WBAN (Wireless Body Area Network) is a growing technology, preferred in medical science. WBAN sensors are now used to monitor postures of a person. Posture, playing a crucial role in a person's life can provide a significant amount of important information on nonverbal communication and emotional cues. In recent trend of research, BAN sensors are used to identify different postures of a person to correctly classify the health condition, as postures could potentially provide valuable clues about a person's health. This class of interaction poses new challenge in classifying both static and dynamic postures. Triaxial accelerometer is required to find the tilt of a person with respect to the line passing through the centre of gravity. Based on the tilt, the posture is classified. This paper aims at uniquely distinguishing both static and dynamic postures. Fall can also be detected at the same time. A detailed deployment scenario, system architecture and a suitable algorithm are presented to illustrate the working of the proposed system.