聊天EMG:合成数据生成控制一个机器人手 Orthosis 的中风.
Jingxi Xu1, Runsheng Wang2, Siqi Shang1
1Department of Computer Science, Columbia University in the City of New York, NY, USA.
IEEE robotics and automation letters
|December 23, 2024
概括
聊天EMG生成合成的EMG信号,以改善中风患者对手臂形的意图识别. 这种方法减少了数据收集需求,并提高了功能控制的分类器准确性.
科学领域:
- 生物医学工程 生物医学工程
- 康复技术 康复技术 康复技术
- 机器学习 机器学习
背景情况:
- 对于中风患者手关节的意图推断受到数据收集挑战和电肌图 (EMG) 信号变异性的阻碍.
- 传统方法需要对新条件,会话或对象进行广泛的标记数据,证明耗时和繁.
研究的目的:
- 引入ChatEMG,这是一个自回归生成模型,用于创建合成EMG信号.
- 通过提示,实现特定上下文的EMG数据扩展,减少对新标记数据的需求.
- 为了提高中风幸存者的手关节器的意图推断分类器的概括性和准确性.
主要方法:
- 开发了ChatEMG,这是一个生成模型,可以合成基于提供提示 (EMG序列) 的EMG信号.
- 利用大数据集的生成培训,通过提示实现特定环境的生成.
- 评估了合成样本对培训意图推断分类器的有用性.
主要成果:
- 由ChatEMG生成的合成EMG样本是分类器不可知的.
- 合成数据的使用显著提高了各种分类器的意图推断准确性.
- 完整的方法被成功地集成到一个单一的病人会话,以功能性骨架控制.
结论:
- 聊天EMG有效地生成特定于环境的合成EMG数据,克服了传统数据收集的局限性.
- 这种方法提高了意图推断分类器的性能,使得中风幸存者能够更准确地控制手臂形.
- 这是首次部署在合成数据上训练有素的意图分类器,用于对中风幸存者的功能性骨架控制.
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