Comparative Analysis of High- and Low-Performing Factory Workers with Attention-Based Neural Networks

被引：3

作者：

Xia, Qingxin ^{[1
]}

Wada, Atsushi ^{[2
]}

Yoshii, Takanori ^{[2
]}

Namioka, Yasuo ^{[2
]}

Maekawa, Takuya ^{[1
]}

机构：

[1] Osaka Univ, Grad Sch Informat Sci & Technol, Osaka 5650871, Japan

[2] Toshiba Co Ltd, Corp Mfg Engn Ctr, Yokohama, Kanagawa 2350017, Japan

来源：

MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING, NETWORKING AND SERVICES | 2022年 / 419卷

关键词：

Attention networks; Work performance; Wearable sensor; Factory work; JOB-PERFORMANCE;

D O I：

10.1007/978-3-030-94822-1_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：469 / 480

页数：12

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