Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Emerging Machine Learning in Wearable Healthcare Sensors

  • Gandha Satria Adi (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Inkyu Park (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST))
  • Received : 2023.11.23
  • Accepted : 2023.11.30
  • Published : 2023.11.30
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Abstract

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

Keywords

Acknowledgement

This study was supported by the following grants: (1) National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT, No. 2021R1A2C3008742) and (2) Technology Innovation Program (02220146) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).

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