Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user’s daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based HAR system is proposed. The impact of data segmentation, sensors used, and feature selection on HAR was fully investigated. The Support Vector Machine (SVM), a supervised learning algorithm, has been used to recognise six ambulation activities: downstairs, sit to stand, sitting, standing, upstairs, and walking. Considering the impact that data segmentation can have on the classification, the sliding window size was optimised, identifying the length of 10 s with 50% of overlap as the best performing. The inertial sensors and pressure sensors embedded into the smart insoles have been assessed to determine the importance that each one has in the classification. A feature selection technique has been applied to reduce the number of features from 272 to 227 to improve the robustness of the proposed system and to investigate the importance of features in the dataset. According to the findings, the inertial sensors are reliable for the recognition of dynamic activities, while pressure sensors are reliable for stationary activities; however, the highest accuracy (94.66%
) was achieved by combining both types of sensors.
Original languageEnglish
Article number5(3)
Pages (from-to)45
Number of pages14
JournalMethods and Protocols
Issue number3
Publication statusPublished (in print/issue) - 31 May 2022

Bibliographical note

Funding Information:
Luigi D’Arco is funded by the Ulster University Beitto Research Collaboration Programme. This research is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 823978.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • activity recognition
  • smart insole
  • machine learning
  • window size optimisation
  • feature selection


Dive into the research topics of 'Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition'. Together they form a unique fingerprint.

Cite this