Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89674
Title: Non-intrusive robust human activity recognition for diverse age groups
Authors: Wang, Di
Tan, Ah-Hwee
Zhang, Daqing
Keywords: Activity Recognition
DRNTU::Engineering::Computer science and engineering
Human Activity Recognition
Issue Date: 2015
Source: Wang, D., Tan, A.-H., & Zhang, D. (2015). Non-intrusive robust human activity recognition for diverse age groups. 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 368-375. doi:10.1109/WI-IAT.2015.152
Abstract: Many elderly prefer to live independently at their own homes. However, how to use modern technologies to ensure their safety presents vast challenges and opportunities. Being able to non-intrusively sense the activities performed by the elderly definitely has great advantages in various circumstances. Non-intrusive activity recognition can be performed using the embedded sensors in modern smartphones. However, not many activity recognition models are robust enough that allow the subjects to carry the smartphones in different pockets with unrestricted orientations and varying deviations. Moreover, to the best of our knowledge, no existing literature studied the difference between the youth and the elderly groups in terms of human activity recognition using smartphones. In this paper, we present our approach to perform robust activity recognition using only the accelerometer readings collected from the smartphone. First, we tested our model on two published data sets and found its performance is encouraging when compared against other models. Furthermore, we applied our model on two newly collected data sets: one consists of only young subjects (mean age = 22.5) and the other consists of only elderly subjects (mean age = 70.5). The experimental results show convincing prediction accuracy for both within and across diverse age groups. This paper fills the blank of elderly activity recognition using smartphones and shows promising results, which will serve as the groundwork of our future extensions to the current model.
URI: https://hdl.handle.net/10356/89674
http://hdl.handle.net/10220/47044
DOI: 10.1109/WI-IAT.2015.152
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/WI-IAT.2015.152].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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