Please use this identifier to cite or link to this item:
Title: Robust unobtrusive fall detection using infrared array sensors
Authors: Fan, Xiuyi
Zhang, Huiguo
Leung, Cyril
Shen, Zhiqi
Keywords: DRNTU::Engineering::Computer science and engineering
Fall Detection
Infrared Arrays
Issue Date: 2017
Source: Fan, X., Zhang, H., Leung, C., & Shen, Z. (2017). Robust unobtrusive fall detection using infrared array sensors. 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 194-199. doi:10.1109/MFI.2017.8170428
Abstract: As the world's aging population grows, fall is becoming a major problem in public health. It is one of the most vital risk to the elderly. Many technology based fall detection systems have been developed in recent years with hardware ranging from wearable devices to ambience sensors and video cameras. Several machine learning based fall detection classifiers have been developed to process sensor data with various degrees of success. In this paper, we present a fall detection system using infrared array sensors with several deep learning methods, including long-short-term-memory and gated recurrent unit models. Evaluated with fall data collected in two different sets of configurations, we show that our approach gives significant improvement over existing works using the same infrared array sensor.
DOI: 10.1109/MFI.2017.8170428
Rights: © 2017 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: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

Files in This Item:
File Description SizeFormat 
Robust_Unobtrusive_Fall_Detection_using_Infrared_Array_Sensors_accepted.pdf1.96 MBAdobe PDFThumbnail

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.