Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIS.2010.131 | ACIS-ICIS |
Keywords | Field | DocType |
finger motion classification,finger movement,integral calculus emg signal,freedom interface,emg signal,flexural finger force,cepstral coefficient,freedom finger movement classification,classification technique,surface-electromyogram signals,fingers classification,surface emg signal,degree of freedom,data handling,support vector machines,support vector machine,electrodes,feature extraction,indexes | Thumb,Pattern recognition,Computer science,Finger tracking,Support vector machine,Cepstrum,Electromyography,Feature extraction,Artificial intelligence,Signal classification,Group method of data handling | Conference |
Citations | PageRank | References |
4 | 0.76 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keisuke Ishikawa | 1 | 4 | 1.10 |
| Masashi Toda | 2 | 39 | 14.63 |
| Shigeru Sakurazawa | 3 | 42 | 9.39 |
| Junichi Akita | 4 | 83 | 19.41 |
| Kazuaki Kondo | 5 | 39 | 12.42 |
| Yuichi Nakamura | 6 | 396 | 40.51 |