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Authors: Theodoros Giannakopoulos and Stasinos Konstantopoulos

Affiliation: National Center for Scientific Research 'Demokritos', Greece

ISBN: 978-989-758-251-6

Keyword(s): Audio Event Recognition, Raspberry PI, Audio Sensors, Activity Recognition, Fusion.

Related Ontology Subjects/Areas/Topics: Ambient Intelligence ; Applications ; Artificial Intelligence ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Health Engineering and Technology Applications ; Pattern Recognition ; Signal Processing ; Software Engineering ; Symbolic Systems

Abstract: This paper presents a method for recognizing activities taking place in a home environment. Audio is recorded and analysed realtime, with all computation taking place on a low-cost Raspberry PI. In this way, data acquisition, low-level signal feature calculation, and low-level event extraction is performed without transferring any raw data out of the device. This first-level analysis produces a time-series of low-level audio events and their characteristics: the event type (e.g., ‘music’) and acoustic features that are relevant to further processing, such as energy that is indicative of how loud the event was. This output is used by a meta-classifier that extracts long-term features from multiple events and recognizes higher-level activities. The paper also presents experimental results on recognizing kitchen and living-room activities of daily living that are relevant to assistive living and remote health monitoring for the elderly. Evaluation on this dataset has shown that our appro ach discriminates between six activities with an accuracy of more than 90%, that our two-level classification approach outperforms one-level classification, and that including low-level acoustic features (such as energy) in the input of the meta-classifier significantly boosts performance. (More)

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Paper citation in several formats:
Giannakopoulos T. and Konstantopoulos S. (2017). Daily Activity Recognition based on Meta-classification of Low-level Audio Events.In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, ISBN 978-989-758-251-6, pages 220-227. DOI: 10.5220/0006372502200227

@conference{ict4awe17,
author={Theodoros Giannakopoulos and Stasinos Konstantopoulos},
title={Daily Activity Recognition based on Meta-classification of Low-level Audio Events},
booktitle={Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE,},
year={2017},
pages={220-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006372502200227},
isbn={978-989-758-251-6},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE,
TI - Daily Activity Recognition based on Meta-classification of Low-level Audio Events
SN - 978-989-758-251-6
AU - Giannakopoulos T.
AU - Konstantopoulos S.
PY - 2017
SP - 220
EP - 227
DO - 10.5220/0006372502200227

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