Author:
Akio Sashima
Affiliation:
Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa II Campus, University of Tokyo, 6-2-3 Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan
Keyword(s):
Healthcare, Dementia, Wearable, Machine Learning, Digital Biomarker.
Abstract:
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.