优化机动机动任务集用于训练生物关节时刻估计器
概括
优化外骨的控制需要准确的关节时刻估计. 本研究介绍了一种任务集优化策略,以减少对深度学习模型的数据收集需求,保持准确性,同时降低成本.
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:
- 来自可穿戴传感器的精确生物关节运动时刻估计对于现实世界运动中的高级外骨控制至关重要.
- 目前的深度学习方法需要大量的实验室内数据,由于数据采集的挑战,阻碍了稳健的模型开发.
研究的目的:
- 开发一个运动机动任务集优化策略,以最大限度地减少用于基于可穿戴传感器的联合时刻估计的数据收集.
- 确定最小的,代表性的任务集,以保持神经网络性能,用于外骨控制应用程序.
主要方法:
- 从各种循环和非循环运动任务中对尺寸缩小的生物力学特征进行集群分析.
- 确定了最小可行的任务集群,以训练一个神经网络来估计关节的时刻.
- 使用跨主体交叉验证评估模型性能.
主要成果:
- 基于任务集的优化模型实现了0.29 ± 0.06 Nm/kg的根平均平方误差,用于关节运动时刻估计.
- 性能明显优于仅使用循环任务 (p<0.05) 和与使用整个任务集相比.
- 在不影响准确性的情况下,显著降低了数据收集和模型培训成本.
结论:
- 提出的任务集优化策略有效地减少了对外骨架控制中的深度学习模型的数据需求.
- 这种方法可以保持高模型准确性,同时显著降低数据收集和培训的负担.
- 未来的外骨设计师可以利用这种策略来最大限度地减少对基于深度学习的可穿戴机器人控制的数据需求.
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