Research Article
Comparative Analysis of High- and Low-Performing Factory Workers with Attention-Based Neural Networks
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@INPROCEEDINGS{10.1007/978-3-030-94822-1_26, author={Qingxin Xia and Atsushi Wada and Takanori Yoshii and Yasuo Namioka and Takuya Maekawa}, title={Comparative Analysis of High- and Low-Performing Factory Workers with Attention-Based Neural Networks}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 18th EAI International Conference, MobiQuitous 2021, Virtual Event, November 8-11, 2021, Proceedings}, proceedings_a={MOBIQUITOUS}, year={2022}, month={2}, keywords={Attention networks Work performance Wearable sensor Factory work}, doi={10.1007/978-3-030-94822-1_26} }- Qingxin Xia
Atsushi Wada
Takanori Yoshii
Yasuo Namioka
Takuya Maekawa
Year: 2022
Comparative Analysis of High- and Low-Performing Factory Workers with Attention-Based Neural Networks
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-030-94822-1_26
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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