SMARTPHONE-BASED USER ACTIVITY RECOGNITION METHOD FOR HEALTH REMOTE MONITORING APPLICATIONS

Igor Bisio, Fabio Lavagetto, Mario Marchese, Andrea Sciarrone

2012

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.

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Paper Citation


in Harvard Style

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 - Volume 1: PECCS, ISBN 978-989-8565-00-6, pages 200-205. DOI: 10.5220/0003905502000205


in Harvard Style

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 - Volume 1: PECCS, ISBN 978-989-8565-00-6, pages 200-205. DOI: 10.5220/0003905502000205


in Bibtex Style

@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 - Volume 1: PECCS,},
year={2012},
pages={200-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003905502000205},
isbn={978-989-8565-00-6},
}


in Bibtex Style

@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 - Volume 1: PECCS,},
year={2012},
pages={200-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003905502000205},
isbn={978-989-8565-00-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - SMARTPHONE-BASED USER ACTIVITY RECOGNITION METHOD FOR HEALTH REMOTE MONITORING APPLICATIONS
SN - 978-989-8565-00-6
AU - Bisio I.
AU - Lavagetto F.
AU - Marchese M.
AU - Sciarrone A.
PY - 2012
SP - 200
EP - 205
DO - 10.5220/0003905502000205


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - SMARTPHONE-BASED USER ACTIVITY RECOGNITION METHOD FOR HEALTH REMOTE MONITORING APPLICATIONS
SN - 978-989-8565-00-6
AU - Bisio I.
AU - Lavagetto F.
AU - Marchese M.
AU - Sciarrone A.
PY - 2012
SP - 200
EP - 205
DO - 10.5220/0003905502000205