Towards Group-Activities Based Community Detection

Mobile-phone based activity recognition has been successfully applied to many useful scenarios like measuring the 'calories burnt' by a person. Unlike activities that are performed by a person alone, many activities are performed in a group-setting for example 'classroom teaching'. Because people often make friends with whom they are together, it's natural to look for communities in which people are engaged in similar physical-activities. Automated ways to learn such communities involve fusing physical-sensor-data from multiple users and hence, is a challenging problem. In this research, we measured physical-activities of seventy-two students located on two different university campuses for ten days. Using this data, we propose a model to detect communities based on similar physical-activities. Detecting such communities could be of great use e.g. it allows to invite new members who could be interested in similar activities and find those members who are in the community but are not actively engaged.