PRELIMINARY STUDY ON SPECTRAL CHARACTERISTICS FOR IDENTIFICATION OF SKIN COLOR UNDER CIRCULATORY DYSFUNCTION USING ARTIFICIAL SKIN SAMPLES

Published

November 14, 2017

Author(s)

Yoshi Ohno, Yuki Akizuki

Abstract

To assist development of appropriate light sources to diagnose circulatory dysfunction conditions effectively based on the color appearance of patient's skin under emergency situation in disaster sites, a set of urethane skin model samples have been developed, which simulate visual characteristics of real human skins under different circulatory dysfunction conditions (shocked, healthy, and congested) for young female/male and elderly female/male. Their characteristics have been evaluated by calculations of samples' color differences under various light spectra compared to those of real skins. Visual evaluation experiments have been conducted using these samples to evaluate distinction between different circulatory dysfunction conditions under the optimized LED light spectra used in our previous study. The developed urethane samples were found better in closeness of colors to real skin than previously-used paper color charts, but it needs further improvements in matching spectral reflectance characteristics. The experimental method directly comparing perceived color differences of the samples under the LED lights and reference lights (broadband spectra) was found useful for such evaluation.

Proceedings Title

Proc. CIE 2017 Midterm Meeting, Jeju, Korea

Conference Dates

October 23-25, 2017

Conference Location

Jeju, KR

Conference Title

CIE 2017 Midterm Meeting

Pub Type

Conferences

Download Paper

https://doi.org/10.25039/x44.2017

Local Download

Keywords

skin color, spectral distribution, circulatory dysfunction, emergency medical care

Citation

Ohno, Y. and Akizuki, Y. (2017), PRELIMINARY STUDY ON SPECTRAL CHARACTERISTICS FOR IDENTIFICATION OF SKIN COLOR UNDER CIRCULATORY DYSFUNCTION USING ARTIFICIAL SKIN SAMPLES, Proc. CIE 2017 Midterm Meeting, Jeju, Korea, Jeju, KR, [online], https://doi.org/10.25039/x44.2017, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=924194 (Accessed December 26, 2024)