A Survey on Mobile Social Signal Processing

Niklas Palaghias
Seyed Amir Hoseinitabatabaei
Michele Nati
Alexander Gluhak
Klaus Moessner
University of Surrey
Published in: 
ACM Comput. Surv., vol. 48, no. 4, pp. 1-52, 2016.

Understanding human behaviour in an automatic but non-intrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behaviour into self-acting tools. These tools will reduce human error that is introduced by current obtrusive methods such as questionnaires. To achieve unobtrusiveness, we focus on exploiting the pervasive and ubiquitous character of mobile devices. In this article, a survey of existing techniques for extracting social behaviour through mobile devices is provided. Initially we expose the terminology used in the area and introduce a concrete architecture for social signal processing applications on mobile phones, constituted by sensing, social interaction detection, behavioural cues extraction, social signal inference and social behaviour understanding. Furthermore, we present state-of-the-art techniques applied to each stage of the process. Finally, potential applications are shown while arguing about the main challenges of the area.