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.

通过使用专有的 UWB 雷达传感器技术，本文提出了一个强大的基于 UWB 的静态手势识别框架。我们通过大量数据收集建立了包含五种常用手势的数据集。我们的方法包括全面的数据预处理流程，其中包括异常值处理、保持纵横比缩放和伪彩色图像转换。我们在处理后的图像上训练了 CNN 和 MobileNet 模型。令人惊讶的是，我们的最佳模型达到了 96.78% 的准确率。此外，我们还开发了一个用户友好的 GUI 框架，用于评估模型的系统资源使用情况和处理时间，结果显示低内存占用率和不到一秒的实时任务完成时间。这项研究在使用 UWB 技术提高静态手势识别方面迈出了重要的一步，可以在各个领域中实现实际应用。