用可穿戴式多式传感器和异常检测器预先预测即将发生的强迫性热中风
IEEE journal of biomedical and health informatics
|October 9, 2023
概括
可穿戴传感器和机器学习可以在崩前一小时预测运动热中风 (EHS). 这项技术允许进行早期干预,以防止在艰苦的体力活动期间发生严重的与热有关的疾病.
科学领域:
- 运动医学 运动医学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 强迫性热中风 (EHS) 是在强烈的体力活动期间发生的关键医疗紧急情况.
- 早期发现EHS是具有挑战性的,但对于预防严重后果至关重要.
研究的目的:
- 开发和验证用于使用可穿戴传感器数据早期预测EHS的机器学习模型.
- 为了确定生理上的压力和身体压力模式,表明即将到来的EHS.
主要方法:
- 在充满载荷的游行中,利用可穿戴多式传感器 (心率,加速度计) 对2,102名美国陆军人员进行检测.
- 应用数据驱动的机器学习方法,结合生理应变和压力轨迹.
- 在用于EHS预测的压缩数据表示上使用异常检测.
主要成果:
- 在崩前33到69分钟成功预测了即将到来的EHS.
- 确定了低维的生理应力-应力模式,可以预测EHS.
- 使用478名参与者的数据验证了该模型.
结论:
- 在可穿戴传感器数据上检测机器学习异常,使得早期的EHS预测.
- 这种方法可以促进及时干预,最大限度地减少与EHS相关的危害.
- 该方法可适应各种体力活动,并可通过新的传感器进行增强.
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