Human activity recognition emerges as one of the prominent research areas in the recent past. However, the activity recognition still encounters many challenges like reliability of sensor data and accuracy of prediction that severely affects the aspect of decision making. In this paper, a futuristic framework has been proposed and experimented to build a precision-centric activity recognition method by analyzing the data obtained from Environment Monitoring System (EMS) and Personalized Positions Detection System (PPDS) using machine learning methods such as AdaBoost, Support Vector Machine (SVM) and Probabilistic Neural Networks (PNN). Further, the proposed approach utilizes the Dempster-Shafer Theory (DST)-based complete sensor data fusion thereby improving the global activity recognition performance. Finally, the proposed approach is validated using a real-world dataset obtained from UCI machine learning repository. The results conclude that the proposed activity recognition framework outperforms its existing context/situation-awareness approaches in terms of reliability, efficiency, and accuracy.
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