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Social interaction is one of the basic components of human life that impacts thoughts, emotions, decisions, and the overall wellbeing of individuals. In this regard, monitoring social activity constitutes an important factor in a number of disciplines, particularly those related to social and health sciences. Sensor-based social interaction data collection has been seen as a groundbreaking tool, having the potential to overcome the drawbacks of traditional self-reporting methods and revolutionize social behavior analysis. However, monitoring of social interactions typically implies a trade-off between the quality of collected data and the levels of unobtrusiveness and privacy respect, aspects that can affect spontaneity in subjects' behavior. In this article we discuss the challenges of automatic monitoring of social interactions; then we provide an overview of the current automatic monitoring concepts and the associated trade-offs. We finally present our approach of using non-visual and non-auditory mobile sources that mitigate privacy concerns and do not interfere with individuals daily routines, while providing a reliable platform for social interaction data collection.
Matic et al. (Mon,) studied this question.