Bhamborae Mayur J, Flotho Philipp, Mai Adrian, Schneider Elena N, Francis Alexander L, Strauss Daniel J
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1799-1802. doi: 10.1109/EMBC44109.2020.9176359.
This paper presents a proof-of-concept for contactless and nonintrusive estimation of electrodermal activity (EDA) correlates using a camera. RGB video of the palm under three different lighting conditions showed that for a suitably chosen illumination strategy the data from the camera is sufficient to estimate EDA correlates which agree with the measurements done using laboratory grade physiological sensors. The effects we see in the recorded video can be attributed to sweat gland activity, which inturn is known to be correlated with EDA. These effects are so pronounced that simple pixel statistics can be used to quantify them. Such a method benefits from advances in computer vision and graphics research and has the potential to be used in affective computing and psychophysiology research where contact based sensors may not be suitable.
本文展示了一种使用摄像头对皮肤电活动（EDA）相关指标进行非接触式、非侵入性估计的概念验证。在三种不同光照条件下拍摄的手掌RGB视频表明，对于适当选择的照明策略，来自摄像头的数据足以估计EDA相关指标，这些指标与使用实验室级生理传感器进行的测量结果相符。我们在录制的视频中看到的效果可归因于汗腺活动，而汗腺活动又已知与EDA相关。这些效果非常明显，以至于可以使用简单的像素统计来对其进行量化。这种方法受益于计算机视觉和图形学研究的进展，并且有可能用于情感计算和心理生理学研究，在这些研究中基于接触的传感器可能并不适用。
