Feature extraction for psychophysiological load assessment in unconstrained scenarios

The relevance of psychophysiological measurements for affective computing and emotion analysis applications has been widely recognized. However, and although several authors have studied the informative content of parameters derived from cardiovascular and other modalities, feature extraction remains an open topic in the field. This is particularly relevant in scenarios where the autonomic nervous system triggering stimuli are unknown. In this paper, we analyze a set of features extracted from multimodal biosignal data, applicable to the assessment of psychophysiological load in unconstrained settings. Experimental evaluation is performed on real world data, collected both from control subjects and subjects with a strong clinical background, in a context of questionnaire-based clinical history reporting. The devised feature set has shown promising properties, making it prone to complement the more traditional measurements.