This paper proposes a method to visualize worker behavior by utilizing machine learning from data obtained using wearable devices and videos. In conventional IE, worker behavior is observed using a stopwatch or video camera, and the amount of time required for each motion is measured. On the other hand, it has been reported that human motion estimation using machine learning is highly accurate when skeletal data is extracted from videos, and motion estimation is performed using a learning model of a graph neural network series. However, when applied to manufacturing sites, various problems have hindered remove widespread use, such as the loss of skeletal data obtained at the manufacturing site due to the positional relationship with equipment and other workers. In order to solve the problem mentioned above, this paper proposes a method to collect position and motion data from smart tags and skeletal data from video analysis, and to estimate workers behavior using machine learning. The smart tag was jointly developed by the research group and a company as a simple wearable device for workers. One of the features of this paper providing proof that the combination of video analysis and wearable devices can solve problems that are difficult to solve otherwise. This paper clarifies the effectiveness of the proposed motion estimation method in the case where posture estimation accuracy is reduced by obstacles such as machinery and equipment, work-in-progress, and other workers, and where skeletal data is often missing. For this purpose, an experiment is conducted assuming cell production and remove the accuracies of several motion estimation models are compared. © 2023 Japan Industrial Management Association. All rights reserved.
