Zhang Congyi, Zhou Dalin, Fang Yinfeng, Kubota Naoyuki, Ju Zhaojie
School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK.
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310005, China.
Biomimetics (Basel). 2025 Apr 7;10(4):229. doi: 10.3390/biomimetics10040229.
Surface electromyography (sEMG) non-invasively captures the electrical activity generated by muscle contractions, offering valuable insights into motion intentions. While sEMG has been widely applied to general gesture recognition in rehabilitation, there has been limited exploration of specific, intricate daily tasks, such as the pouring action. Pouring is a common yet complex movement requiring precise muscle coordination and control, making it an ideal focus for rehabilitation studies. This research proposes a granular computing-based deep learning approach utilizing ConvMixer architecture enhanced with feature fusion and granular computing to improve gesture recognition accuracy. Our findings indicate that the addition of hand-crafted features significantly improves model performance; specifically, the ConvMixer model's accuracy improved from 0.9512 to 0.9929. These results highlight the potential of our approach in rehabilitation technologies and assistive systems for restoring motor functions in daily activities.
表面肌电图（sEMG）以非侵入方式捕捉肌肉收缩产生的电活动，为运动意图提供有价值的见解。虽然sEMG已广泛应用于康复中的一般手势识别，但对特定的、复杂的日常任务，如倒水动作的探索却很有限。倒水是一个常见但复杂的动作，需要精确的肌肉协调和控制，使其成为康复研究的理想重点。本研究提出了一种基于粒计算的深度学习方法，利用通过特征融合和粒计算增强的ConvMixer架构来提高手势识别准确率。我们的研究结果表明，添加手工特征显著提高了模型性能；具体而言，ConvMixer模型的准确率从0.9512提高到了0.9929。这些结果凸显了我们的方法在康复技术和辅助系统中恢复日常活动中运动功能的潜力。
