通过EMG驱动的神经肌肉骨模型和LSTM网络估计联合扭矩
概括
使用可穿戴传感器预测关节扭矩是外骨控制的关键. 神经肌肉骨 (NMS) 和长期短期记忆 (LSTM) 模型准确地估计了扭矩,聚类为NMS模型提供了轻微的改进.
科学领域:
- 生物力学 生物力学
- 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:
- 准确的关节扭矩预测对于开发有效的按需辅助外骨控制器至关重要.
- 可穿戴传感器,电肌图 (EMG) 和动力学是估计人类运动动态的关键输入.
研究的目的:
- 在日常活动中使用神经肌肉骨 (NMS) 和长期短期记忆 (LSTM) 模型估计关节扭矩 (脚,膝盖,部).
- 评估聚类方法对扭矩估计模型准确性的影响.
- 为了比较聚合,个人和集群NMS和LSTM模型的性能.
主要方法:
- 使用电肌图 (EMG) 和动力学数据来估计关节扭矩.
- 使用神经肌肉骨 (NMS) 模型和长期短期记忆 (LSTM) 网络进行扭矩预测.
- 采用聚类方法,按相似性分组运动,并评估其对模型准确性的影响.
- 从动作捕捉数据中通过反向动力学获得地面真实联合扭矩.
主要成果:
- 无论是NMS和LSTM模型都表现出高关节扭矩估计的准确性.
- 集群和个人NMS模型的表现同样好,优于通用聚合模型.
- 集群方法对LSTM模型已经高的准确性产生了最小的影响.
- 单个,集群和聚合的LSTM模型都实现了相对较高的估计准确性.
结论:
- 神经肌肉骨和LSTM模型对于从可穿戴传感器数据中估计关节扭矩是有效的.
- 聚类可以提高特定运动模式的NMS模型准确性,尽管它对LSTM的好处有限.
- 结果为选择合适的模型提供了指导方针,以改善外骨控制器的设计和功能,以进行康复.
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