Abstract
Peripheral hemodynamics, measured via the blood volume pulse and vasomotion, provide a valuable way of monitoring physiological state. Camera imaging-based systems can be used to measure these peripheral signals without contact with the body, at distances of multiple meters. While researchers have paid attention to non-contact imaging photoplethysmography, the study of peripheral hemodynamics and the effect of autonomic nervous system activity on these signals has received less attention. Using a method, based on a tissue-like model of the skin, we extract melanin text {C}_{m} and hemoglobin text {C}_{HbO} concentrations from videos of the hand and face and show that significant decreases in peripheral pulse signal power (by 36% ± 29%) and vasomotion signal power (by 50% ± 26%) occur during periods of cognitive and psychological stress. Via three experiments we show that similar results are achieved across different stimuli and regions of skin (face and hand). While changes in peripheral pulse and vasomotion power were significant the changes in pulse rate variability were less consistent across subjects and tasks.
Highlights
Peripheral hemodynamics, measured via the blood volume pulse and vasomotion, provide a valuable way of monitoring physiological state
Our results show that the characteristics of peripheral pulse amplitude and low-frequency oscillations change during cognitive and psychological stress versus rest
Our results suggest that peripheral pulse amplitude and vasomotion signals present a more reliable signal of sympathetic activation than pulse rate variability (PRV)
Summary
Peripheral hemodynamics, measured via the blood volume pulse and vasomotion, provide a valuable way of monitoring physiological state. The peripheral blood volume pulse waveform envelope pinches when a person is startled, fearful or anxious, which is the result of the body diverting blood from the extremities to the vital organs and working muscles, to prepare them for action, the “fight or flight” response Use of this phenomenon in affective computing applications is well established and has been leveraged to capture emotional responses in marketing/media testing[2], computer tasks[3], in training machine learning systems[4] and many psychological studies[5, 6]. If ubiquitous imaging devices can be used to measure vasomotion reliably, computer vision could be a very useful tool in tracking changes in ANS activity
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