Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108387
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Type: Conference paper
Title: Evaluation of wearable sensor tag data segmentation approaches for real time activity classification in elderly
Author: Shinmoto Torres, R.
Ranasinghe, D.
Shi, Q.
Citation: Mobile and Ubiquitous Systems: Computing, Networking, and Services, 2014 / Stojmenovic, I., Cheng, Z., Guo, S. (ed./s), vol.131, pp.384-395
Publisher: SPRINGER INTERNATIONAL PUBLISHING AG
Issue Date: 2014
Series/Report no.: Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering
ISBN: 9783319115689
ISSN: 1867-8211
Conference Name: 10th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MOBIQUITOUS) (02 Dec 2013 - 04 Dec 2013 : Tokyo, Japan)
Statement of
Responsibility: 
Roberto Luis Shinmoto Torres, Damith C. Ranasinghe, and Qinfeng Shi
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 95% using a time weighted windowing technique to aggregate contextual information to input sensor data.
Keywords: Conditional random fields; RFID; Feature extraction
Description: Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 131)
Rights: © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014
RMID: 0030015121
DOI: 10.1007/978-3-319-11569-6_30
Published version: http://link.springer.com/chapter/10.1007/978-3-319-11569-6_30
Appears in Collections:Mathematical Sciences publications

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