@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00213056,
 author = {Qingxin, Xia and Atsushi, Wada and Takanori, Yoshii and Yasuo, Namioka and Takuya, Maekawa and Qingxin, Xia and Atsushi, Wada and Takanori, Yoshii and Yasuo, Namioka and Takuya, Maekawa},
 book = {マルチメディア，分散協調とモバイルシンポジウム2021論文集},
 issue = {1},
 month = {Jun},
 note = {This study presents a method for identifying significant activity differences between skilled and unskilled factory workers by a neural network with an attention mechanism using wrist-worn accelerometer sensor data collected in real manufacturing. To discover skill knowledge from skilled workers, industrial engineers manually identify activity differences between skilled and unskilled workers, which is likely to obtain skill knowledge, by watching video recordings or sensor data. However, a factory has many workers and manual comparison between pairs of workers is time-consuming. We propose an attention-based neural network to visualize the importance of input segments that contribute to the classification output, which is useful to identify activity differences between workers., This study presents a method for identifying significant activity differences between skilled and unskilled factory workers by a neural network with an attention mechanism using wrist-worn accelerometer sensor data collected in real manufacturing. To discover skill knowledge from skilled workers, industrial engineers manually identify activity differences between skilled and unskilled workers, which is likely to obtain skill knowledge, by watching video recordings or sensor data. However, a factory has many workers and manual comparison between pairs of workers is time-consuming. We propose an attention-based neural network to visualize the importance of input segments that contribute to the classification output, which is useful to identify activity differences between workers.},
 pages = {1133--1140},
 publisher = {情報処理学会},
 title = {A Supporting Technique for Comparative Analysis of Factory Work by Skilled and Unskilled Workers using Neural Network with Attention Mechanism},
 volume = {2021},
 year = {2021}
}