Activity Recognition in Smartphones Using Non-Intrusive Sensors

Pedro Fernandes, Cesar Analide, Bruno Fernandes, Bruno Fernandes

2024

Abstract

Activity recognition using smartphones has gained increased attention in recent years due to the widespread adoption of these devices and, consequently, their various sensors. These sensors are capable of providing very relevant data for this purpose. Non-intrusive sensors, in particular, offer the advantage of collecting data without requiring the user to perform any specific action or use any additional devices. The objective of this study was, therefore, the development of an application designed for activity recognition using exclusively non-intrusive sensors available in any smartphone. The data collected by these sensors underwent several processing stages, and after numerous iterations, a set of highly favorable features for training the machine learning models was obtained. The most prominent result was achieved by the model using the XGBoost algorithm, which achieved an impressive accuracy rate of 0.979. This quite robust result confirms the high effectiveness of using this type of sensors for activity recognition.

Paper Citation

in Harvard Style

Fernandes P., Analide C. and Fernandes B. (2024). Activity Recognition in Smartphones Using Non-Intrusive Sensors. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 88-93. DOI: 10.5220/0012303900003636

in Bibtex Style

@conference{icaart24,
author={Pedro Fernandes and Cesar Analide and Bruno Fernandes},
title={Activity Recognition in Smartphones Using Non-Intrusive Sensors},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={88-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012303900003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Activity Recognition in Smartphones Using Non-Intrusive Sensors
SN - 978-989-758-680-4
AU - Fernandes P.
AU - Analide C.
AU - Fernandes B.
PY - 2024
SP - 88
EP - 93
DO - 10.5220/0012303900003636
PB - SciTePress