生物信息学启发的IMU步骤序列建模用于使用光谱特征和混合人工智能来检测疲劳,用于性能运动
Attila Biró1,2,3,4,5, Levente Kovács1,6, László Szilágyi1,4,6
1Physiological Controls Research Center, Obuda University, 1034 Budapest, Hungary.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一个新的框架,使用可穿戴传感器通过分析步骤序列来检测跑步疲劳. 个性化的AI模型准确地识别疲劳,优于一般方法,并使实时监控成为可能.
科学领域:
- 生物力学 生物力学
- 可穿戴技术可穿戴技术
- 数据科学数据科学数据科学
背景情况:
- 可穿戴惯性测量单元 (IMU) 提供可访问的运行生物力学监测.
- 现有的方法往往缺乏个性化,并与个人间的变异性作斗争.
- 疲劳检测对于性能优化和伤害预防至关重要.
研究的目的:
- 开发一种以生物信息学为灵感的框架,用于使用单个腰部安装IMU检测疲劳.
- 将光谱,样本和频域特征与统计建模相结合.
- 评估人口层面和个性化的疲劳检测模型.
主要方法:
- 在非疲劳和疲劳状态下从19名休跑步者收集了步骤水平的生物力学数据.
- 采用混合效应的统计模型来分析疲劳对生物力学特征的影响.
- 开发和比较全球离开一个参与者 (LOPO) 模型,个性化监督的随机森林分类器和非疲劳的单一类SVM.
主要成果:
- 混合效应模型显示了显著的多维疲劳效应 (科恩d高达1.35,部分R2高达0.31).
- 全球LOPO模型的准确性很低 (55%),表明个体间差异很大.
- 个性化随机森林模型实现了高精度 (97.7%) 和AUC (0.997); SVM实现了AUC 0.967.
- 增加运动不规则性和减少神经肌肉控制与疲劳有关.
结论:
- IMU的脚步序列包含对疲劳敏感的生物力学特征.
- 混合方法将序列分析与个性化的AI模型相结合,使得可靠的个性化疲劳监测成为可能.
- 拟议的框架有可能用于体育分析,数字教练和实时可穿戴疲劳检测.
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