Infrared–ultrasonic sensor fusion for support vector machine–based fall detection
Abstract
This article presents an infrared–ultrasonic sensor fusion approach for support vector machine–based fall detection, often required by elderly healthcare. Its detection algorithms and performance evaluation are detailed. The location, size, and temperature profile of the user can be estimated based on a novel sensory fusion algorithm. Different feature sets of the support vector machine–based machine learning algorithm are analyzed and their impact on fall detection accuracy is evaluated and compared empirically. Experiments study three non-fall activities, standing, sitting, and stooping, and two fall actions, forward falling and sideway falling, to simulate daily activities of the elderly. Fall detection accuracy studies are performed based on discretely and continuously (closer to reality) recorded experimental data, respectively. For the discrete data recording, an average accuracy of 92.2% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to 96.7% when sensor fusion is used. For the continuous data recording (180 training sets, 60 test sets at each distance), an average accuracy less than 70.0% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to around 90.3% after sensor fusion. New features will be explored in the next step to further increase detection accuracy.
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1507201
- Resource Type:
- Published Article
- Journal Name:
- Journal of Intelligent Material Systems and Structures
- Additional Journal Information:
- Journal Name: Journal of Intelligent Material Systems and Structures Journal Volume: 29 Journal Issue: 9; Journal ID: ISSN 1045-389X
- Publisher:
- SAGE Publications
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Chen, Zhangjie, and Wang, Ya. Infrared–ultrasonic sensor fusion for support vector machine–based fall detection. United States: N. p., 2018.
Web. doi:10.1177/1045389X18758183.
Chen, Zhangjie, & Wang, Ya. Infrared–ultrasonic sensor fusion for support vector machine–based fall detection. United States. https://doi.org/10.1177/1045389X18758183
Chen, Zhangjie, and Wang, Ya. Thu .
"Infrared–ultrasonic sensor fusion for support vector machine–based fall detection". United States. https://doi.org/10.1177/1045389X18758183.
@article{osti_1507201,
title = {Infrared–ultrasonic sensor fusion for support vector machine–based fall detection},
author = {Chen, Zhangjie and Wang, Ya},
abstractNote = {This article presents an infrared–ultrasonic sensor fusion approach for support vector machine–based fall detection, often required by elderly healthcare. Its detection algorithms and performance evaluation are detailed. The location, size, and temperature profile of the user can be estimated based on a novel sensory fusion algorithm. Different feature sets of the support vector machine–based machine learning algorithm are analyzed and their impact on fall detection accuracy is evaluated and compared empirically. Experiments study three non-fall activities, standing, sitting, and stooping, and two fall actions, forward falling and sideway falling, to simulate daily activities of the elderly. Fall detection accuracy studies are performed based on discretely and continuously (closer to reality) recorded experimental data, respectively. For the discrete data recording, an average accuracy of 92.2% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to 96.7% when sensor fusion is used. For the continuous data recording (180 training sets, 60 test sets at each distance), an average accuracy less than 70.0% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to around 90.3% after sensor fusion. New features will be explored in the next step to further increase detection accuracy.},
doi = {10.1177/1045389X18758183},
journal = {Journal of Intelligent Material Systems and Structures},
number = 9,
volume = 29,
place = {United States},
year = {Thu Mar 01 00:00:00 EST 2018},
month = {Thu Mar 01 00:00:00 EST 2018}
}
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https://doi.org/10.1177/1045389X18758183
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