Context-aware posture analysis in a workstation-oriented office environment

Abstract

Among current research trends, correction of the sitting posture is attracting growing attention. Most office workers suffer several health problems during their work. The two greatest causes of health problems in the office environment are simple things. The first is poor sitting posture. Sitting with poor posture in front of a computer for hours causes cumulative damage. The second is an inappropriate workstation environment. The workstation environment is related to good sitting posture. For example, if the desk is too low, the user has to lean forward to look at the display. To address this problem, we propose a sitting posture recognition system that can recognize both human posture and the context of the workstation environment. The proposed system has three components. First, skeleton tracking is used to create a sideways view of the human skeleton. The skeleton model in this research is used to measure the joint angles of the human body. Second, we detect information on objects using a proposed workstation environment tracking system. Three types of features are used to filter the objects from the depth image. Finally, we compare the overall information with a standard sitting posture in a model-matching component. Experimental studies showed that the system can provide the necessary information for analyzing the human posture. A physician or user can apply this information to achieve correct sitting posture or prevent health problems in the office using the provided results. © 2014 Springer International Publishing Switzerland.