Attend and discriminate: beyond the state-of-the-art for Human Activity Recognition using wearable sensors

Alireza Abedin, Mahsa Ehsanpour, Qinfeng Shi, Hamid Rezatofighi, Damith C. Ranasinghe

Research output: Contribution to journalArticleResearchpeer-review

41 Citations (Scopus)

Abstract

Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis. Although our collective know-how to solve Human Activity Recognition (HAR) problems with wearables has progressed immensely with end-to-end deep learning paradigms, several fundamental opportunities remain overlooked. We rigorously explore these new opportunities to learn enriched and highly discriminating activity representations. We propose: i) learning to exploit the latent relationships between multi-channel sensor modalities and specific activities; ii) investigating the effectiveness of data-agnostic augmentation for multi-modal sensor data streams to regularize deep HAR models; and iii) incorporating a classification loss criterion to encourage minimal intra-class representation differences whilst maximising inter-class differences to achieve more discriminative features. Our contributions achieves new state-of-the-art performance on four diverse activity recognition problem benchmarks with large margins-with up to 6% relative margin improvement. We extensively validate the contributions from our design concepts through extensive experiments, including activity misalignment measures, ablation studies and insights shared through both quantitative and qualitative studies. The code base and trained network parameters are open-sourced on GitHub https://github.com/AdelaideAuto-IDLab/Attend-And-Discriminate to support further research.

Original languageEnglish
Article number3448083
Number of pages22
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume5
Issue number1
DOIs
Publication statusPublished - Mar 2021

Keywords

  • activity recognition
  • attention
  • center-loss
  • cross-channel interaction encoder
  • data augmentation
  • deep learning
  • time-series data
  • wearable sensors

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