Static Gesture Recognition using Leap Motion
This is an incremental domain-specific solution for automating bar orders, but it raises ethical concerns about user interaction and real-world deployment.
The authors tackled the problem of automating bartender orders by developing a static gesture recognition system using Leap Motion and machine learning, achieving an average accuracy of 95%.
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.