@inproceedings{oai:kanazawa-u.repo.nii.ac.jp:00008021,
 author = {秋田, 純一 and Ishikawa, Keisuke and Toda, Masashi and Sakurazawa, Shigeru and Akita, Junichi and Kondo, Kazuaki and Nakamura, Yuichi},
 book = {Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010},
 issue = {5593147},
 month = {Jan},
 note = {The finger movement has the information about force, speed to bend and the combination of fingers. If these information is estimated, the many degrees of freedom interface can apply it. In this study, we aimed for the many degrees of freedom finger movement classification. We tried each fingers classification and the estimate of the flexural finger force using surface-electromyogram signals. In the technique, amount of characteristic are a cepstral coefficient of EMG signals and an integral calculus EMG signals. A support vector machine performs learning and classtification. Therefore, I propose the classification technique and inspected a classification each finger and the combination of fingers by offline data handling using surface EMG signals. © 2010 IEEE., 金沢大学融合研究域融合科学系 / 金沢大学理工研究域電子情報学系},
 pages = {37--42},
 publisher = {IEEE = Institute of Electrical and Electronics Engineers},
 title = {Finger motion classification using surface-electromyogram signals},
 year = {2010}
}