SEQUENTIAL CLASSIFICATION OF PALM GESTURES BASED ON A☆ ALGORITHM AND MLP NEURAL NETWORK FOR QUADROCOPTER CONTROL

This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector) and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the real-time operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPU-and GPU-based solutions.