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Recent technological advances foster the spreading of social software in the mobile domain. Hence, future usage patterns of mobile devices will involve more group interaction. While collaboration using mobile devices is an active area of research, only limited attention has been paid to the efficient initiation of group communication from mobile terminals.(More)
Mobile on-body sensing has distinct advantages for the analysis and understanding of crowd dynamics: sensing is not geographically restricted to a specific instrumented area, mobile phones offer on-body sensing and they are already deployed on a large scale, and the rich sets of sensors they contain allows one to characterize the behavior of users through(More)
City-scale mass gatherings attract hundreds of thousands of pedestrians. These pedestrians need to be monitored constantly to detect critical crowd situations at an early stage and to mitigate the risk that situations evolve towards dangerous incidents. Hereby, the crowd density is an important characteristic to assess the criticality of crowd situations.(More)
Previous work on the recognition of human movement patterns has mainly focused on movements of individuals. This paper addresses the joint identification of the indoor movement of <i>multiple</i> persons forming a cohesive whole - specifically a flock - with clustering approaches operating on features derived from multiple sensor modalities of modern(More)
Detecting pedestrians moving together through public spaces can provide relevant information for many location-based social applications. In this work we present an online method to detect such pedestrian flocks by spatio-temporal clustering of location trajectories. Compared to prior work, our method provides increased robustness against the influence of(More)
We introduce a concept that allows attendees of a party to collaboratively influence the music selection process. As explicit feedback is likely to disturb the atmosphere, we introduce an unobtrusive, implicit feedback mechanism. In particular, we propose to sense the partygoers' dance engagement by means of their mobile phones. Since people tend to dance(More)