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Feature Analysis to Human Activity Recognition
International Journal of Computers Communications & Control
Human activity recognition (HAR) is one of those research areas whose importance and popularity have notably increased in recent years. HAR can be seen as a general machine learning problem which requires feature extraction and feature selection. In previous articles different features were extracted from time, frequency and wavelet domains for HAR but it is not clear that, how to determine the best feature combination which maximizes the performance of a machine learning algorithm. The aim ofdoi:10.15837/ijccc.2017.1.2787 fatcat:prjniylugnaappix46hvgh3k7e