@Article{cmes.2025.066517,
AUTHOR = {Nadhmi A. Gazem, Sultan Noman Qasem, Umair Naeem, Shahid Latif, Ibtehal Nafea, Faisal Saeed, Mujeeb Ur Rehman},
TITLE = {Enhancing Fall Detection in Alzheimer’s Patients Using Unsupervised Domain Adaptation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {144},
YEAR = {2025},
NUMBER = {1},
PAGES = {407--427},
URL = {http://www.techscience.com/CMES/v144n1/63284},
ISSN = {1526-1506},
ABSTRACT = {Falls are a leading cause of injury and morbidity among older adults, especially those with Alzheimer’s disease (AD), who face increased risks due to cognitive decline, gait instability, and impaired spatial awareness. While wearable sensor-based fall detection systems offer promising solutions, their effectiveness is often hindered by domain shifts resulting from variations in sensor placement, sampling frequencies, and discrepancies in dataset distributions. To address these challenges, this paper proposes a novel unsupervised domain adaptation (UDA) framework specifically designed for cross-dataset fall detection in Alzheimer’s disease (AD) patients, utilizing advanced transfer learning to enhance generalizability. The proposed method incorporates a ResNet-Transformer Network (ResT) as a feature extractor, along with a novel DualAlign Loss formulation that aims to align feature distributions while maintaining class separability. Experiments on the preprocessed KFall and SisFall datasets demonstrate significant improvements in F1-score and recall, crucial metrics for reliable fall detection, outperforming existing UDA methods, including a convolutional neural network (CNN), DeepCORAL, DANN, and CDAN. By addressing domain shifts, the proposed approach enhances the practical viability of fall detection systems for AD patients, providing a scalable solution to minimize injury risks and improve caregiving outcomes in real-world environments.},
DOI = {10.32604/cmes.2025.066517}
}