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A lightweight hybrid vision transformer network for radar-based human activity recognition.

Sha Huan^1,2, Zhaoyue Wang¹, Xiaoqiang Wang³

¹School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, 510006, China.

Scientific Reports

|October 21, 2023

Summary

This study introduces a lightweight hybrid Vision Transformer (LH-ViT) for efficient radar-based human activity recognition (HAR). The novel network achieves high accuracy and low latency, making it suitable for embedded applications.

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Area of Science:

Computer Science
Signal Processing
Artificial Intelligence

Background:

Radar-based human activity recognition (HAR) provides privacy-preserving and lighting-robust sensing.
Deep neural networks excel at classifying radar micro-Doppler signals for HAR but are computationally intensive for embedded systems.

Purpose of the Study:

To develop an efficient and lightweight network for radar-based HAR that balances accuracy and computational cost.
To address the challenges of implementing complex deep learning models in resource-constrained embedded applications.

Main Methods:

Proposed a lightweight hybrid Vision Transformer (LH-ViT) integrating efficient convolutions with Vision Transformer (ViT) self-attention.
Employed Feature Pyramid architecture for multi-scale micro-Doppler map feature extraction.
Utilized stacked Radar-ViT with fold/unfold operations and RES-SE blocks to reduce computational load and enhance features.

Main Results:

The proposed LH-ViT method demonstrated superior expressiveness and computing efficiency compared to traditional methods.
Experimental results on two human activity datasets validated the network's performance advantages.

Conclusions:

The LH-ViT offers an effective solution for radar-based HAR in embedded systems, achieving high accuracy with reduced computational requirements.
This approach enables practical deployment of advanced HAR technologies in devices with limited resources.