14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Next2Me: Capturing Social Interactions through Smartphone Devices using WiFi and Audio signals

  • @INPROCEEDINGS{10.4108/eai.7-11-2017.2274985,
        author={Jon Baker and Christos Efstratiou},
        title={Next2Me: Capturing Social Interactions through Smartphone Devices using WiFi and Audio signals},
        proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={ACM},
        proceedings_a={MOBIQUITOUS},
        year={2018},
        month={4},
        keywords={smartphone sensing social sensing wifi audio},
        doi={10.4108/eai.7-11-2017.2274985}
    }
    
  • Jon Baker
    Christos Efstratiou
    Year: 2018
    Next2Me: Capturing Social Interactions through Smartphone Devices using WiFi and Audio signals
    MOBIQUITOUS
    ACM
    DOI: 10.4108/eai.7-11-2017.2274985
Jon Baker1,*, Christos Efstratiou1
  • 1: University of Kent
*Contact email: jonty800@gmail.com

Abstract

Typical approaches in detecting social interactions consider the use of co-location as a proxy for real-world interactions. Such approaches can under-perform in challenging situations where multiple social interactions can occur in close proximity to each other. In this paper, we present a novel approach to detect co-located social interactions using smartphones. Next2Me relies on the use of WiFi signals and audio signals to accurately distinguish social groups interacting within a few meters from each other. Through a range of real-world experiments, we demonstrate a technique that utilises WiFi fingerprinting, along with sound fingerprinting to identify social groups. Experimental results show that Next2Me can achieve a precision of 88% within noisy environments, including smartphones that are placed in users' pockets, whilst maintaining a very low energy footprint (<3% of battery capacity per day).