在手动骑行过程中基于动力学对外部负荷的预测
Griffin C Sipes1, Matthew Lee1, Kellie M Halloran1
1Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
机器学习准确地预测轮椅使用者的手自行车载荷. 这使得更安全的运动指南能够改善心血管健康并预防脊髓损伤患者的伤害.
科学领域:
- 生物力学 生物力学
- 康复工程 康复工程
- 机器学习 机器学习
背景情况:
- 脊髓损伤的个人面临心血管疾病风险增加.
- 手自行车为轮椅使用者提供了一种可能有益的运动.
- 评估外部负载对于安全的手动循环指南至关重要.
研究的目的:
- 为了预测手自行车行驶期间的六度自由度外部负荷.
- 为了利用惯性测量单元的数据进行负载预测.
- 为此目的开发机器学习模型.
主要方法:
- 将五个神经网络和两个集合模型与统计模型进行比较.
- 使用分段加速和速度作为输入数据.
- 在预测手动自行车载荷时评估模型性能.
主要成果:
- 一个时间卷积网络 (TCN) 显示出优异的预测准确性.
- 在平面内的力和时刻显示了最高的精度 (r = 0.95-0.97).
- 该TCN模型有效地预测了不同运动强度的负载.
结论:
- 机器学习,特别是TCN,可以准确地预测手动循环的外部负载.
- 可穿戴式传感器数据可以为开发安全有效的运动指南提供信息.
- 这种方法支持轮椅使用者和脊髓损伤者改善健康结果.
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