Feature selection for unsupervised machine learning of accelerometer data physical activity clusters – A systematic review

Volume: 90, Pages: 120 - 128
Published: Oct 1, 2021
Abstract
Identifying clusters of physical activity (PA) from accelerometer data is important to identify levels of sedentary behaviour and physical activity associated with risks of serious health conditions and time spent engaging in healthy PA. Unsupervised machine learning models can capture PA in everyday free-living activity without the need for labelled data. However, there is scant research addressing the selection of features from accelerometer...
Paper Details
Title
Feature selection for unsupervised machine learning of accelerometer data physical activity clusters – A systematic review
Published Date
Oct 1, 2021
Volume
90
Pages
120 - 128
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