A Technique for Classifying Static Gestures Using UWB Radar
This research addresses gesture recognition for practical applications in various domains, but it is incremental as it applies existing methods to new UWB radar data.
The paper tackled static gesture recognition using UWB radar by developing a framework with data pre-processing and training CNN and MobileNet models, achieving an accuracy of 96.78% and real-time processing under one second.
Our paper presents a robust framework for UWB-based static gesture recognition, leveraging proprietary UWB radar sensor technology. Extensive data collection efforts were undertaken to compile datasets containing five commonly used gestures. Our approach involves a comprehensive data pre-processing pipeline that encompasses outlier handling, aspect ratio-preserving resizing, and false-color image transformation. Both CNN and MobileNet models were trained on the processed images. Remarkably, our best-performing model achieved an accuracy of 96.78%. Additionally, we developed a user-friendly GUI framework to assess the model's system resource usage and processing times, which revealed low memory utilization and real-time task completion in under one second. This research marks a significant step towards enhancing static gesture recognition using UWB technology, promising practical applications in various domains.