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
Human activity recognition (HAR) in smart environments plays a critical role in applications such as healthcare monitoring, intelligent transportation systems, and ambient assisted living; however, existing approaches are limited by their inability to effectively handle heterogeneous multimodal sensor data, capture lon...
Maira Khalid, S. Manzoor, Jisi Chandroth· Multimedia· 0 citations
Self-Supervised Learning (SSL) has emerged as an effective paradigm for reducing the dependence of Human Activity Recognition (HAR) models on labeled data. To address the inadequate exploitation of IMU spatio-temporal correlations during pre-training and the limited generalization caused by simplistic fine-tuning strat...
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...
