Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81438
Title: Fall detection with unobtrusive infrared array sensors
Authors: Fan, Xiuyi
Zhang, Huiguo
Leung, Cyril
Shen, Zhiqi
Keywords: Fall Detection
Machine Learning
Engineering::Computer science and engineering
Issue Date: 2018
Publisher: Springer, Cham
Source: Fan, X., Zhang, H., Leung C., & Shen, Z. (2018). Fall detection with unobtrusive infrared array sensors. Lee, S., Ko, H., & Oh, S. (Eds.), Multisensor fusion and integration in the wake of big data, deep learning and cyber physical system (pp.253-267). Springer, Cham.
Abstract: As the world’s aging population grows, fall is becoming a major problem in public health. It is one of the most vital risks 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.
URI: https://hdl.handle.net/10356/81438
http://hdl.handle.net/10220/50387
ISBN: 978-3-319-90508-2
DOI: 10.1007/978-3-319-90509-9_15
Rights: © 2018 Springer, Cham. All rights reserved. This paper was published in Multisensor fusion and integration in the wake of big data, deep learning and cyber physical system and is made available with permission of Springer, Cham.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Books & Book Chapters

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