© 2016 Global IT Research Institute (GiRI). Smartphone is most ubiquitous device and provides unique opportunity of continuous and automated tracking of sedentary lifestyle with the help of embedded sensors. In this paper, we present the evaluation of our pilot study results to track the sedentary lifestyle. The proposed model works well in real-time and inside the smartphone environment to process the sensory data. We compute the time and frequency domain features over the acceleration signals and classify the context with non-parametric nearest neighbor algorithm. To analyze the lifestyle patterns, information is transferred to the cloud server for archiving, further computation and its availability anywhere, anytime for visualization. It facilitates users to maintain and monitor their everyday lifestyle patterns and assists them to change their unhealthy sedentary behaviour identified by the proposed research. Furthermore, achieved results demonstrate the applicability of the proposed research in real-world scenarios.