转动一个旋:基于深度学习的扭矩和臂角的预测,使用强力肌肉学
Ramandeep Singh1, Parikshith Chavakula1, Joy Chatterjee1
1Neuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, 110029, India.
Physical and engineering sciences in medicine
|December 15, 2025
概括
强力肌图 (FMG) 准确地预测旋转动,包括扭矩和臂角,准确度超过95%. 这项技术对人机交互和日常生活活动具有前景.
科学领域:
- 人与计算机的互动.
- 生物力学 生物力学
- 信号处理 信号处理
背景情况:
- 准确预测人类运动动作对于直观的人机交互至关重要.
- 强力肌图 (FMG) 是一种用于非侵入性肌肉活动测量的新兴技术.
- 了解日常任务中的四肢动力学是适应性系统发展的关键.
研究的目的:
- 调查FMG在预测旋转动活动中的有效性.
- 评估不同扭矩和臂角对预测准确性的影响.
- 评估皮下脂肪对基于 FMG 的预测的影响.
主要方法:
- 参与者执行了旋转动任务,扭矩和臂角各不相同.
- 在这些活动期间收集了强迫肌肉图 (FMG) 数据.
- 使用混合深度学习模型 (CNN-LSTM) 来进行数据分类和预测.
主要成果:
- 基于FMG的预测模型实现了对扭矩 (95.87 ± 2.59%) 和臂角 (94.06 ± 2.44%) 的高精度.
- 皮下脂肪的存在并没有显著影响扭矩和臂角的预测准确性.
- 该研究表明,在不同的任务参数中表现强.
结论:
- 强迫肌肉学是一种可行且准确的方法,用于预测复杂的运动行为,如旋转动.
- 开发的深度学习方法显示了实时人机交互的巨大潜力.
- FMG技术为增强辅助技术和理解日常生活活动提供了一个有前途的途径.
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