Group affiliation detection using model divergence for wearable devices

Abstract

Methods for recognizing group affiliations using mobile devices have been proposed using centralized instances to aggregate 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) recognition of group affiliations in multi-group environments, using the divergence of mobile phone sensor data distributions as an indicator of similarity. The method assesses pairwise similarity between individuals using model parameters instead of sensor observations, and then interprets that information in a distributed manner. An experiment was conducted with 10 individuals in different group configurations to compare 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 filter. We foresee applications in mobile social networking, life logging, smart environments, crowd situations and possibly crowd emergencies.

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“…However, sometimes it is not easy to differentiate whether two nearby people are involved in the same group activity. We will explore new sensing techniques to address this problem [8,49].…”

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“…In terms of comparing with the state of the art, it is difficult to provide a fair quantitative comparison (such as classification accuracy or algorithm performance) given that there is no widely accepted benchmark in the area of sensor-based group activity recognition. Most of the studies simulated their experiments and executed their proposed algorithms offline, e.g., [7,15,27,35], in which, challenges such as the inaccuracy caused by communication delay has been disregarded. Also, having domaindependent parameters can influence results, such as window size variations.…”

Identification of Partitions in a Homogeneous Activity Group Using Mobile Devices

“…To reduce cost (in particular in terms of energy consumption) of data collection and exchange to measure similarity between users, it is necessary to summarize the sensor data time series into aggregate sensor features. We choose to use probability distribution function (PDF) as the aggregate sensor feature [1]. The length of sensor data time series for summarization significantly impacts similarity measurement, so we need to determine the measurement time window for each sensing modality and deal with the different time window sizes when combining the measurements of multiple sensing modalities.…”

“…Lots of work have been done in group detection and activity recognition using mobile devices, but the problem at hand has not been fully addressed by existing work as detailed in Section 2. We have been inspired by the divergence-based affiliation detection (DBAD) approach [1] which provides a framework to identify group affiliation given a sensing 2 Mobile Information Systems modality to be used for an activity. Different from the group activity recognition problem which typically first recognizes each user's activity and then analyzes their cooperative or collaborative relationship in a group [2], the group affiliation detection problem is about how to identify which users have similar behavior instead of identifying their specific activities.…”