一个时间效率高的连续坡道协议,用于数据驱动的跨多种速度行走能耗估计.
1Mechanical Engineering Department, KAIST, 291, Daehak-ro, 34141, Daejeon, Korea, Republic of.
Journal of neuroengineering and rehabilitation
|October 2, 2025
概括
一个连续的坡道协议有效地收集步行数据用于能源支出估计. 在这些数据上训练的深度学习模型与离散方法和商业设备相比,显示出更好的准确性.
科学领域:
- 生物力学和运动生理学
- 可穿戴技术和机器学习
背景情况:
- 使用可穿戴设备估计行走能源消耗对于数据驱动模型至关重要.
- 传统的离散步骤协议对于创建深度学习所需的多样化数据集是时间低效的.
- 连续协议为数据收集提供了一种节省时间的替代方案.
研究的目的:
- 为了比较连续坡道协议数据集与离散步骤协议数据集的有效性,以估计步行能源支出.
- 评估在两种协议数据上训练的深度学习模型的性能.
主要方法:
- 14名受试者用IMU在跑步机上行走,通过间接热量计测量能量消耗.
- 采用连续坡道协议 (1.0-1.75 m/s) 和离散步骤协议.
- 深度学习模型在两种协议的数据集上进行训练,并与商业智能手表进行比较.
主要成果:
- 在呼吸延迟补偿后,连续和离散协议之间没有发现能量消耗的显著差异.
- 在连续数据 (10.7%的误差) 上训练的深度学习模型的表现优于在离散数据 (13.1%的误差) 和智能手表上训练的模型.
- 一个具有连续数据的单一IMU能够在广泛的速度范围内实现低误差.
结论:
- 连续坡道协议是一种有效的时间效率方法,用于生成有效的行走能耗数据集.
- 来自连续协议的更丰富的数据多样性提高了深度学习模型的性能.
- 这种方法可以扩展到各种运动强度和运动类型,可能取代传统的间接热量计.
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