筋隆起を利用した電動義手制御のための動作識別手法の比較研究

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/08/01

タイトル(英語): A Study on Comparing Method of Motion Classification Using Muscle Bulging for Control of Powered Prosthetic Hand

著者名: 岩井　隼人（公立大学法人前橋工科大学大学院），王　鋒（公立大学法人前橋工科大学）

著者名(英語): Hayato Iwai (Graduate School of Maebashi Institute of Technology), Feng Wang (Maebashi Institute of Technology)

キーワード: 義手，PVDFフィルム，動作識別，筋隆起，圧電センサ　　prosthetic hand，PVDF film，motion classification，muscle bulging，piezoelectric sensor

要約(英語): Aiming at the control of a powered prosthetic hand, this paper compares methods for the classification of intended hand motions using muscle bulging patterns caused by muscle contraction. Two sheets of Polyvinylidene Difluoride (PVDF) film were used as sensors to detect the muscle bulging on the forearm caused by intended hand motions. A neural network had been successfully trained for the classification of 6 types of hand motions using the muscle bulging pattern detected by the two PVDF sensors. In this paper, we further studied the motion classification methods of back propagation neural network (BPNN), k-nearest neighbor algorithm (k-NN), and support vector machine (SVM) to compare their classification performance. We found that all three methods had a similar classification rate of about 95 % for 6 types of hand motions. Moreover, a regressive analysis comparison of the time for each classification method to converge to 95 % of the total classification rate showed that SVM converged significantly earlier than BPNN and k-NN. The time it takes for SVM to converge the classification rate to 95 % is less than 0.1 s, suggesting that real-time motion classification is possible by using SVM. In like manner, we found that SVM requires the least training data of the three methods at only 9 trials for a type of motion. Furthermore, SVM had the highest classification rate at about 90 % in practical experimental conditions. In conclusion, SVM was found to be the most practical of the three methods.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.8 （2023） 特集Ⅰ：Smart City を支える高度な情報通信，センシング及び医療技術　特集Ⅱ：情報処理技術の適用による有用情報の獲得と応用

本誌掲載ページ: 819-829 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/8/143_819/_article/-char/ja/