Name
Abstract | ||
|---|---|---|
Over the last decade, technical innovations have resulted in major achievements in the field of upper limb prostheses. However, the limitation of such devices is not the electro-mechanical realization, but rather the lack of astute cutaneous control-sources. This contribution demonstrates real-time detection of muscle exertions by combining myoelectric and near-infrared signals. The presented sensor technology and classification scheme have been developed for five-finger hand prostheses but can be employed to control a variety of prosthetic devices. This control mechanism only requires a patient to individually contract extant muscles, for which a minimal-threshold myoelectric or reflected near-infrared signal can be measured on the surface of the skin. Our experimental data show that the combination of both sensor types provides better classification results and surpasses the spatial resolution of a single pickup device. Features extracted from these signals can be used as input data for our existing classifier and allow compensation of muscle fatigue effects. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-27579-1_77 | EUROCAST (2) |
Keywords | Field | DocType |
muscle exertion,input data,myoelectric signal,minimal-threshold myoelectric,control mechanism,near-infrared signal,muscle fatigue effect,classification scheme,prostheses control,experimental data,contract extant muscle,better classification result | Computer vision,Experimental data,Computer science,Classification scheme,Near-infrared spectroscopy,Sensor fusion,Artificial intelligence,Extant taxon,Classifier (linguistics),Pickup,Image resolution,Machine learning | Conference |
Volume | ISSN | Citations |
6928 | 0302-9743 | 6 |
PageRank | References | Authors |
0.75 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Herrmann | 1 | 6 | 0.75 |
| Andreas Attenberger | 2 | 12 | 2.44 |
| Klaus Buchenrieder | 3 | 125 | 18.89 |