A Technique for Classifying Static Gestures Using UWB Radar

Abhishek Sebastian, Pragna R
10/23/2023
cs.CVcs.AI

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Abstract

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.

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Cite this paper

@article{sebastian2023technique,
  title  = {A Technique for Classifying Static Gestures Using UWB Radar},
  author = {Abhishek Sebastian and Pragna R},
  year   = {2023},
  eprint = {2310.15036},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.15036v3}
}

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