  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.
