sEMG gait phase classification based on CNN-transformer and transfer learning
Qixin Guo, Yi Zheng, Chang Li, Feiyang Wang et autres
Accurate motion intention recognition is essential for active control of lower-limb rehabilitation exoskeletons. To address inter-subject variability in sEMG signals and real-time requirements, a lightweight CNN-Transformer hybrid with two-stage transfer learning is proposed. Inverted residual CNNs extract multi-dimensional local features while Transformer …
cn (code pays fourni par la source)
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Computer Methods in Biomechanics & Biomedical Engineering