UNIFIED REPRESENTATION LEARNING FRAMEWORKS FOR WEARABLEBASED HUMAN ACTIVITY RECOGNITION: FROM UNSUPERVISED EMBEDDING TO MULTI-TASK ADAPTATION

Wearable-based Human Activity Recognition (HAR) has become an important element in ubiquitous computing,
healthcare monitoring and intelligent human-computer interaction. Nevertheless, current HAR systems face
challenges in terms of generality across different sensor types, users, and activity areas and are mostly because of
the lack of labelled data and the over reliance on task-specific learning paradigms. In order to overcome the
limitations, recent scholarship has been directed towards unified representation learning frameworks that make
use of unsupervised, weakly supervised, and multitask adaptation strategies that learn generalisable feature
embeddings from multimodal wearable sensor data. This manuscript forms a detailed study on these unified
frameworks that describes how these frameworks have evolved from unsupervised embedding learning (Sheng &
Huber, 2020) and weakly supervised Siamese networks (Sheng & Huber, 2019) to state-of-the-art consistencybased weakly self-supervised approaches (Sheng & Huber, 2024; Sheng & Huber, 2025). It explores how the
representation learning methods of contrastive learning, self-supervised pre-training and multitask architectures
(Samyoun et al., 2022) can improve cross-domain generalisation and reduce the dependence on large amounts of
annotation. Moreover, the review brings together the results of new emergent models such as multimodal and
transformer-based models such as Harformer (Wang, Mo, & Zhu, 2025) and CrossHAR (Hong et al., 2024)
pointing to the promise of hierarchical and channel-separated embeddings for domain-invariant representations.
The synthesis of the confluence of unsupervised embedding, weakly supervised adaptation and multitask learning
has been identified as the cornerstone of next-generation unified frameworks for scalable, adaptive HAR in a
variety of contexts. The article concludes with an overview of outstanding challenges related to modality
imbalance, temporal dynamics alignment, and interpretability, as well as some recommendations for future
research in the area of universal representation learning for wearable-based HAR systems.