Name
Abstract | ||
|---|---|---|
Traditionally, Electromyography (EMG) technology was used primarily in the medical domain for the investigation of neuromuscular normalities. However, the development of cheap surface EMG armbands have made this high-end technology commonly accessible to a much wider community. For example, providing gesture interfaces for gaming or controlling peripheral hardware devices. So far, research within this field has typically used complex machine learning classifiers, substantially large databases and long learning/training phases to develop applications-based approaches. In this paper, a novel algorithm is presented based on one shot learning, i.e. requiring only one example per gesture, therefore substantially reducing the training time and database size. To assess the reliability and the usefulness of the developed system, the accuracy of the algorithm has been compared with classic machine learning approaches providing comparable accuracy. Additionally, the algorithm is successfully demonstrated via a robotic control experiment using various gestures for mobile platform and manipulator control. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISSPIT47144.2019.9001765 | 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) |
Keywords | Field | DocType |
electromyography,adaptative gesture recognition,robotic control | Computer vision,Gesture,Computer science,Robotic control,Manipulator,Gesture recognition,Artificial intelligence,One-shot learning | Conference |
ISSN | ISBN | Citations |
2162-7843 | 978-1-7281-5342-1 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Marcheix | 1 | 0 | 0.34 |
| Bryan Gardiner | 2 | 28 | 8.31 |
| Sonya Coleman | 3 | 216 | 36.84 |