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Human Movement Recognition Based on 3D Point Cloud Spatiotemporal Information from Millimeter-Wave Radar.

Xiaochao Dang1, Peng Jin1, Zhanjun Hao1

  • 1College of Computer Science & Engineering, Northwest Normal University, Lanzhou 730070, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
Summary

This study introduces a new millimeter-wave radar system for accurate human movement recognition, achieving up to 94% accuracy for walking and 93% for falls. The system enhances privacy and works in challenging environments.

Keywords:
3D point cloudhuman movementmillimeter-wave radarneural network

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Area of Science:

  • Computer Science
  • Engineering
  • Signal Processing

Background:

  • Traditional human movement recognition methods like video and Wi-Fi struggle with performance in adverse conditions and complex environments.
  • LiDAR-based methods are limited as they primarily capture static object characteristics, not dynamic human movements.
  • Millimeter-wave radar offers advantages in privacy, security, and non-line-of-sight recognition for human motion analysis.

Purpose of the Study:

  • To develop a novel human motion feature recognition system (PNHM) utilizing millimeter-wave radar 3D point cloud spatiotemporal information.
  • To design and implement a neural network, based on PointNet++, for effective recognition of human motion features.
  • To evaluate the system's performance in recognizing four distinct human motions across varied environments and angles.

Main Methods:

  • A human motion feature recognition system (PNHM) was developed using millimeter-wave radar spatiotemporal 3D point cloud data.
  • A neural network architecture, adapted from PointNet++, was designed for processing and classifying motion features.
  • A dataset comprising four human movements (walking, squat-to-stand, stand-to-sit, falling) was created under controlled experimental conditions.

Main Results:

  • The PNHM system achieved high recognition accuracies: 94% for walking upright, 84% for squatting to standing, 87% for standing to sitting, and 93% for falling.
  • The system demonstrated robust performance across different angles and experimental environments.
  • Comparison with four mainstream 3D point cloud action recognition models validated the proposed system's effectiveness.

Conclusions:

  • Millimeter-wave radar 3D point cloud spatiotemporal information is effective for human movement recognition.
  • The proposed PNHM system, leveraging PointNet++, provides a privacy-preserving and accurate solution for human motion analysis.
  • This technology holds significant potential for applications in intelligent pensions, remote health monitoring, and child supervision.