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惯性Mov:基于惯性传感器的机器学习测试,用于预测腰部疼痛患者的运动障碍.

Jeremy Carlosama1, Luis Zhinin-Vera2, Cesar Guevara3

  • 1School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador.

Sensors (Basel, Switzerland)
|November 13, 2025
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概括

机器学习模型使用惯性传感器准确地预测干部移动性,帮助针对腰部疼痛 (LBP) 的个性化康复. 这项技术提供客观的临床评估,并降低医疗保健成本.

关键词:
这是一个ANOVA.机器学习 机器学习临床评估 临床评估惯性传感器 惯性传感器腰部疼痛 腰部疼痛 腰部疼痛预测模型的预测模型.回归模型是一种回归模型.

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科学领域:

  • 生物力学 生物力学
  • 机器学习 机器学习
  • 康复技术 康复技术 康复技术

背景情况:

  • 腰部疼痛 (LBP) 是全球残疾的主要原因之一.
  • 对LBP的客观临床评估是有限的.
  • 干的移动性是LBP严重程度和恢复的关键指标.

研究的目的:

  • 为了比较五个机器学习模型来预测车的移动性.
  • 评估惯性传感器数据在行李箱移动性预测中的有效性.
  • 评估在LBP管理中基于ML的客观评估的潜力.

主要方法:

  • 采集了77名使用惯性传感器的人体干移动性数据 (屈曲-延伸,旋转,横向化).
  • 应用数据增强和规范化技术.
  • 训练并评估了LightGBM,XGBoost,HistGradientBoosting,GradientBoosting和StackingRegressor模型. 这些模型都在使用.
  • 使用平均绝对误差 (MAE),平均平方误差 (MSE) 和R2评估性能,通过ANOVA和Tukey的HSD获得统计意义.

主要成果:

  • 梯度增强回归器在曲延伸和横向化方面显示出最小的误差和最高的统计意义.
  • 堆叠Regressor实现了旋转预测的最佳性能.
  • 所有测试的ML模型都证明了从惯性传感器数据中预测干移动性的潜力.

结论:

  • 惯性传感器与机器学习相结合,提供了一种可行的方法来预测车的移动性.
  • 这种方法可以为LBP患者提供个性化康复计划.
  • 预测性干部运动建模可以改善临床监测,并减少与LBP相关的社会经济负担.