TY  - EJOU
AU  - Gazem, Nadhmi A. 
AU  - Qasem, Sultan Noman 
AU  - Naeem, Umair 
AU  - Latif, Shahid 
AU  - Nafea, Ibtehal 
AU  - Saeed, Faisal 
AU  - Rehman, Mujeeb Ur 

TI  - Enhancing Fall Detection in Alzheimer’s Patients Using Unsupervised Domain Adaptation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Fall detection; Alzheimer’s Disease (AD); transfer learning; wearable sensors; KFall dataset

DO  - 10.32604/cmes.2025.066517