Abstract

Social interaction is one of the basic components of human life which impacts thoughts, emotions, decisions, and the overall wellbeing of individuals. In this regard, monitoring social activity constitutes an important factor for a number of disciplines, particularly the ones related to social and health sciences. Sensor-based social interaction data collection has been seen as a groundbreaking tool which has the potential to overcome the drawbacks of traditional self-reporting methods and to revolutionize social behavior analysis. However, monitoring social interactions typically implies a trade-off between the quality of collected data and the levels of unobtrusiveness and of privacy respecting, aspects which can affect spontaneity in subjects’ behavior. Despite the substantial research in the area of automatic recording of social interactions, the existing solutions remain limited: they either capture audio/video data which may raise privacy concerns in monitored subjects and may restrict the application to very specific areas, or provide low accuracy in detecting social interactions that occur on small spatio-temporal scale. The objective of this thesis is to provide and evaluate a solution for mobile monitoring of face-to-face social interactions, which maximizes privacy and minimizes obtrusiveness. In order to reliably detect social interactions that occur on small spatio-temporal scale, the proposed solution infers two types of information, namely spatial settings between subjects and their speech activity status. The challenge was to select appropriate sources that do not restrict application scenarios only to certain areas and do not capture privacy sensitive data, which are the drawbacks of video/audio systems. The second stage was to interpret the data acquired from non-visual and non-auditory sources and to model social interactions on small space- and time- scales. The work in this thesis assesses the reliability of the proposed approach in several scenarios, demonstrating the accuracy of approximately 90% in detecting the occur-rence of face-to-face social interactions. The feasibility of using the proposed approach for social interaction data collection is further evaluated with respect to the study of social psychology, which serves as the guideline for extracting the relevant features of social interactions. The evaluation has demonstrated the possibility to extract various nonverbal behavioral cues related to spatial organization between individuals and their vocal behavior in social interactions. By modeling social context using the extracted features, it is possible to achieve the accuracy of 81% in the automatic classification between formal versus informal social interactions. In addition, the proposed approach was applied to gather daily patterns of social activity for investigating their correlation with the mood changes in individuals, which has been explored so far only using the traditional self-reporting methods. The findings are consistent with previous studies thus indicating the possibility to use the proposed method of collecting social interaction data for investigating psychological effects of social activities.