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Human activity monitoring using a compressive active sensing electro-optical sensor
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Paper Abstract

Unobtrusive human movement monitoring is important in elderly care and assisted living to alert the caregivers of any potential accidents. The current state of art relies on features extracted from a radar system. The conventional optical camera system is not generally used in this application due to the concern of privacy. As an alternative, we investigate a different kind of electro-optical sensor – compressive active sensing electro-optical (CASEO) sensor. A CSAEO system consists of an infrared (IR) based spatial light modulation devices (SLM) based illuminator and a single-element avalanche photodetector (APD) based receiver. The scene information is encoded through a sequence of coded illumination patterns via the SLM illuminator and the corresponding measurements captured by the APD. Such compressive measurements can be used in scene classification instead of the actual video frames. In this work, we explore the feasibility of using CASEO in the identification of different activities. A CASEO processing framework was developed. Simulations using UIUC Human Activity Recognition test dataset were conducted to validate this framework.

Paper Details

Date Published: 13 May 2019
PDF: 9 pages
Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890H (13 May 2019); doi: 10.1117/12.2520139
Show Author Affiliations
Bing Ouyang, Harbor Branch Oceanographic Institute (United States)
Dennis Estrada, Harbor Branch Oceanographic Institute (United States)
Yanjun Li, Harbor Branch Oceanographic Institute (United States)
Fauzia Ahmad, Temple Univ. (United States)


Published in SPIE Proceedings Vol. 10989:
Big Data: Learning, Analytics, and Applications
Fauzia Ahmad, Editor(s)

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