基于sEMG的端到端继续预测人类膝关节的角度,使用紧紧合的卷积变压器模型
IEEE journal of biomedical and health informatics
|August 11, 2023
概括
这项研究引入了一种新方法,使用紧密合卷积变压器 (TCCT) 模型从表面电肌图 (sEMG) 信号预测膝关节角度,从而实现外骨架机器人的实时控制.
科学领域:
- 生物医学工程 生物医学工程
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 可穿戴的外骨架机器人有助于身体康复,但面临着人机交互挑战.
- 传统的表面电肌图 (sEMG) 功能提取是复杂的,手动的,并且阻碍了实时性能.
研究的目的:
- 开发一种端到端的方法,使用sEMG信号预测人类膝关节的关节角度.
- 为了提高外骨架机器人的实时人机交互能力.
主要方法:
- 从5名健康受试者收集了sEMG信号.
- 从消除噪音的sEMG信号中提取信封作为模型输入.
- 开发并应用了紧合卷积变压器 (TCCT) 模型,用于在100毫秒内预测膝盖角度.
主要成果:
- 该TCCT模型实现了高精度,平均RMSE为3.79°,调整R2为0.96和CC为0.98.
- 预测性能超过了Informer,CNN和CNN-BiLSTM等传统模型.
- 该模型的预测时间为11.67±0.67毫秒,可确保实际实时应用.
结论:
- 拟议的TCCT模型准确且快速地从sEMG信号中预测人类膝关节的关节角度.
- 这一进步满足了在外骨架应用中连续,实时的膝关节节角预测的要求.
- 这些发现显著增强了人机交互,用于康复机器人.
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