机器学习驱动的可穿戴痴呆症医疗保健:新兴技术和挑战的回顾

A. Sashima
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引用次数: 0

摘要

随着智能手机和智能手表等个人移动设备的日益商品化,测量个人生理和身体状态并持续记录变得更加容易。将机器学习技术应用于数据,我们可以检测老年人疾病的早期迹象,如痴呆症,并预测未来疾病的可能性。本文综述了机器学习技术在实现老年人可穿戴医疗保健中的应用。首先,我们调查了机器学习驱动的可穿戴技术用于早期检测痴呆症的文献。其次,我们讨论了用于构建ML模型的数据集问题。第三,我们描述了通过持续监测用户健康状况来收集纵向数据的服务框架的需求。最后,我们讨论服务框架的社会可接受的实现。
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Machine-learning-driven Wearable Healthcare for Dementia: A Review of Emerging Technologies and Challenges
: As personal mobile devices, such as smartphones and smartwatches, are increasingly commoditized, it has become easier to measure individual physiological and physical states and record them continuously. Applying machine learning techniques to the data, we can detect early signs of diseases in older people, such as dementia, and predict probabilities of future disorders. This review paper describes the machine learning technologies in realizing wearable healthcare for older people. First, we survey the literature on machine-learning-driven wearable technologies for the early detection of dementia. Second, we discuss issues of the datasets for constructing ML models. Third, we describe the need for a service framework to collect longitudinal data through continuous monitoring of the user’s health status. Finally, we discuss the socially acceptable implementation of the service framework.
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