Mobile-Centric Supervised Machine Learning Approach for Elderly Fall Detection Using YOLOv8 - chotphornseema, khaninnat (Naresuan University Engineering Journal, 2024)
The global surge in the elderly population has underscored the need for advanced safety systems tailored to seniors, particularly those living independently. Central to these systems is the pivotal role of fall detection in ensuring their welfare. This paper presents a cutting-edge fall detection system designed specifically for the elderly, leveraging supervised machine learning techniques with a mobile-centric approach. Departing from traditional hospital-centric setups, our system offers cost-effectiveness and improved mobility, facilitating deployment across diverse environments. The methodology comprises three core stages: data collection and annotation, model training, and inference. We curated a dataset of 1500 images categorized into three classes: standing, falling, and fallen, meticulously annotated using RoboFlow. Subsequent model training utilized YOLOv8, culminating in the inference stage, which underwent quantitative evaluation employing 10-fold cross-validation, yielding an average accuracy of 97.88%. Qualitative assessment across four distinct scenarios further validated our system, achieving an average accuracy of 95.92%. These results underscore the efficacy of our approach and lay the foundation for practical implementation and widespread adoption. Subsequent to the successful development of the core algorithm, we operationalized it for real-world applications by seamlessly integrating it with smartphones via TensorFlowLite. This integration underscores the synergy between algorithm design and software development, further facilitating the practical deployment and widespread acceptance of our system in diverse settings