Emerging Machine Learning in Wearable Healthcare Sensors
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초록
Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to theshortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is apromising 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 trainingon observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised MLpresents 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 VectorMachines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complexclinical data.
Abstract
Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to theshortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is apromising 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 trainingon observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised MLpresents 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 VectorMachines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complexclinical data.
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