Reducing Fall Risks with Machine Learning-Based Detection Systems in Elderly Care

Abstract

Falls among the elderly are a major concern, with approximately 37.3 million falls requiring medical attention globally each year. In the United States alone, the Centers for Disease Control and Prevention (CDC) reports that one in four older adults experiences a fall annually. This results in significant healthcare costs, projected to reach $101 billion by 2030. The pressing need for effective fall detection systems is clear to mitigate these risks and enhance elderly care. Traditional fall detection methods often rely on manual monitoring and reporting, which can be both labor-intensive and prone to errors. Common approaches include the use of wearable devices or call buttons, which require active engagement from the user and may not always capture falls in real-time. Additionally, these methods lack accuracy and fail to differentiate between falls and other sudden movements, leading to false alarms
or missed detections. So, proposed machine learning (ML) offers a promising solution by leveraging data from IoT to provide accurate fall detection. ML algorithms can analyze patterns in sensor data to identify fall events with high precision, reducing false alarms and improving response times. By integrating these systems with existing IoT infrastructure, elderly care can be significantly enhanced,
providing both preventive measures and immediate assistance, ultimately improving quality of life and reducing healthcare costs.