IoT-Driven Fall Detection and Smart Monitoring System for Elderly Care

Epidemiological studies indicate that the incidence of accidental falls among older adults is significantly higher than previously estimated, representing a major public health concern. Falls are a leading cause of morbidity and mortality in the elderly population, particularly among individuals aged 75 years and above. Clinical data suggest that approximately 70% of injury-related deaths in this age group are associated with fall events. Furthermore, over 90% of hip fractures in older adults are attributed to falls, often resulting in prolonged hospitalization, reduced mobility, and increased risk of secondary complications. Earlier reports, including findings published in American Family Physician, indicate that nearly one-third of elderly individuals living independently experience at least one fall annually, while the prevalence increases to approximately 60% among nursing home residents. These statistics highlight the urgent need for proactive monitoring systems and awareness strategies aimed at early detection and timely intervention. In response to these challenges, this study proposes a multimodal IoT-driven fall detection and assistance system designed to enhance elderly safety through real-time monitoring and intelligent alert mechanisms. The system integrates multiple sensing modalities—including inertial measurement sensors for orientation and acceleration tracking, load sensors for weight distribution monitoring, and motion analysis modules—to accurately detect abnormal events indicative of falls. A microcontroller unit (MCU) serves as the central processing unit (CPU), interfacing with sensors and a Wi-Fi communication module to enable real-time data transmission to caregivers or emergency responders. The detection algorithm continuously analyzes parameters such as sudden changes in acceleration (impact force), abnormal body orientation, rapid velocity variations, and mass displacement patterns. By comparing real-time sensor readings with predefined thresholds and probabilistic models, the system identifies potential fall events and triggers an automated alert protocol. Upon fall detection, the microcontroller transmits notifications via wireless networks to designated contacts, healthcare providers, or cloud-based monitoring platforms. Bidirectional communication capabilities allow remote acknowledgment and system status updates.