This paper describes the implementation of a control system based on ten different hand gestures, providing a useful approach for the implementation of better user-friendly human-machine interfaces. Hand detection is achieved using fast detection and tracking algorithms, and classification by a light convolutional neural network. The experimental results show a real-time response with an accuracy of 95.09%, and making use of low power consumption. These results demonstrate that the proposed system could be applied in a large range of applications such as virtual reality, robotics, autonomous driving systems, human-machine interfaces, augmented reality among others.
CITATION STYLE
Núñez-Fernández, D. (2020). Development of a Hand Gesture Based Control Interface Using Deep Learning. In Communications in Computer and Information Science (Vol. 1070 CCIS, pp. 143–150). Springer. https://doi.org/10.1007/978-3-030-46140-9_14
Mendeley helps you to discover research relevant for your work.