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Authors: Igor Bisio ; Fabio Lavagetto ; Mario Marchese and Andrea Sciarrone

Affiliation: University of Genoa, Italy

Keyword(s): Remote Monitoring, Activity Recognition, Accelerometer, Decision Trees, Windowed Decision, Android Smartphones.

Related Ontology Subjects/Areas/Topics: Context ; Context-Aware Applications ; Detection and Estimation ; Digital Signal Processing ; Mobile and Pervasive Computing ; Mobile Computing ; Paradigm Trends ; Pervasive Health ; Software Engineering ; Telecommunications ; Ubiquitous Computing Systems and Services

Abstract: In the framework of health remote monitoring applications for individuals with disabilities or particular pathologies, quantity and type of physical activity performed by an individual/patient constitute important information. On the other hand, the technological evolution of Smartphones, combined with their increasing diffusion, gives mobile network providers the opportunity to offer real-time services based on captured real world knowledge and events. This paper presents a Smartphone-based Activity Recognition (AR) method based on decision tree classification of accelerometer signals to classify the user’s activity as Sitting, Standing, Walking or Running. The main contribution of the work is a method employing a novel windowing technique which reduces the rate of accelerometer readings while maintaining high recognition accuracy by combining two single-classification weighting policies. The proposed method has been implemented on Android OS smartphones and experimental tests have produced satisfying results. It represents a useful solution in the aforementioned health remote applications such as the Heart Failure (HF) patients monitoring mentioned below. (More)

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Paper citation in several formats:
Bisio, I.; Lavagetto, F.; Marchese, M. and Sciarrone, A. (2012). SMARTPHONE-BASED USER ACTIVITY RECOGNITION METHOD FOR HEALTH REMOTE MONITORING APPLICATIONS. In Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - PECCS; ISBN 978-989-8565-00-6; ISSN 2184-2817, SciTePress, pages 200-205. DOI: 10.5220/0003905502000205

@conference{peccs12,
author={Igor Bisio. and Fabio Lavagetto. and Mario Marchese. and Andrea Sciarrone.},
title={SMARTPHONE-BASED USER ACTIVITY RECOGNITION METHOD FOR HEALTH REMOTE MONITORING APPLICATIONS},
booktitle={Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - PECCS},
year={2012},
pages={200-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003905502000205},
isbn={978-989-8565-00-6},
issn={2184-2817},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - PECCS
TI - SMARTPHONE-BASED USER ACTIVITY RECOGNITION METHOD FOR HEALTH REMOTE MONITORING APPLICATIONS
SN - 978-989-8565-00-6
IS - 2184-2817
AU - Bisio, I.
AU - Lavagetto, F.
AU - Marchese, M.
AU - Sciarrone, A.
PY - 2012
SP - 200
EP - 205
DO - 10.5220/0003905502000205
PB - SciTePress