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UniHAR: A unified hierarchical transformer with multi-scale temporal modeling for wearable human activity recognition

Author

Listed:
  • Memoona
  • Muhammad Haseeb Hashir
  • Sung Won Kim

Abstract

UniHAR is a lightweight hierarchical multi-scale transformer based on wearable sensor data that has to be able to model time well; be able to recognize human activity across different datasets; and do so in real-time. There are several problems with what currently exists. UniHAR uses four major components: universal channel harmonization; adaptive multi-scale patch embedding; scale-adaptive Fourier cross-attention; factorized temporal-channel attention. The components allow for the ability to use various sensors and enables the low-complexity multi-scale fusion of features. UniHAR achieved an accuracy of 96.35%; 94.45%; 96.44%; and 99.23% on UCI-HAR, Opportunity, Daphnet, and PAMAP2 datasets, while being very lightweight and having high-speed inference capabilities; therefore, UniHAR is suitable for real-time healthcare applications.

Suggested Citation

  • Memoona & Muhammad Haseeb Hashir & Sung Won Kim, 2026. "UniHAR: A unified hierarchical transformer with multi-scale temporal modeling for wearable human activity recognition," PLOS ONE, Public Library of Science, vol. 21(10), pages 1-34, October.
    • Handle: RePEc:plo:pone00:0359774
    DOI: 10.1371/journal.pone.0359774
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