Estimating Individualized Daily Self-Reported Affect with Wearable Sensors

Wearable sensors (smart watches, health/fitness trackers, etc.) are experiencing an explosion in popularity. Their pervasiveness allows for effective data collections to quantify human behavior in natural settings, enriching traditional behavioral science research opportunities. In this paper, we focus on the problem of affect estimation from sensor-generated data, whereas ground truth is available to us in the form of daily self-reported affective states. First, our analysis shows that individuals’ self-reported affect labels exhibit low variance, with a few samples deviating significantly from average observations, which may relate to experiencing out-of-the-ordinary events. Second, we explore different machine learning models as well as label transformation techniques to directly infer the self-reported scores from available sensor data. Experimental results show that mixed effects model and label transformation are able to better estimate the individual daily affect. This work poses the basis for future sensor-based individualized and real-time affective digital and/or clinical interventions.