Benedikt Baldursson, Behnood Rasti, Karl S. Gudmundsson, D. Cojocaru, Kristinn Andersen, Saemundur E. Thorsteinsson
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Gesture Interpretation Control System Using Convolutional Neural Networks
This paper proposes a non-invasive control system for electrical wheelchairs utilizing facial gestures of individuals captured by real-time monocular camera. The images are interpreted with a convolutional neural network that achieves up to ~99.5% overall accuracy. The control system uses an embedded system with a graphics processing unit for predicting real-time throughput with fast inference time. This solution offers great versatility, where the user can make a gesture to depict a command of his choice.
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