Novelty Detection in Physical Activity

Bernardo Leite, Amr Abdalrahman, João Castro, Julieta Frade, João Moreira, Carlos Soares

Abstract

Artificial Intelligence (AI) is continuously improving several aspects of our daily lives. There has been a great use of gadgets & monitoring devices for health and physical activity monitoring. Thus, by analyzing large amounts of data and applying Machine Learning (ML) techniques, we have been able to infer fruitful conclusions in various contexts. Activity Recognition is one of them, in which it is possible to recognize and monitor our daily actions. The main focus of the traditional systems is only to detect pre-established activities according to the previously configured parameters, and not to detect novel ones. However, when applying activity recognizers in real-world applications, it is necessary to detect new activities that were not considered during the training of the model. We propose a method for Novelty Detection in the context of physical activity. Our solution is based on the establishment of a threshold confidence value, which determines whether an activity is novel or not. We built and train our models by experimenting with three different algorithms and four threshold values. The best results were obtained by using the Random Forest algorithm with a threshold value of 0.8, resulting in 90.9% of accuracy and 85.1% for precision.

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Paper Citation


in Harvard Style

Leite B., Abdalrahman A., Castro J., Frade J., Moreira J. and Soares C. (2021). Novelty Detection in Physical Activity.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 859-865. DOI: 10.5220/0010254908590865


in Bibtex Style

@conference{icaart21,
author={Bernardo Leite and Amr Abdalrahman and João Castro and Julieta Frade and João Moreira and Carlos Soares},
title={Novelty Detection in Physical Activity},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={859-865},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010254908590865},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Novelty Detection in Physical Activity
SN - 978-989-758-484-8
AU - Leite B.
AU - Abdalrahman A.
AU - Castro J.
AU - Frade J.
AU - Moreira J.
AU - Soares C.
PY - 2021
SP - 859
EP - 865
DO - 10.5220/0010254908590865