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使用可穿戴传感器和机器学习来评估亨廷顿病上肢功能

Adonay S Nunes¹, İlkay Yıldız Potter¹, Ram Kinker Mishra¹

¹BioSensics LLC, 57 Chapel St, Newton, MA, USA.

Communications medicine

|February 25, 2025

概括

可穿戴传感器和深度学习可以监测亨廷顿病 (HD) 的上肢功能. 这项技术有助于早期检测和远程监测HD症状.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
数字健康数字健康

背景情况:

亨廷顿病 (HD) 是一种神经退行性疾病,影响四肢功能.
临床评估的HD症状仅限于特定的设置.
可穿戴式传感器可以捕获现实世界的数据,以补充临床评估.

研究的目的:

研究可穿戴传感器对监测亨廷顿病患者上肢功能的实用性.
应用深度学习和机器学习模型来分析传感器数据并预测疾病状态和临床分数.

主要方法:

一个戴在手腕上的可穿戴传感器被用来收集7天内来自亨廷顿病 (HD),前发性HD (pHD) 和对照组 (CTR) 患者的数据.
一个深度学习模型确定了目标定向的手动,并分析了动力学特征.
统计和机器学习模型被用来预测疾病组和临床分数.

主要成果:

在HD,pHD和CTR组之间观察到目标导向运动特征的显著差异.
运动特征与临床评分有很强的相关性.
分类模型准确地区分了组 (67%的平衡准确度,HD的回忆率为0.72),回归模型预测了临床分数.

结论:

可穿戴传感器与机器学习相结合,为监测亨廷顿病上肢功能提供了一个有希望的方法.
这项技术可以支持早期检测,远程监测患者,并在临床试验中评估治疗疗效.

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