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CAREC: Continual Wireless Action Recognition with Expansion-Compression Coordination.

Tingting Zhang1,2, Qunhang Fu1, Han Ding3

  • 1School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

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|August 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces CAREC, a framework for Wi-Fi-based action recognition that prevents forgetting old actions when learning new ones. CAREC efficiently compresses models while maintaining high accuracy.

Keywords:
continual learninghuman action recognitionincremental learningwireless sensing

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Real-world applications require adaptive systems that can learn new functionalities without complete retraining.
  • Class-incremental learning (CIL) is crucial for evolving systems, but Wi-Fi-based action recognition faces challenges like catastrophic forgetting and model expansion.
  • Existing methods struggle with performance degradation and uncontrolled parameter growth in incremental learning scenarios.

Purpose of the Study:

  • To propose CAREC, a novel class-incremental framework for Wi-Fi-based indoor action recognition.
  • To address catastrophic forgetting and uncontrolled model expansion in CIL for action recognition.
  • To balance dynamic model expansion with efficient compression for adaptive and scalable systems.

Main Methods:

  • Implemented a multi-branch architecture to integrate new action classes without degrading performance on existing ones.
  • Utilized balanced knowledge distillation for significant model compression (80%) while preserving accuracy.
  • Employed a data replay strategy for retaining old class information and a super-feature extractor to improve discrimination.

Main Results:

  • CAREC demonstrated a 51.82% reduction in performance degradation across four incremental learning stages on the XRF55 dataset.
  • Achieved 67.84% accuracy with only 21.08 million parameters, representing a significant reduction compared to conventional methods.
  • The framework effectively compressed the model while maintaining robust performance in incremental learning.

Conclusions:

  • CAREC offers an effective solution for class-incremental learning in Wi-Fi-based action recognition.
  • The proposed framework successfully mitigates catastrophic forgetting and manages model size during incremental updates.
  • CAREC enables the development of more adaptive, scalable, and efficient indoor action recognition systems.