Online continual learning for human activity recognition

Martin Schiemer*, Lei Fang, Simon Dobson, Juan Ye

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
19 Downloads (Pure)

Abstract

Sensor-based human activity recognition (HAR), with the ability to recognise human activities from wearable or embedded sensors, has been playing an important role in many applications including personal health monitoring, smart home, and manufacturing. The real-world, long-term deployment of these HAR systems drives a critical research question: how to evolve the HAR model automatically over time to accommodate changes in an environment or activity patterns. This paper presents an online continual learning (OCL) scenario for HAR, where sensor data arrives in a streaming manner which contains unlabelled samples from already learnt activities or new activities. We propose a technique, OCL-HAR, making a real-time prediction on the streaming sensor data while at the same time discovering and learning new activities. We have empirically evaluated OCL-HAR on four third-party, publicly available HAR datasets. Our results have shown that this OCL scenario is challenging to state-of-the-art continual learning techniques that have significantly underperformed. Our technique OCL-HAR has consistently outperformed them in all experiment setups, leading up to 0.17 and 0.23 improvements in micro and macro F1 scores.

Original languageEnglish
Article number101817
Number of pages20
JournalPervasive and Mobile Computing
Volume93
DOIs
Publication statusPublished - 8 Jul 2023

Keywords

  • Human activity recognition
  • Online continual learning
  • Deep learning
  • Pervasive computing

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