使用可穿戴IMU的时间序列数据自动分类静脉行走.
概括
使用可穿戴传感器的机器学习模型可以通过分析步态来自动识别前庭缺陷. 这些模型直接从传感器数据中学习,简化了过程,并帮助制定防摔策略.
科学领域:
- 生物力学 生物力学
- 神经学 神经学
- 机器学习 机器学习
背景情况:
- 静脉管疾病会损害平衡,增加跌倒的风险.
- 早期发现前体缺陷对于干预至关重要.
- 可穿戴式传感器和机器学习提供了自动步态评估潜力.
研究的目的:
- 开发和验证机器学习模型,用于分类前庭行走缺陷.
- 使用最小的预处理惯性测量单元 (IMU) 数据.
- 将模型性能与基于特性的方法进行比较.
主要方法:
- 在30名参与者的IMU时间序列数据上训练了双向LSTM (BiLSTM) 模型 (15名前庭缺陷患者,15名对照患者).
- 在原始时间序列数据和融合数据 (时间序列 + 工程特征) 上训练模型.
- 使用接收器操作特征曲线 (AUROC) 下面的面积来评估分类性能.
主要成果:
- 在BiLSTM模型中,使用最小的预处理数据实现了优异的分类性能 (AUROC = 0.86).
- 性能与以前使用工程特征的随机森林模型 (AUROC = 0.89) 相比.
- BiLSTM模型成功地从IMU原始数据中学习了歧视性步行模式.
结论:
- 机器学习模型,特别是BiLSTMs,可以有效地从最小处理的IMU数据中对前庭走势缺陷进行分类.
- 使用可穿戴传感器对步态动态的自动化分析显示出临床应用的前景.
- 这种方法减少了对手动特征工程的依赖,以识别前置障碍的个体.
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