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Radar-Based Swimming Activity Recognition with Temporal Dynamic Convolution and Spectral Data Augmentation
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  • Yi Zhou ,
  • Xuliang Yu ,
  • Miguel Lopez-Benitez ,
  • Limin Yu ,
  • Yutao Yue
Yi Zhou
Xi’an Jiaotong-Liverpool University

Corresponding Author:[email protected]

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Xuliang Yu
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Miguel Lopez-Benitez
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Yutao Yue
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Abstract

Radar-based human activity recognition (HAR) is a popular research field. In this paper, we explore methods to improve the generalization of micro-Doppler-based swimming activity recognition. We identify three primary challenges for this task: a small dataset, inaccurate period estimation, and an inefficient network design that does not account for the unique characteristics of spectrograms. To address the limited dataset size, we propose spectral data augmentation tailored for micro-Doppler spectrograms. We also investigate two strategies, namely repeated augmentation and contrastive pretraining, to effectively utilize these augmentations. To tackle inaccurate period estimation, we introduce a segmentation approach based on energy distribution to handle temporal period variation, and we include a temporal modeling module in the network structure. To exploit the spread pattern of limb motion in the Doppler dimension and the continuous properties of torso motion in the temporal dimension,  we design a module that consists of both 2D convolution and 1D temporal dynamic convolution to serve as the feature extractor. Our evaluation on a self-collected swimming activity recognition dataset demonstrates that our model achieves high classification accuracy with significantly reduced computational costs. The augmentation methods, particularly when combined with contrastive pretraining, result in improved performance across accuracy and robustness metrics.