Machine and Deep Learning Algorithms for Wearable Health Monitoring

Abstract

Because people desire a high quality of life, health is a vital standard of living factor that is attracting considerable attention. Thus, the development of methods that enable rapid and real-time evaluation and monitoring of the human health status has been crucial. In this study, we systematically reviewed the techniques of data mining and machine learning (ML) for wearable health monitoring (WHM) and their applications, including conventional ML methods (artificial neural networks, the Kriging model, support vector machines, and principal component analysis) and the latest advance in deep learning (DL) algorithms for WHM; specifically, the advantages of the DL-based approaches over the traditional ML methods were analyzed in line with metrics associated with data feature extraction and identification performances. Moreover, to attain an intuitive insight, this study further reviewed the developments on the classifier performance with regard to detection, monitoring, identification, and accuracy. Finally, with regard to the characteristics of time series data acquired using health condition monitoring through sensors, recommendations and advices are provided to apply DL methods to human body evaluation in specific fields. Moreover, future research trends required to improve the capability of DL algorithms further are offered.