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Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition.

Fahad Ayaz¹, Basim Alhumaily¹, Sajjad Hussain¹

¹James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

This summary is machine-generated.

Radar technology combined with convolutional neural networks (CNNs) enhances human activity recognition (HAR). MobileNetV2 with STFT preprocessing achieved 96.30% accuracy, balancing efficiency and performance for real-time applications.

Keywords:

computational cost deep learning human activity classification radar domain representations transfer learning

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Area of Science:

Radar Signal Processing
Machine Learning
Computer Vision

Background:

Human activity recognition (HAR) is crucial for smart security and healthcare.
Radar technology offers a privacy-preserving alternative for HAR.
Integrating advanced signal processing with deep learning is key to improving HAR.

Purpose of the Study:

To investigate the integration of CNNs with radar signal processing for enhanced HAR.
To evaluate different radar map generation techniques and CNN architectures.
To identify optimal configurations for real-time, resource-constrained HAR applications.

Main Methods:

Utilized three 2D radar processing techniques: range-FFT time-range maps, STFT time-Doppler maps, and SPWVD maps.
Evaluated four CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2.
Analyzed twelve CNN and preprocessing configurations for accuracy and computational efficiency.

Main Results:

MobileNetV2 with STFT preprocessing achieved 96.30% accuracy.
This configuration demonstrated high computational efficiency with a 2.57 ms inference time.
Spectrogram generation time was 220 ms, suitable for real-time processing.

Conclusions:

Radar-generated maps serve as effective visual data for HAR, ensuring privacy.
The study highlights the trade-offs between preprocessing complexity and recognition accuracy.
Optimal configurations, like MobileNetV2 with STFT, enable wider application of radar-based HAR in edge computing and AR.