Aug 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 61 references
TL;DR
A novel frequency-domain feature learning framework named FIRE, which enhances domain generalization to improve HAR performance and validating its robustness against domain shifts is proposed.
A novel lightweight cross-domain few-shot sensor-based HAR network (CFSH-Net) is proposed for cross-domain activity recognition with limited labeled samples, which demonstrates strong cross-user generalization on PAMAP2 and USC-HAD, and stable cross-dataset transfer when trained on OPPORTUNITY and evaluated on four oth...
Hao Zheng, Hongji Xu, Fei Gao et al.· IEEE journal of biomedical a...· 0 citations
The proposed Joint Embedding Predictive Architecture framework designed to learn robust and generalizable representations from unlabeled datasets demonstrates superior generalization on minority, high variance transitional activities such as sit-to-stand and sit-to-lie where supervised learning tend to overfit due to l...
Mohd Halim Mohd Noor, AbdulRahman M. A. Baraka· arXiv.org· 0 citations
By explicitly aligning target-domain features with source-domain distributions, FA-DANN enables WiFi-based HAR models to generalize across new environments without requiring labeled data, offering a scalable, cost-effective solution for real-world deployment.
Amany Elkelany, Robert Ross, Susan Mckeever· Journal of Ambient Intellige...· 0 citations
DMART-HAR is presented, a Dynamic Multimodal Activity Recognition Transformer framework that unifies structured multimodal representation learning, transformer-based temporal modeling, cross-modal interaction, and adversarial domain adaptation within a single architecture and consistently outperforms the strongest comp...
Maira Khalid, S. Manzoor, Jisi Chandroth· Multimedia· 0 citations
The proposed SSL framework employs the Transformer and Depthwise Separable Convolution to jointly capture global temporal dependencies and local spatial features, which are adaptively fused into discriminative spatio-temporal representations for downstream HAR.
Qian Yang, Ying-Long Huang, Jin Han et al.· Processes· 0 citations
FTDA (Frequency–Time Domain Alignment), an unsupervised domain-adaptation method that processes raw signals and their FFT-magnitude spectra through a dual-branch encoder, combines a Gradient-Reversal adversarial loss on the joint feature with a Multi-Kernel MMD on the frequency branch and applies a symmetric-KL time–fr...
