Construction activities performed by workers are usually repetitive and physically demanding. Execution
of such tasks in awkward postures can strain their body parts and can result in fatigue, injuries or in
severe cases permanent disabilities. In view of this, it is essential to train workers, before the commencement
of any construction activity. Furthermore, traditional worker monitoring methods are tedious, inef-
ficient and are carried out manually whereas, an automated approach, apart from monitoring, can yield
valuable information concerning work-related behavior of worker that can be beneficial for worker training
in a virtual reality world. Our research work focuses on developing an automated approach for posture
estimation and classification using a range camera for posture analysis and categorizing it as
ergonomic or non-ergonomic. Using a range camera, first we classify worker’s pose to determine whether
a worker is ‘standing’, ‘bending’, ‘sitting’, or ‘crawling’ and then estimate the posture of the worker using
OpenNI middleware to get the body joint angles and spatial locations. A predefined set of rules is then
formulated to use this body posture information to categorize tasks as ergonomic or non-ergonomic.