Group affiliation detection using model divergence for wearable devices

Methods for recognizing group affiliations using mobile de-vices have been proposed using centralized instances to ag-gregate and evaluate data. However centralized systems do not scale well and fail when the network is congested. We present a method for distributed, peer-to-peer (P2P) recog-nition of group affiliations in multi-group environments, us-ing the divergence of mobile phone sensor data distributions as an indicator of similarity. The method assesses pairwise similarity between individuals using model parameters in-stead of sensor observations, and then interprets that informa-tion in a distributed manner. An experiment was conducted with 10 individuals in different group configurations to com-pare P2P and conventional centralized approaches. Although the output of the proposed method fluctuates, we can still correctly detect 93 % of group affiliations by applying a fil-ter. We foresee applications in mobile social networking, life logging, smart environments, crowd situations and possibly crowd emergencies. Author Keywords Group affiliation detection; computational social sciences; peer-to-peer; wearable computing; mobile computing; ACM Classification Keywords I.2.11 Distributed Artificial Intelligence: Miscellaneou