A Mobile-Integrated IoT Framework for Women’s Security and Real-Time Threat Notification

Epidemiological research indicates that the prevalence of accidental falls among older adults is substantially higher than previously reported, posing a significant public health challenge. Falls are recognized as a leading cause of morbidity and mortality in the elderly population, particularly in individuals aged 75 years and above. Clinical statistics reveal that approximately 70% of injury-related deaths within this demographic are associated with fall incidents, while more than 90% of hip fractures in older adults result from falls. These events often lead to prolonged hospitalization, reduced mobility, increased susceptibility to secondary health complications, and a consequent decline in quality of life. Previous studies, including reports published in American Family Physician, have demonstrated that nearly one-third of independently living elderly individuals experience at least one fall per year, with the prevalence rising to approximately 60% among nursing home residents. Such alarming trends underscore the necessity for proactive monitoring and intervention strategies to ensure timely response and risk mitigation. In response to these challenges, this study proposes the design and implementation of a multimodal IoT-driven fall detection and assistance system aimed at enhancing elderly safety through real-time monitoring and intelligent alert mechanisms. The system employs multiple sensing modalities to ensure robust detection, including inertial measurement units (IMUs) for tracking body orientation and acceleration, load sensors to monitor weight distribution and pressure changes, and motion analysis modules to identify abnormal movement patterns indicative of falls. A microcontroller unit (MCU) serves as the central processing hub, interfacing with the sensors and a Wi-Fi communication module to enable real-time transmission of data to caregivers, emergency responders, or cloud-based monitoring platforms. The embedded detection algorithm continuously evaluates key parameters such as sudden acceleration spikes (impact force), abnormal body orientation, rapid velocity fluctuations, and mass displacement dynamics. By comparing these real-time measurements against predefined thresholds and probabilistic fall models, the system can accurately discriminate between normal activities and potential fall events. Upon detection, the MCU triggers an automated alert protocol, transmitting instant notifications to designated contacts or healthcare providers via wireless networks. The platform also supports bidirectional communication, allowing remote acknowledgment, verification of the individual’s status, and real-time system monitoring, thereby enhancing both reliability and user trust. By integrating IoT-enabled sensing, advanced analytics, and automated alert mechanisms, the proposed system represents a scalable and intelligent solution for fall prevention and rapid response in elderly care settings, ultimately reducing injury risk and improving health outcomes.