整合了对比的跨模式注意力和堆叠的GRU,用于手功能康复机器人控制
Wei Liu1, Huidong Wu1, Shi-Fu Feng2
1Department of Prosthetic and Orthotic Engineering, School of Rehabilitation, Kunming Medical University, Kunming, China.
PloS one
|March 6, 2026
概括
本研究介绍了C-GAP模型用于手部康复机器人,通过整合电肌图 (EMG) 和其他传感器数据来提高控制精度. 该模型增强了各种手部功能状态的康复培训.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 人口老龄化和神经疾病增加增加了对先进手部康复机器人的需求.
- 传统的控制方法缺乏准确性和概括性,原因是难以捕获动态手动特征和模式间数据差异.
研究的目的:
- 为精确和智能控制手部康复机器人提出C-GAP模型.
- 为了提高控制的准确性和适应性,不同的患者手的功能状态.
主要方法:
- 开发了C-GAP模型,其中包括用于多源数据 (EMG,力,联合角度) 协作的跨模式注意力机制.
- 使用堆叠式门式循环单元来提取手部运动的时间动态 (抓住,捏,手腕旋转).
- 集成了一个可适应的PID控制器,用于优化强力控制的轨迹在康复训练.
主要成果:
- 在Ninapro DB5和MUSED-I数据集上,C-GAP模型在分类手动和动态控制方面表现稳定.
- 该模型有效地适应了不同手部功能状态 (健康人和中风患者) 的康复培训需求.
结论:
- 该C-GAP模型提供了技术支持,用于精确感知和控制手部康复机器人的连续运动.
- 这项研究提高了康复培训的特异性和安全性,促进了康复机器人中的循环神经网络.
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