PREDICTIVE FALL DETECTION IN ELDERLY CARE USING MACHINE LEARNING MODELS

Abstract

Falls are among the leading causes of injury, hospitalization, disability, and mortality among elderly individuals worldwide. As populations continue to age, the need for intelligent healthcare systems capable of monitoring elderly individuals and providing timely interventions has become increasingly important. Traditional fall detection methods often rely on manual observation or emergency reporting, which may delay assistance and increase the risk of severe health consequences. Recent advancements in wearable sensors, Internet of Things (IoT) devices, artificial intelligence, and machine learning technologies have enabled the development of automated fall detection systems that continuously monitor physical activities and identify potential fall events in real time. These technologies offer significant opportunities for improving elderly safety, reducing healthcare costs, and enhancing quality of life. This study investigates the application of machine learning models for predictive fall detection in elderly care environments. The proposed system utilizes wearable sensors such as accelerometers and gyroscopes to collect motion and activity data from elderly individuals. The collected sensor data undergo preprocessing, feature extraction, and classification processes before being analyzed using machine learning algorithms including Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost, and Long Short-Term Memory (LSTM) networks. These algorithms are trained to recognize movement patterns associated with normal daily activities and fall-related incidents. The research examines the effectiveness of machine learning techniques in predicting falls before or immediately after their occurrence. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F1-score, sensitivity, and detection rate. Existing studies demonstrate that machine learning-based fall detection systems can achieve high classification accuracy while significantly reducing false alarms. Furthermore, the integration of wearable technologies and intelligent analytics enables continuous health monitoring and rapid emergency response capabilities. Despite substantial progress, challenges remain regarding sensor reliability, energy efficiency, privacy protection, and real-world deployment. Future developments involving deep learning, edge computing, federated learning, and smart healthcare ecosystems are expected to further enhance predictive capabilities and system reliability. The study concludes that machine learning-based predictive fall detection systems represent a promising solution for improving elderly care by enabling proactive healthcare monitoring, early intervention, and enhanced patient safety.