Classification and feature analysis of actigraphy signals


We evaluate the effectiveness of 63 different features commonly used in the classification of actigraphy signals. We implement two feature selection techniques to rank the effectiveness of the features and select the “best” among them. Once the “best” feature(s) is (are) selected, a minimum distance classifier is used to classify… (More)


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