Erdas, C.BerkeAtasoy, IsilAcici, KorayOgul, Hasan2019-06-202019-06-2020161877-0509https://www.sciencedirect.com/science/article/pii/S1877050916322153?via%3Dihubhttp://hdl.handle.net/11727/3650Activity recognition is the problem of predicting the current action of a person through the motion sensors worn on the body. The problem is usually approached as a supervised classification task where a discriminative model is learned from known samples and a new query is assigned to a known activity label using learned model. The challenging issue here is how to feed this classifier with a fixed number of features where the real input is a raw signal of varying length. In this study, we consider three possible feature sets, namely time-domain, frequency domain and wavelet-domain statistics, and their combinations to represent motion signal obtained from accelerometer reads worn in chest through a mobile phone. In addition to a systematic comparison of these feature sets, we also provide a comprehensive evaluation of some preprocessing steps such as filtering and feature selection. The results determine that feeding a random forest classifier with an ensemble selection of most relevant time-domain and frequency-domain features extracted from raw data can provide the highest accuracy in a real dataset. (C) 2016 The Authors. Published by Elsevier B.V.enginfo:eu-repo/semantics/openAccessActivity recognitionAccelerometer analysisFeature selectionconferenceObject985225270003875512000742-s2.0-84992391447