Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System

Evangelia Pippa, Iosif Mporas, Vasileios Megalooikonomou

2016

Abstract

In order to plan and deliver health care in a world with increasing number of older people, human motion monitoring is a must in their surveillance, since the related information is crucial for understanding their physical status. In this article, we focus on the physiological function and motor performance thus we present a light human motion identification scheme together with preliminary evaluation results, which will be further exploited within the FrailSafe Project. For this purpose, a large number of time and frequency domain features extracted from the sensor signals (accelerometer and gyroscope) and concatenated to a single feature vector are evaluated in a subject dependent cross-validation setting using SVMs. The mean classification accuracy reaches 96%. In a further step, feature ranking and selection is preformed prior to subject independent classification using the ReliefF ranking algorithm. The classification model using feature subsets of different size is evaluated in order to reveal the best dimensionality of the feature vector. The achieved accuracy is 97% which is a slight improvement compared to previous approaches evaluated on the same dataset. However, such an improvement can be considered significant given the fact that it is achieved with lighter processing using a smaller number of features.

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Paper Citation


in Harvard Style

Pippa E., Mporas I. and Megalooikonomou V. (2016). Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System . In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016) ISBN 978-989-758-180-9, pages 88-95. DOI: 10.5220/0005912200880095


in Bibtex Style

@conference{ict4awe16,
author={Evangelia Pippa and Iosif Mporas and Vasileios Megalooikonomou},
title={Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System},
booktitle={Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)},
year={2016},
pages={88-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005912200880095},
isbn={978-989-758-180-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)
TI - Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System
SN - 978-989-758-180-9
AU - Pippa E.
AU - Mporas I.
AU - Megalooikonomou V.
PY - 2016
SP - 88
EP - 95
DO - 10.5220/0005912200880095