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Authors: Ana Luísa Gomes 1 ; Vítor Paixão 2 and Hugo Gamboa 3

Affiliations: 1 Universidade Nova de Lisboa, Portugal ; 2 Champalimaud Centre for the Unknown, Portugal ; 3 Universidade Nova de Lisboa and PLUX - Wireless Biosignals, Portugal

ISBN: 978-989-758-069-7

Keyword(s): Human Activity Recognition, Forward Feature Selection, Hidden Markov Models, Clustering.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Multimedia ; Multimedia Signal Processing ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Telecommunications ; Time and Frequency Response ; Time-Frequency Analysis ; Wearable Sensors and Systems

Abstract: The Human Activity Recognition (HAR) systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according to the performed activities. In this work, a framework for human activity recognition in accelerometry (ACC) based on our previous work and with new features and techniques was developed. The new features set covered wavelets, the CUIDADO features implementation and the Log Scale Power Bandwidth creation. The Hidden Markov Models were also applied to the clustering output. The Forward Feature Selection chose the most suitable set from a 423th dimensional feature vector in order to improve the clustering performances and limit the computational demands. K-means, Affinity Propagation, DBSCAN and Ward were applied to ACC databases and showed promising results in activity recognition: from 73.20% 7.98% to 89.05% +/- 7.43% and from 70.75% +/- 10.09% to 83.89% +/- 13.65% with the Hungarian accuracy (HA) for the FCHA and P AMAP databases, respectively. The Adjust Rand Index (ARI) was also applied as clustering evaluation method. The developed algorithm constitutes a contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications. (More)

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Paper citation in several formats:
Luísa Gomes, A.; Paixão, V. and Gamboa, H. (2015). Human Activity Recognition Based on Novel Accelerometry Features and Hidden Markov Models Application.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 76-85. DOI: 10.5220/0005215800760085

@conference{biosignals15,
author={Ana Luísa Gomes. and Vítor Paixão. and Hugo Gamboa.},
title={Human Activity Recognition Based on Novel Accelerometry Features and Hidden Markov Models Application},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={76-85},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005215800760085},
isbn={978-989-758-069-7},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Human Activity Recognition Based on Novel Accelerometry Features and Hidden Markov Models Application
SN - 978-989-758-069-7
AU - Luísa Gomes, A.
AU - Paixão, V.
AU - Gamboa, H.
PY - 2015
SP - 76
EP - 85
DO - 10.5220/0005215800760085

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