融合TCN-注意:一种因果关系维护的时间模型,用于单边基于IMU的步态预测和合作式外骨控制
Sichuang Yang1, Kang Yu1, Lei Zhang1
1Department of Mechatronics Engineering, College of Mechanical Engineering, Guangxi University, Nanning 530004, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
研究人员开发了FusionTCN-Attention,这是一个新的模型,使用惯性测量单元 (IMU) 数据预测身体一侧的四肢运动. 这一进步为半外骨控制提供了低延迟,稳定的预测.
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:
- 人类的步态显示一致的对侧合,健康的四肢运动预测受影响的四肢运动.
- 准确预测四肢动力学对于开发有效的辅助技术至关重要.
研究的目的:
- 开发一种因果关系维护的时间模型,FusionTCN-Attention,用于使用单边IMU数据预测逆侧部和膝盖轨迹.
- 建立一个方法论基础,用于单边预测在半外骨应用.
主要方法:
- 实施了FusionTCN-Attention,整合了扩张时间卷曲和轻量级的注意力机制.
- 确保了低延迟预测的严格实时因果关系.
- 在21名受试者的数据上对模型进行了评估.
主要成果:
- 在部 (5.71°) 和膝盖 (7.43°) 预测中实现了低平方根平均误差 (RMSE).
- 获得的相关系数超过0.9和相位延迟14.56 ms.
- 融合TCN-注意力超越了传统的序列模型,如Seq2Seq和因果变压器.
结论:
- 单边IMU传感能够稳定,低延迟地预测四肢动力学.
- 开发的模型为先进的外骨控制系统中单方面预测提供了基础.
- 这项研究是未来半外骨应用的先决条件.
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