通过时间感知知识蒸进行地面反应力估计
Eun Som Jeon1, Sinjini Mitra2, Jisoo Lee2
1Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
IEEE internet of things journal
|September 8, 2025
概括
我们开发了一个时间感知知识蒸框架,使用可穿戴内传感器准确估计地面反应力 (GRF). 这种高效的方法改善了用于便携式医疗保健应用程序的人类步行分析.
科学领域:
- 生物力学 生物力学
- 可穿戴技术可穿戴技术
- 机器学习 机器学习
背景情况:
- 人类步态分析对于医疗保健,康复和诊断至关重要.
- 仪表式跑步机是准确的,但昂贵的,不便携的,用于测量地面反应力 (GRF).
- 可穿戴的内传感器提供了便携性,但也面临着准确性和噪声方面的挑战,而深度学习解决方案则资源密集.
研究的目的:
- 提出一种高效和准确的方法,用可穿戴内传感器数据来估计GRF.
- 为了解决当前GRF测量技术的局限性,用于便携式步态分析.
- 开发一个适合实时应用的低成本的深度学习框架.
主要方法:
- 开发了一个时间意识的知识蒸框架,以利用时间和特征的相似性.
- 使用轻量级的"学生"模型,通过从更复杂的"老师"模型中蒸训练.
- 对各种步行速度和模型架构的仪器化跑步机GRF测量进行了性能评估.
主要成果:
- 拟议的时间意识知识蒸框架在GRF估计准确性方面显著超过了现有的基线.
- 蒸的轻量级模型实现了与更复杂的方法相比的卓越准确性和可靠性.
- 该框架显示,所需的培训参数大幅减少,提高了效率.
结论:
- 时间意识的知识蒸提供了一个数据和资源高效的解决方案,用于从可穿戴传感器准确估计GRF.
- 这种方法可以在便携式和多样化的环境中实现实用,实时的人类步行分析.
- 该框架提高了可穿戴传感器技术的可行性,用于广泛的医疗保健和康复应用.
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