Machine Learning on the Role of Eliminating Human Error on the Manufacturing Industry

Addanke, Supriya; Krishna, M. Vamsi; Pradeep, K.; Jayant, Khel Prakash; Ilyassova, Karlygash; Sainath, Kalya Lakshmi

doi:10.1109/icoei53556.2022.9776731

Cited by 1 publication

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“…As a matter of fact, human error significantly contributes to manufacturing production disturbances and safety problems [1][2][3][4][5][6][7][8][9][10][11]. In addition, humans are considered the most active objects; thus, detecting worker behavior is vital.…”

Section: Introductionmentioning

confidence: 99%

Detection of Anomalous Behavior of Manufacturing Workers Using Deep Learning-Based Recognition of Human–Object Interaction

2023

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The increasing demand for industrial products has expanded production quantities, leading to negative effects on product quality, worker productivity, and safety during working hours. Therefore, monitoring the conditions in manufacturing environments, particularly human workers, is crucial. Accordingly, this study presents a model that detects workers’ anomalous behavior in manufacturing environments. The objective is to determine worker movements, postures, and interactions with surrounding objects based on human–object interactions using a Mask R-CNN, MediaPipe Holistic, a long short-term memory (LSTM), and worker behavior description algorithm. The process begins by recognizing the objects within video frames using a Mask R-CNN. Afterward, worker poses are recognized and classified based on object positions using a deep learning-based approach. Next, we identified the patterns or characteristics that signified normal or anomalous behavior. In this case, anomalous behavior consists of anomalies correlated with human pose recognition (emergencies: worker falls, slips, or becomes ill) and human pose recognition with object positions (tool breakage and machine failure). The findings suggest that the model successfully distinguished anomalous behavior and attained the highest pose recognition accuracy (approximately 96%) for standing, touching, and holding, and the lowest accuracy (approximately 88%) for sitting. In addition, the model achieved an object detection accuracy of approximately 97%.

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