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Continuous Activity Recognition Algorithm for FMCW Radar Integrating Consistency Regularization and Self-Distillation Techniques
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AI
DOI：10.1109/TAES.2025.3622577.png)
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Abstract
En 中文
In recent years, frequency-modulated continuous wave (FMCW) radar-based continuous human activity recognition (HAR) has attracted significant attention due to its privacy-preserving and robust capabilities. However, existing methods often focus on isolated actions, neglecting transitional phases and real-world dynamics. To overcome these limitations, a continuous HAR algorithm, DST-Net, is introduced. It combines a two-branch self-distillation multistage temporal convolutional network with a spectrum enhancement strategy, incorporating elements like time warping, time masking, and frequency masking. This strategy significantly improves the model's robustness by augmenting the dataset and preventing over-reliance on specific input features. The proposed loss function of DST-Net is comprised of three components: classification loss, consistency loss, and continuous temporal smoothness loss. The consistency loss boosts agreement between the two branches, the continuous temporal smoothness loss helps in minimizing sudden changes during activity transitions, and the classification loss is employed for continuous HAR. Experimental results show that proposed DST-Net achieves a 69.7% F1 score, 74.6 edit distance, and 95.6% classification accuracy, outperforming existing advanced methods. Quantitative analysis reveals a 5% and 4% improvement in F1 score in three different environments, validating its robustness and generalization ability. These innovations highlight DST-Net's effectiveness in addressing the limitations of existing continuous HAR algorithms, offering a comprehensive solution for real-world applications.
Keywords:
Consistency loss
continuous activity recognition
frequency-modulated continuous wave (FMCW) radar
self-distillation
spectrum enhancement
Journal
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