the challenge of identifying waveform changes during gesture switching. An integrated fuzzy decision layer dynamically adjusts the recognition thresholds for static and switching states to suppress noise interference caused by muscle fatigue. By analyzing the impact of switching duration on waveform distortion, the network achieves duration detection for finger movement switching, enabling prediction of target gestures to enhance operational continuity and dexterity. The experiment involved 10 subjects switching between their thumb, index finger, and little finger for different durations. The results show that the correlation between finger switching recognition and gesture targets is 0.859, which is at least 0.026 above that of the baseline methods, and an F1-score at least 0.069 higher. The detection error for switching duration is reduced to a minimum of 80 ms. This work provides a feasible solution for controlling dexterous prostheses in temporal movement switching tasks. Through the collaborative design of hybrid architecture, it paves the way for novel solutions in the practical application of myoelectric prosthetic hand dexterous control.
Keywords:
Deep learning
finger movement recognition
fuzzy decision layer
motor unit (MU) action potential (MUAP)
surface electromyography
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