loading
Papers
  • Login
  • Sign-Up

Research.Publish.Connect.

Paper

Authors: Evangelia Pippa 1 ; Iosif Mporas 2 and Vasileios Megalooikonomou 1

Affiliations: 1 University of Patras, Greece ; 2 University of Patras and University of Hertfordshire, Greece

ISBN: 978-989-758-180-9

Keyword(s): Human Motion Detection, Machine Learning, Feature Extraction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Health Engineering and Technology Applications ; Knowledge-Based Systems ; Symbolic Systems

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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user or Join INSTICC now for free.

Sign In SciTePress user: please login.

Sign In INSTICC Members: please login. If not a member yet, Join INSTICC now for free.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.224.44.168. INSTICC members have higher download limits (free membership now)

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Sorted by: Show papers

Note: The preferred Subjects/Areas/Topics, listed below for each paper, are those that match the selected paper topics and their ontology superclasses.
More...

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.

Show authors

Note: The preferred Subjects/Areas/Topics, listed below for each author, are those that more frequently used in the author's papers.
More...