A Computati onally Effi cient Bad Posture Detecti on Method to Alert Computer Users in Real Time

Abstract

These days lot of people spend long hours at a computer, so they face lot of health issues because of wrong sitti ng postures. Good posture is crucial for human well-being. A vision-based system is proposed to minimize the poor ergonomic practi ces among computer users. It can be installed in the corresponding user computer, and it runs with low computati on burden. The proposed system is based on image comparison with a reference image. The reference image is an image, that the user uti lizing their webcam or computer camera to capture themselves in the correct seated posture. Initi ally it has been a converted into grayscale and applied a Gaussian fi lter before store in the memory. Aft er making the reference image, the proposed system is stared to capture user’s image at a free defi ned frequency. The captured user’s image is fi ltered and converted to a binary image using a predefi ned threshold. Aft er that, two parallel processes are applied to same resultant image to identi fy the moment of left and right side and bending of forward and backward. The decision of both processes has been esti mated by comparing the calculated blackto- white pixel rati o with a predefi ned threshold. The noti fi cati on has been generated via a pop-up message on corresponding computer screen. To test the eff ecti veness, the proposed system has been implemented on a laptop computer in a Python environment with the support of OpenCV library. The test has been conducted of four diff erent bad postures such as over leaning to the right angle, left angle, forward and backward postures with 150 att empts. The test results shows that it has high potenti al to identi fy the bad postures of computer uses.