Document Type
Article
Publication Date
1-20-2017
Publication Source
Computers
Abstract
Conventional wearable sensors are mainly used to detect the physiological and activity information of individuals who wear them, but fail to perceive the information of the surrounding environment. This paper presents a wearable thermal sensing system to detect and perceive the information of surrounding human subjects. The proposed system is developed based on a pyroelectric infrared sensor. Such a sensor system aims to provide surrounding information to blind people and people with weak visual capability to help them adapt to the environment and avoid collision. In order to achieve this goal, a low-cost, low-data-throughput binary sampling and analyzing scheme is proposed. We also developed a conditioning sensing circuit with a low-noise signal amplifier and programmable system on chip (PSoC) to adjust the amplification gain. Three statistical features in information space are extracted to recognize static humans and human scenarios in indoor environments. The results demonstrate that the proposed wearable thermal sensing system and binary statistical analysis method are efficient in static human detection and human scenario perception.
ISBN/ISSN
2073-431X
Document Version
Published Version
Copyright
Copyright © 2017, The Author(s)
Publisher
MDPI AG
Volume
6
Peer Reviewed
yes
Issue
1
eCommons Citation
Sun, Qingquan; Shen, Ju; Qiao, Haiyan; Huang, Xinlin; Chen, Chen; and Hu, Fei, "Static Human Detection and Scenario Recognition via Wearable Thermal Sensing System" (2017). Computer Science Faculty Publications. 150.
https://ecommons.udayton.edu/cps_fac_pub/150
Comments
This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC-BY 4.0).
Permission documentation on file.