Eliciting, Measuring and Predicting Affect via Physiological Measures for Emotional Design

Abstract

Emotional design plays an important role in the development of products and services towards high value-added user satisfaction and performance enhancement. One critical challenge in emotional design is the measurement and prediction of affect. Most current measurement and prediction methods are affected by many biases and artifacts. For example, verbal reports only represent the sheer reflection of consciously experienced feelings. This study aimed to evaluate affect via physiological measures. Specifically, standardized affective stimuli in both visual and auditory forms were used to elicit different affective states (7 types of affect for the visual stimuli and 6 for the auditory ones). Each affective stimulus was presented for 6 s and a wide range of physiological signals were measured, including facial electromyography (EMG) (zygomatic and corrugator muscle activity), respiration rate, electroencephalography, and skin conductance response (SCR). Subjective ratings were also recorded immediately after stimulus presentation. The physiological measures show a relatively high differentiating ability in postulating affect via statistical tests and data mining-based prediction, with highest mean recognition rates of 91.47 and 71.13 % for the visual stimuli, and 91.36 and 80.66 % for the auditory stimuli, for valence- and affect-based predictions, respectively. This technological and methodological advancement offers a great potential for the development of emotional design.