Accurate detection of real-world social interactions with smartphones

Quantifying social interactions requires accurate, reliable and real-time recognition, of both users' interpersonal distance and relative orientation. DARSIS is the outcome of our research towards fulfilling these requirements based upon a non-intrusive opportunistic mechanism that solely relies on sensors and communication capabilities of off-the-shelf smartphones. We developed a novel hierarchical classifier for interpersonal distance estimation, produced by a substantive training set of Bluetooth Received Signal Strength Indicator (RSSI) and a concrete feature selection process. The presented relative orientation estimation mechanism addresses problems associated with lack of facing direction information in prior works, independently of the wearing position. In addition, DARSIS introduces a collaborative sensing scheme which allows on-the-fly exchange of facing direction information between users and facilitates the interpersonal distance recognition process by sharing RSSI values among devices. We show that the proposed interpersonal distance estimation models outperform state-of-the-art solutions and achieve up to 93.52% accuracy while DARSIS as a coherent system detects accurately 81.40% of the interactions in a real-world environment.