Reconstructing Compound Affective States Using Physiological Sensor Data

The human affective state is a product of complex biological processes and environmental stimuli. Situation aware systems aim at identifying the affective state of an individual using data from a gamut of connected devices. The bottle necks for such systems include continuous data streams , mobility of the data collection apparatus, device ubiquity and the device cost. While there is research done using physiological sensors that can overcome these challenges, their accuracy is often dismal and the results are not granular, i.e. the affective state is singular. In this paper we present results from an experiment that enabled us to generate models to identify an individuals affective state as a mixture of emotional states and their respective activation's. Secondly, we show that the affective state of an individual is actually a mixture of emotional states ( amusement, anger, neutral, sad, fear and disgust). During an experimental study, 85 participants were induced with specific emotions using audio-visual stimulus. Physiological data including heart rate, blood volume pressure (BVP), inter beat interval(IBI) and electrodermal activity(EDA) along with a self-report indicating the levels of 6 emotional states that include Amusement, Anger, Sad, Disgust, Fear and Neutral was recorded. Additionally, we recorded a self-reported score for Anxiety. The videos used to induce emotions were validated in a recently published study in Psychology. The data collected was used to create models that identify the dominant emotional state and the emotional spectrum (activation levels of all emotional states) for an individual. We create a map between the physiological data and the dominant emotional state and also between physiological data and the self-report scores. In addition, we identify often overlooked characteristics of human emotion such as variability in perception.