Abstract
This study presents a new method that supports the comparative analysis of works performed by high- and low-performing factory workers. Our method, based on explainable deep learning, automatically detects a sensor data segment that potentially contains knowledge about the skill of works by analyzing acceleration sensor data from high- and low-performing workers. Our evaluation with industrial engineers using sensor data from actual factory workers revealed that 78% of sensor data segments detected by our method included knowledge about skill.
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This study is partially supported by JSPS JP16H06539 and JP21K19769.
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Xia, Q., Wada, A., Yoshii, T., Namioka, Y., Maekawa, T. (2022). Comparative Analysis of High- and Low-Performing Factory Workers with Attention-Based Neural Networks. In: Hara, T., Yamaguchi, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-94822-1_26
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DOI: https://doi.org/10.1007/978-3-030-94822-1_26
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