使用过度参数化的深度神经网络,轻松准确地预测步行的联合接触力时间序列
Bernard X W Liew1, David Rügamer2,3, Qichang Mei4,5,6
1School of Sport, Rehabilitation, and Exercise Sciences, University of Essex, Colchester, United Kingdom.
Frontiers in bioengineering and biotechnology
|July 19, 2023
概括
深度神经网络可以仅使用联合角度来预测联合接触力 (JCF). 这一突破允许在现实环境中持续,非侵入性地监测组织负荷.
科学领域:
- 生物力学 生物力学
- 计算建模计算建模
- 肌肉骨健康 肌肉骨健康
背景情况:
- 关节接触力 (JCF) 在肌肉骨和骨科疼痛疾病中至关重要.
- 目前的非侵入性JCF评估方法仅限于实验室环境.
- 估计体内JCF对于理解疼痛机制至关重要.
研究的目的:
- 开发一种使用深度神经网络预测JCF的非侵入性方法.
- 用关节角度作为JCF估计的唯一预测指标.
- 为了在自由生活的环境中实现持续的JCF监测.
主要方法:
- 深度神经网络被训练来预测JCF.
- 联合角度被用作输入预测器.
- 对模型的性能进行了评估,并根据公布的最小可检测变化值进行了评估.
主要成果:
- 神经网络模型准确地预测了JCF,在最小可检测变化范围内存在错误.
- 预测错误范围从0.03体重 (BW) 关节JCF在走路期间到0.65BW膝盖JCF在跑步期间.
- 延长训练时代 (>100) 提高了波形预测的准确性和流性.
结论:
- 仅使用关节动力学来预测JCF是一种有前途的方法.
- 这种方法为临床医生和教练提供了持续的组织负载监测.
- 这些发现支持了在肌肉骨健康管理中实际应用的潜力.
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