基于对外骨在不同地形上的转移学习的模式过渡识别
Yifan Gao1, Jianbin Zheng1, Yang Gao1
1School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei, China.
PeerJ. Computer science
|September 24, 2025
概括
这项研究引入了一种新的转移学习方法,用于在可穿戴机器人中检测人类运动意图. 该方法准确地预测了移动模式的转换,使外骨在不同地形上更顺地导航.
科学领域:
- 机器人和人机交互的人机交互
- 机器学习和模式识别.
背景情况:
- 人类运动意图检测对于先进的可穿戴机器人至关重要.
- 识别运动模式 (如步行,爬楼梯) 之间的过渡是具有挑战性的,特别是在不同的物理负载和地形下.
研究的目的:
- 开发和评估一种新的转移学习方法,用于准确和早期识别移动模式模式转换.
- 提高可穿戴机器人在适应不同地形和物理条件方面的性能.
主要方法:
- 一种结合时间卷积网络 (TCN) 与空间注意力 (SA) 的转移学习方法被开发用于模式过渡识别.
- 该方法在三重物理负载下在五种动态模式中识别八种运动模式之间的过渡时进行了测试.
- 与ResNet和LSTM等其他模型相比,基于准确性和预测时间 (Pre-T) 评估了性能.
主要成果:
- 在TCN-空间注意力 (TCN-SA) 转移学习方法实现了高精度,达到高达98.21%.
- 实现了对下一个移动模式的早期预测,预测时间在实际过渡之前从120-600 ms不等.
- 步行周期 (Pre-T/GC) 中预测时间的比例显著降低,表明有效的预测.
结论:
- 提议的TCN-SA转移学习方法有效地检测出运动模式的转换,从而能够更早地进行预测.
- 这种早期检测允许可穿戴机器人,如外骨,在相邻的地形之间更平稳,更有效地导航.
- 该方法在预测时间和准确性方面表现出比现有方法更好的性能.
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