A Fast Mobile‐Based Elderly Fall Detection Method Using Neural Networks in the Internet of Things

With the rise of the Internet of Things (IoT), mobile devices have become essential for real‐time human activity monitoring, particularly in elderly care. This study presents a lightweight and privacy‐preserving fall detection approach using a single smartphone accelerometer, eliminating the need for intrusive cameras, which are impractical in private spaces such as bathrooms and culturally sensitive regions like Iran and other Muslim countries. We proposed an enhanced multilayer perceptron model optimized for mobile deployment, trained on a novel dataset of fewer than 10,000 labelled actions collected via Android smartphones. The model achieved 99.76% accuracy on this dataset and demonstrated strong generalizability with 98.28% and 98.49% accuracy on the UP‐Fall and SisFall datasets, respectively. This approach offers a practical and culturally sensitive solution for real‐time fall detection in IoT‐based elderly monitoring systems.