Learning Human Behaviour Patterns by Trajectory and Activity Recognition

Letícia Fernandes, Marília Barandas, Hugo Gamboa

Abstract

The world’s population is ageing, increasing the awareness of neurological and behavioural impairments that may arise from the human ageing. These impairments can be manifested by cognitive conditions or mobility reduction. These conditions are difficult to be detected on time, there is a lack of routine screening which demands the development of solutions to better assist and monitor human behaviour. This study investigates the question of what we can learn about human behaviour patterns from the rich and pervasive mobile sensing data. Data was collected over 6 months, measuring two different human routines through human trajectory analysis and activity recognition comprising indoor and outdoor environment. A framework for modelling human behaviour was developed using human motion features, extracted with and without previous knowledge of the user’s behaviour. The human patterns were modelled through probability density functions and clustering approaches. Using the learned patterns, inferences about the current human behaviour were continuously quantified by an anomaly detection algorithm where distance measurements were used to detect significant changes in behaviour. Experimental results demonstrate the effectiveness of the proposed framework that revealed an increased potential to learn behavioural patterns and detect anomalies.

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Paper Citation


in Harvard Style

Fernandes L., Barandas M. and Gamboa H. (2020). Learning Human Behaviour Patterns by Trajectory and Activity Recognition.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-398-8, pages 220-227. DOI: 10.5220/0008953902200227


in Bibtex Style

@conference{biosignals20,
author={Letícia Fernandes and Marília Barandas and Hugo Gamboa},
title={Learning Human Behaviour Patterns by Trajectory and Activity Recognition},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,},
year={2020},
pages={220-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008953902200227},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,
TI - Learning Human Behaviour Patterns by Trajectory and Activity Recognition
SN - 978-989-758-398-8
AU - Fernandes L.
AU - Barandas M.
AU - Gamboa H.
PY - 2020
SP - 220
EP - 227
DO - 10.5220/0008953902200227