A Survey on Mobile Social Signal Processing
Niklas Palaghias, Seyed Amir Hoseinitabatabaei, Michele Nati, Alexander Gluhak, Klaus Moessner · ACM Computing Surveys · 2016
Understanding human behavior in an automatic but nonintrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behavior 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 behavior 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 , behavioral cues extraction , social signal inference, and social behavior 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.