Real-time evaluation and feedback system for ergonomics on the shop floor.

Despite the greatly increased automation in manufacturing industries, manual operations still exist, and ergonomic risk factors that arise because of manual operations can lead to Work-Related Musculoskeletal Disorders (WMSDs). To mitigate the risk, manual operations should be assessed to identify if any risk, such as awkward posture, exist. Most assessments are carried out offline but this cannot alert and prevent operators from adopting awkward postures in time. Hence, due to the popularity of flexible manufacturing systems that require immediate response to changes, there is need for a real-time assessment. Therefore, the aim of this research is to develop a real-time knowledge-based ergonomic assessment system for use in the real-time evaluation of work postures on the shop floor and provision of feedback to workers, using 3D motion sensors. The developed intelligent system utilizes the knowledge from health and safety (H&S) guidelines, set of rules and an inference engine, to automatically capture and assess worker’s postures and provide real-time feedback to the worker through an easy-to-understand user interface. The system has been validated using many case studies which include the posture assessment of: 6 operators assembling engine valve, 4 seated researchers conducting desk-based reading and 15 operators during lifting, assembly and hammering of IKEA table. The system when tested proved to achieve: real-time assessment, easy-to-understand feedback, reliable measurements with Cronbach’s alpha of 0.978, p=0.045 and Kendall’s coefficient of concordance of 0.634, p = 0.000. The main contribution of this work lies in providing real-time feedback to workers. This contribution is in three sub-areas namely: i) Development of a real-time Kinect-based tool for H&S-compliant ergonomic assessment. ii) Development of a knowledge-based real-time feedback system for improved posture assessment. iii) Provision of real-time feedback to alert workers in time. The novelty of this research is in the development of a knowledge-based system for real-time ergonomic assessment and feedback to workers using 3D motion sensors.PhD in Manufacturin