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.
@article{arxiv.2310.15036,
title = {A Technique for Classifying Static Gestures Using UWB Radar},
author = {Abhishek Sebastian and Pragna R},
journal= {arXiv preprint arXiv:2310.15036},
year = {2024}
}
Comments
This is not a technical research paper, but an excerpt of what was applied during a funded project for the promotion of Open Science