Integrating Features for Accelerometer-based Activity Recognition

Erdaş Ç. B., Atasoy I., Açıcı K., Oğul H.

PROCEDIA COMPUTER SCIENCE, vol.98, pp.522-527, 2016 (Scopus)

  • Publication Type: Article / Article
  • Volume: 98
  • Publication Date: 2016
  • Journal Indexes: Scopus
  • Page Numbers: pp.522-527
  • Ankara University Affiliated: No


Activity 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.