Title

Static Human Detection And Scenario Recognition Via Wearable Thermal Sensing System

Keywords

Binary statistical information analysis; Human scenario recognition; Static human detection; Wearable PIR sensing

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.

Publication Date

3-1-2017

Publication Title

Computers

Volume

6

Issue

1

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.3390/computers6010003

Socpus ID

85053535607 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85053535607

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