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Static Human Detection and Scenario Recognition via Wearable Thermal Sensing System
  • Qingquan Sun, California State University - San Bernardino
  • Ju Shen, University of Dayton
  • Haiyan Qiao, California State University - San Bernardino
  • Xinlin Huang, Tongji University
  • Chen Chen, University of Central Florida
  • Fei Hu, University of Alabama - Tuscaloosa
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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.

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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).

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Qingquan Sun, Ju Shen, Haiyan Qiao, Xinlin Huang, et al.. "Static Human Detection and Scenario Recognition via Wearable Thermal Sensing System" Computers Vol. 6 Iss. 1 (2017)
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