In this report, an automated bartender system was developed for making orders in a bar using hand gestures. The gesture recognition of the system was developed using Machine Learning techniques, where the model was trained to classify gestures using collected data. The final model used in the system reached an average accuracy of 95%. The system raised ethical concerns both in terms of user interaction and having such a system in a real world scenario, but it could initially work as a complement to a real bartender.
Cite
@article{arxiv.1705.05884,
title = {Static Gesture Recognition using Leap Motion},
author = {Babak Toghiani-Rizi and Christofer Lind and Maria Svensson and Marcus Windmark},
journal= {arXiv preprint arXiv:1705.05884},
year = {2017}
}
Comments
Results based on a study conducted during the course Intelligent Interactive Systems at Uppsala University, spring 2016