Recognition of Group Mobility Level and Group Structure with Mobile Devices - 2018


Monitoring cluster mobility and structure is crucial for understanding group activities and social relations. During this Project, we have a tendency to develop algorithms for fine-grained mobility classification and structure recognition of social groups utilizing mobile devices. Initial, we gift a technique that acknowledges four levels of cluster mobility, as well as stationary, strolling, walking, and running. Second, using multiple types of mobile sensors, a completely unique relative position relationship estimation algorithm is developed to understand totally different moving cluster structures. We have a tendency to have conducted real-life experiments in that 12 volunteers moved in several little teams either in an office building or a shopping mall with numerous speeds and structures. Experimental results show that our approach achieves an accuracy of 99.five percent in cluster mobility level classification and concerning eighty % in cluster structure recognition.

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