Evaluation of Wearable Sensor Tag Data Segmentation Approaches for Real Time Activity Classification in Elderly

The development of human activity monitoring has allowed the creation of multiple applications, among them is the recognition of high falls risk activities of older people for the mitigation of falls occurrences. In this study, we apply a graphical model based classification technique (conditional r...

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Bibliographic Details
Published inMobile and Ubiquitous Systems: Computing, Networking, and Services Vol. 131; pp. 384 - 395
Main Authors Shinmoto Torres, Roberto Luis, Ranasinghe, Damith C., Shi, Qinfeng
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2014
Springer International Publishing
SeriesLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Subjects
Computer science
Conditional random fields
Feature extraction
RFID
Online AccessGet full text
ISBN9783319115689
3319115685
ISSN1867-8211
1867-822X
DOI10.1007/978-3-319-11569-6_30

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Summary:The development of human activity monitoring has allowed the creation of multiple applications, among them is the recognition of high falls risk activities of older people for the mitigation of falls occurrences. In this study, we apply a graphical model based classification technique (conditional random field) to evaluate various sliding window based techniques for the real time prediction of activities in older subjects wearing a passive (batteryless) sensor enabled RFID tag. The system achieved maximum overall real time activity prediction accuracy of $$95\,\%$$ using a time weighted windowing technique to aggregate contextual information to input sensor data.
Bibliography:Original Abstract: The development of human activity monitoring has allowed the creation of multiple applications, among them is the recognition of high falls risk activities of older people for the mitigation of falls occurrences. In this study, we apply a graphical model based classification technique (conditional random field) to evaluate various sliding window based techniques for the real time prediction of activities in older subjects wearing a passive (batteryless) sensor enabled RFID tag. The system achieved maximum overall real time activity prediction accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\,\%$$\end{document} using a time weighted windowing technique to aggregate contextual information to input sensor data.
This research was supported by a grant from the Hospital Research Foundation (THRF) and the Australian Research Council (DP130104614).
ISBN:9783319115689
3319115685
ISSN:1867-8211
1867-822X
DOI:10.1007/978-3-319-11569-6_30