Abstract

Hand gestures feel natural to perform, which makes them well-suited to use as Human-Computer Interaction interfaces. But detecting them with high accuracy in real-time is a challenging task. This paper presents an approach based on the Long Short-Term Memory Neural Network architecture to evaluate Surface Electromyography signals and determine the gesture performed. This approach is not new and has limited performance on people for whom it wasn't trained. Therefore, this research evaluates an approach where the Neural Network's existing knowledge is adjusted to a new person using just a few samples from the new person and very little training. This strategy allows getting accurate results with an approach that is usable in a Human-Computer Interface.

DUPLICATE OF https://wiki.tum.de/x/BoaRJ