Derya, SerhatGözüm, AlperenElmi, SoheilaElmi, ZahraGüney, Gökhan2026-05-252026-05-2520261863-17111863-1703https://hdl.handle.net/123456789/732https://doi.org/10.1007/s11760-026-05239-zFalls are a major public health risk for older adults, yet practical fall-detection systems must be accurate, low-cost, and deployable on resource-constrained devices. We propose an event-triggered hybrid pipeline where an abnormal-acceleration threshold activates a camera module, and a two-stage model (YOLOv5 person localization followed by an eight-layer CNN) verifies falls from the cropped region of interest. Experiments use a public dataset with a predefined Train/Val split (374/111), treating Val as a held-out test set; labels (Fall/Walking/Sitting) are mapped to Fall vs. Non-Fall. To prevent leakage, offline augmentation is applied only to the training data (374 ->\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ ightarrow $$\end{document} 1092 effective images), while the held-out test set remains unchanged; hyperparameters are selected using an internal split of the training partition only. On the held-out test set (72 Fall, 39 Non-Fall), the proposed system achieves 80.2% accuracy, 84.7% sensitivity, and 84.7% F1-score. The deployed INT8 model requires 2.3 MB and runs at 14.8 FPS with 125ms end-to-end capture-to-decision latency on an embedded camera platform, enabling timely mobile alerts. The dataset does not provide demographic metadata; thus, stratified analyses are out of scope.eninfo:eu-repo/semantics/closedAccessESP32 Camera ModuleEmbedded Deep LearningWearable SensorsMobile HealthFall DetectionEmergency AlarmArticle10.1007/s11760-026-05239-z2-s2.0-105038071301