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

Authors

Memoona, Muhammad Haseeb Hashir, Sung Won Kim

Published in

PloS one. Volume 21. Issue 10. Pages e0359774. Epub Oct 30, 2026.

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.

PMID:
42961989
Bibliographic data and abstract were imported from PubMed on 31 Oct 2026.

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