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RaGeoSense for smart home gesture recognition using sparse millimeter wave radar point clouds.

Honghong Chen1, Xiangyu Wang2, Zhanjun Hao2

  • 1College of Computer Science and Engineering, Northwest Normal University, Gansu, China. chenhh@nwnu.edu.cn.

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|May 1, 2025
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Summary
This summary is machine-generated.

This study introduces RaGeoSense, a millimeter wave point cloud system for contactless gesture recognition in smart homes. It achieves 95.2% accuracy for eight gestures, demonstrating robustness across various conditions.

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Growing demand for contactless interaction in smart homes.
  • Gesture recognition technology offers significant market potential.
  • Millimeter wave sensing provides a robust foundation for gesture recognition.

Purpose of the Study:

  • To propose RaGeoSense, a sparse millimeter wave point cloud-based gesture recognition system for smart home scenarios.
  • To enhance recognition performance and system robustness using advanced signal processing and deep learning.
  • To accurately recognize eight distinct one-arm gestures.

Main Methods:

  • Noise reduction using K-mean clustering, frame difference, and median filtering on raw millimeter wave data.
  • Spatio-temporal feature extraction via sliding sequence sampling and point cloud tiling.
  • Integrated GBDT and XGBoost model for nonlinear feature extraction, coupled with LSTM for gesture sequence classification.

Main Results:

  • Achieved an average recognition rate of 95.2% for eight one-arm gestures.
  • Demonstrated consistent performance across varying distances, angles, and movement speeds.
  • Exhibited minimal impact from personnel differences and a degree of anti-interference capability.

Conclusions:

  • RaGeoSense offers a high-performance, robust solution for contactless gesture recognition in smart homes.
  • The system's effectiveness is validated by its accuracy and resilience to environmental and user variations.
  • This technology holds promise for advancing smart home interaction capabilities.