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Computers 2017, 6(1), 3; doi:10.3390/computers6010003

Static Human Detection and Scenario Recognition via Wearable Thermal Sensing System

1
School of Computer Science and Engineering, California State University, San Bernardino, CA 92407, USA
2
Department of Computer Science, University of Dayton, Dayton, OH 45469, USA
3
Department of Information and Communication Engineering, Tongji University, Shanghai 200092, China
4
Center for Research in Computer Vision, University of Central Florida, Orlando, FL 32816, USA
5
Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Subhas Chandra Mukhopadhyay
Received: 10 August 2016 / Revised: 22 December 2016 / Accepted: 13 January 2017 / Published: 20 January 2017
(This article belongs to the Special Issue Theory, Design and Prototyping of Wearable Electronics and Computing)
View Full-Text   |   Download PDF [5297 KB, uploaded 20 January 2017]   |  

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. View Full-Text
Keywords: static human detection; human scenario recognition; wearable PIR sensing; binary statistical information analysis static human detection; human scenario recognition; wearable PIR sensing; binary statistical information analysis
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MDPI and ACS Style

Sun, Q.; Shen, J.; Qiao, H.; Huang, X.; Chen, C.; Hu, F. Static Human Detection and Scenario Recognition via Wearable Thermal Sensing System. Computers 2017, 6, 3.

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