使用基于传感器的深度学习扭矩预测和PID控制来增强上肢外骨
Farshad Shakeriaski1, Masoud Mohammadian1
1Faculty of Science and Technology, University of Canberra, Canberra 2617, Australia.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究介绍了使用电肌图 (EMG) 基于信号的扭矩估计和预测的上肢辅助外骨架的增强控制方法. 这种方法旨在通过优化外骨控制来改善中风幸存者的康复和独立性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 神经康复疗法 神经康复疗法
- 生物医学工程 生物医学工程
背景情况:
- 上肢辅助外骨架对于中风患者的康复至关重要.
- 对于中风幸存者来说,有效控制这些外骨仍然是一个重大挑战.
研究的目的:
- 提出一种新的方法来加强对上肢辅助外骨架的控制.
- 将估计和预测的扭矩集成到一个比例-整数-导数 (PID) 控制器循环中,以最大限度地减少系统的不确定性.
主要方法:
- 训练有素的深度神经网络模型 (LSTM,BLSTM,GRU) 使用健康受试者的高密度表面电肌图 (HD-sEMG) 信号.
- 开发了用于从EMG信号估计扭矩的模型,以及用于肘部外骨架机器人的预测扭矩.
- 使用估计和预测的扭矩作为PID控制循环和机器人动态的在线输入.
主要成果:
- 拟议的方法有效地估计和预测对上肢外骨控制的扭矩要求.
- 将估计和预测的扭矩集成到PID控制器循环中,证明了机器人的最佳控制.
- 该方法显示了改善康复结果和患者独立性的巨大潜力.
结论:
- 开发的扭矩估计和预测方法为上肢外骨控制提供了强大而创新的解决方案.
- 这一进步可以带来更大的独立性和改善中风幸存者的康复.
- 进一步的研究可以探索这种方法在各种临床环境中的应用.
相关概念视频
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