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Machine and Deep Learning Algorithms for Wearable Health Monitoring

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Computational Intelligence in Healthcare

Part of the book series: Health Information Science ((HIS))

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.

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Acknowledgments

This work was supported in part by the Innovation and Technology Fund of the Hong Kong SAR government (Grant no. ITP/097/18TP), University Grants Committee of the Hong Kong SAR government (Grant no. UGC-UAHB), the National Natural Science Foundation of China (Grant no. 51975127), Shanghai International Cooperation Project of One Belt and One Road of China (Grant No. 20110741700), Aerospace Science and Technology Fund of China (Grant no. AERO201937), and Fudan Research Start-up Fund (Grant no. FDU38341). The authors would like to thank them.

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Authors and Affiliations

  1. Department of Aeronautics and Astronautics, Fudan University, Shanghai, People’s Republic of China

    Chengwei Fei, Zihao Li & Tianmin Wang

  2. Institute of Textiles and Clothing, the Hong Kong Polytechnic University, Hong Kong, SAR, China

    Rong Liu

  3. Department of Health Technology and Informatics, Institute of Textiles and Clothing, the Hong Kong Polytechnic University, Hong Kong, SAR, China

    Faisal N. Baig

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Fei, C., Liu, R., Li, Z., Wang, T., Baig, F.N. (2021). Machine and Deep Learning Algorithms for Wearable Health Monitoring. In: Manocha, A.K., Jain, S., Singh, M., Paul, S. (eds) Computational Intelligence in Healthcare. Health Information Science. Springer, Cham. https://doi.org/10.1007/978-3-030-68723-6_6

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