29 May 2013 Hyperspectral waveband group optimization for time-resolved human sensing
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Abstract
Pulse and respiration rates provide vital information for evaluating the physiological state of an individual during triage. Traditionally, pulse and respiration have been tracked by means of contact sensors. Recent work has shown that visible cameras can passively and remotely obtain pulse signals under controlled environmental conditions [2] [5] [14] [27]. This paper introduces methods for extracting and characterizing pulse and respiration signals from skin reflectivity data captured in peak sensitivity range for silicon detector (400nm-1100nm). Based on the physiological understanding [12] [13] [15] of human skin and reflectivity at various skin depths, we optimize a group of spectral bands to determine pulse and respiration with high Peak Signal-to-Noise Ratio (PSNR) and correlation values [27] [30]. Our preliminary results indicate top six optimal waveband groups in about 100nm - 200nm resolution in each, with rank-ordered peaks at 409nm, 512nm, 584nm, 667nm, 885nm and 772nm. This work, collected under an approved IRB protocol enhances non-contact, remote, passive, and real-time measurement of pulse and respiration for security and medical applications.
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Balvinder Kaur, Balvinder Kaur, Van A. Hodgkin, Van A. Hodgkin, Jill K. Nelson, Jill K. Nelson, Vasiliki N. Ikonomidou, Vasiliki N. Ikonomidou, J. Andrew Hutchinson, J. Andrew Hutchinson, } "Hyperspectral waveband group optimization for time-resolved human sensing", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500J (29 May 2013); doi: 10.1117/12.2018334; https://doi.org/10.1117/12.2018334
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