Real time composite user activity modelling using hybrid approach for recognition

Activity modelling is required to support activity recognition and further to provide activity assistance. In knowledge driven activity modelling, though the time and location is known, no inference can be made thus limiting to simple activities. In data driven approach, activity models are learnt from existing data sets using machine learning based techniques. Thus, conventional approaches for activity modelling do not work well with composite activities due to complexity and uncertainty in real scenarios. Hence, real time modelling of static and dynamic characteristics of activities is required to be done effectively. Activity recognition encompasses three important tasks, namely, activity sensing, activity modelling and activity inference. Due to vast needs from variety of application systems, video based human activity recognition is used in order to track the user activities. In order to bridge the gap between low level data and human understanding, sensor based composite activity recognition is required. In order to analyse composite user activities, hybrid approach is required to build computational models. Also, to determine the ongoing activity, the sensor data is to be processed effectively. This approach in the paper describes the sensor based real time activity modelling to monitor composite user activities for recognition.