Passive Sensing for Class-Incremental Human Activity Recognition

Xue Ding, Yi Zhong, Sheng Wu*, Chunxiao Jiang, Weiliang Xie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Passive sensing technology enables Wi-Fi-based human activity recognition (HAR), which has been widely noted in recent years. This letter presents a novel Wi-Fi-based class-incremental HAR system that allows for the gradual addition of new activity categories. To the best of our knowledge, this is the first attempt to recognize all previously learned activities under the constraint of limited samples for both the original and newly added activity classes. It is challenging in: 1) how to prevent catastrophic forgetting of old activities and 2) how to leverage as few samples as possible to accurately recognize new activities. Therefore, a phased training and update strategy is proposed to avoid the knowledge-forgetting issue. Furthermore, to alleviate the unsatisfactory performance problem caused by insufficient samples of new categories, we design an amplitude-phase enhanced convolution neural network (CNN), which integrates an attention mechanism and dual loss (DL) function to enhance the feature discrimination and the generalization capability of the model. Extensive experiments show that our system can operate with promising perceptual accuracy in different datasets.

Original languageEnglish
Article number3504405
JournalIEEE Geoscience and Remote Sensing Letters
Volume20
DOIs
Publication statusPublished - 2023

Keywords

  • Class-incremental learning
  • human activity recognition (HAR)
  • loss function design
  • passive sensing

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Ding, X., Zhong, Y., Wu, S., Jiang, C., & Xie, W. (2023). Passive Sensing for Class-Incremental Human Activity Recognition. IEEE Geoscience and Remote Sensing Letters, 20, Article 3504405. https://doi.org/10.1109/LGRS.2023.3278091