Timely Identification of Falls in Seniors: AI-Driven Approaches for Intelligent Healthcare Systems

Over the span of a year, falls among elderly individuals account for millions of injuries, putting a strain on medical systems worldwide. The application of artificial intelligence (AI) in healthcare has shown great promise, particularly in early fall detection for older adults, which is essential to minimize injuries, lower healthcare costs, and improve quality of life. This study focuses on developing an AI-driven, cloud-based fall detection and prediction system utilizing wearable technology, IoT-connected sensors, and machine learning techniques. Wearable devices equipped with accelerometers, gyroscopes, and inertial measurement units (IMUs) are enhanced using algorithms such as random forests and convolutional neural networks (CNNs) to detect abnormal gait patterns and predict ‘prefall’ scenarios. To address challenges such as energy inefficiency, high false positives, and data privacy, federated learning on edge devices has been employed for privacy-preserving data sharing. The proposed system improves accuracy, energy efficiency, and system robustness while ensuring cost-effectiveness. Results demonstrate significant advance in real-time fall detection and prediction, reducing false positives, and optimizing energy usage. Aligning with global healthcare objectives, this study provides a scalable and sustainable solution to enhance well-being and reduce healthcare disparities. This system empowers healthcare providers with proactive tools for fall prevention and improves the quality of life of older adults. This research adds value to AI in healthcare by addressing existing limitations and advancing practical privacy-preserving solutions. The broader implications include global scalability and integration of AI-driven fall detection systems into sustainable healthcare practices.