A Study on Automatic Workload Evaluation Based on OWAS Using Multiple Cameras and 2D Skeletal Data

Abstract

In manufacturing sites, it is important to evaluate the workload of workers on a daily basis, and to implement continuous KAIZEN activities in order to maintain a safe and sustainable labor environment. This study proposes an automatic evaluation method for workloads that is applicable to actual manufacturing sites. Specifically, multiple cameras are used to capture work videos, and the workload is automatically evaluated by classifying the postures of the worker's upper limbs, back, and lower limbs using 2D skeletal data based on OWAS (Ovako Working Posture Analysis System). As a result of applying the method in a logistics factory, it was confirmed that the Action Category (AC), which represents workload in OWAS, could be estimated with an accuracy of 85.9%. However, it was also found that the estimation accuracy of AC4, which indicates the highest workloads, was relatively low. Therefore, the proposed method, which incorporates a human review process in order to supplement the results of the automatic evaluation, is demonstrated to be a practical and useful tool for screening high workloads in actual manufacturing sites.