Healthcare is moving rapidly from the long-standing reactive treatment approach to the early detection and preventative era. However, to fully embrace this trend, new approaches need to be developed. A step in this direction is to explore how to leverage data collected from wearables sensors to help in assessing health levels. This would pave the way for continuously monitoring individuals, which, in turn, lead to helping physicians diagnose diseases in the early stages. However, a major missing piece in moving forward with this concept is the lack of a sophisticated data analytics model. In this study, we propose a new correlation network model in which several aspects associated with health levels can be identified using population analysis. The proposed model is based on identifying various mobility parameters associated with groups under study, then a correlation network is developed based on the specified parameters. In such network, each node corresponds to a person and two nodes are connected by an edge if the corresponding individuals share similar mobility profiles. We show that various network properties reflect health information of the groups under study. To test the proposed model, we use gait parameters collected from three various groups, healthy younger people, geriatrics and Parkinson's disease patients. Obtained results show that the proposed model is very promising and can be a starting point towards a robust population analysis technique for utilizing mobility data in assessing health levels and predicting potential health hazards.