Human-robot interaction mechanisms are being developed to cater to growing elderly and disabled population. There are still voids in achieving human-likeness before initiation of an interaction. Interaction scenario could be made interesting and effective by engraving basic cognitive skills into the robot's intelligence. Skills related to human-like interaction depends on cognitive skills and interpretation of the existing situation. Most robot users encounter a common problem with their robots. That is robot trying to interact with the user when he's engaged. In robot's perspective, the robot is not fully capable of deciding when to interact with the user. This paper presents a model to decide when to interact with the user, minimizing such failures. The proposed model has separate functional units for decision making on a user's nonverbal interaction demanding. User's availability for interaction is deduced through extracted information. The system observes a user for his bodily movements and behavior for a specified time duration. The extracted information is analyzed and then put through a module called Interaction Demanding Pose Identifier to interpret the interaction demanding of the user. The identified pose and other calculated parameters are fed into the Fuzzy Interaction Decision Making Module in order to interpret the degree of interaction demanding of the user. Interaction demanding is taken into consideration before going for direct interaction with the user. This method is implemented and tested in a simulated domestic environment with users in a broad age gap. Implementation of the method and results of the experiment are presented.