Resource-Efficient GRU for Real-Time Gesture Recognition

Time-of-flight sensors deliver distance measurements through light propagation timing, facilitating object detection, gesture recognition, and three-dimensional mapping. These sensors maintain functionality across lighting conditions while ensuring response rates required for real-time applications. Based on these properties, combined with their cost and energy consumption parameters, we propose their exploitation in gesture recognition systems for human-robot interaction (HRI). This work presents two contributions: First, we introduce a dataset for gesture recognition tasks. Second, we develop a model for sequence classification optimized for resource constraints. The experimental results demonstrate the feasibility of the approach through quantitative evaluation metrics. Finally, we present classification results on the dataset, analyze the model's performance on gesture categories, and discuss future challenges and directions in this field of research.