Design of an ergonomic App for entire rapid body assessment based on Mask RCNN

The rapid entire body assessment (REBA) is a rapid and semi-quantitative ergonomic assessment method for those who engaged in manual handling and/or standing work for a long time. In order to provide a self-assessment tool for operators, an App software based on Mask RCNN is proposed in this paper. The software is developed by adopting the architecture of a mobile terminal App combined with an imaging processing server. The main functions of the App are video capturing of work process, the operation of the process of REBA, human-machine interaction, etc., while the server is work for processing the video imaging transmitted from the App, key-points extraction of worker's body from the images for work posture identification. One thousand working scene photos marked by VIA are used for training and testing based on the Microsoft COCO dataset to obtain a reliable target detection model. Experiment of container handling scene shows that the App evaluation software has achieved higher evaluation efficiency and accuracy. The validation of this method has been proved compared with manual evaluation based on REBA. And other work posture evaluation methods will be developed in the future to form an ergonomic evaluation software system.