Abstract
BackgroundAs the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated.MethodsThe machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test.ResultsA leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.
Highlights
Emotions play a fundamental role in human life, since they affect both human physiological and psychological status
As previously described in Perpetuini et al (2020), the optical probes were developed by STMicroelectronics (Catania, Italy) and they were composed by a light source consisting in a Light Emitting Diode (LED) emitting a wavelength range centered at 940 nm (SMC940 LED, Roithner Laser Technik, Vienna, Austria) coupled with a detector composed of Silicon PhotoMultiplier (SiPM) chip (Vinciguerra et al, 2017)
Concerning the PPG features, large (Hemphill, 2003) and significant correlation was found for LF/HF (r = 0.66; p = 4.19∙10−14) and root mean square of the successive differences (RMSSD) (r = 0.52; p = 1.22∙10−8), whereas a small significant correlation was found for ABP (r = 0.20; p = 0.04)
Summary
Emotions play a fundamental role in human life, since they affect both human physiological and psychological status. Anxiety produces the increase of muscle tension and activates mostly the sympathetic nervous systems This emotion provokes negative effect on visual attention (Janelle, 2002) and cognitive performances (Eysenck et al, 2007), it could be fundamental to measure the level of anxiety during activities which exploit cognitive functions. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications
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