電気学会論文誌E(センサ・マイクロマシン部門誌)
Online ISSN : 1347-5525
Print ISSN : 1341-8939
ISSN-L : 1341-8939
論文
センサ内蔵グローブと機械学習による手作業の識別手法
松井 遼平田沼 巌川原 綾太朗牛尾 奈緒子吉元 広行河村 哲史杉井 信之
著者情報
ジャーナル 認証あり

2021 年 141 巻 8 号 p. 284-291

詳細
抄録

To digitalize handwork that remains at manufacturing sites, we developed a glove with built-in sensors that capture fingertip pressure and work sounds. To create an identification model with small amounts of training data, we developed a handwork identification model based on machine learning, in which a hidden Malkov model (HMM) extracts features from time-series information of sensor data and a support vector machine (SVM) identifies the type of handwork. The developed gloves and model identify specific handwork every 0.01 seconds. We experimentally confirmed that our proposed model can distinguish two typical types of handwork—connector insertion and screw tightening with a pistol grip electric screwdriver—with 82% accuracy from only 20 seconds of training data for each. This technology can be applied to recording handwork evidence and automation of incorrect work detection.

著者関連情報
© 2021 電気学会
前の記事 次の記事
feedback
Top