Feature extraction and feature selection in smartphone-based activity recognition

Maryam Banitalebi Dehkordi, Abolfazl Zaraki, Rossitza Setchi

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance.

Original languageEnglish
Pages (from-to)2655-2664
Number of pages10
JournalProcedia Computer Science
Publication statusPublished - 2020
Externally publishedYes
Event24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020 - Virtual Online
Duration: 16 Sept 202018 Sept 2020


  • Activity recognition
  • Feature extraction
  • Feature selection
  • Inertial sensor


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