IoT and Machine Learning-Embedded Fall Detection System

Falls are a major cause of medical problems among the elderly, often leading to severe injuries or fatalities, particularly for individuals living alone. Immediate medical attention is critical to minimizing the risk of severe injuries following a fall. While existing fall detection technologies, such as webcams and wearable devices, offer solutions, they come with significant limitations. Webcams are costly to install and operate, are restricted to indoor use, and raise privacy concerns. Wearable devices, such as pendants and wristbands, can restrict movement and frequently produce false alarms due to unintended device motions. This research proposes a cost-effective and reliable IoT and Machine Learning (ML)-embedded fall detection system to address these challenges. The system leverages IoT-enabled sensors to monitor the user's movements in real time, while ML algorithms analyze sensor data to accurately identify fall events. Upon detecting a fall, the system automatically alerts nearby healthcare centers or family members to provide immediate support. The proposed solution offers advantages such as affordability, privacy preservation, and adaptability to various environments, both indoors and outdoors. By eliminating the need for invasive or restrictive wearable devices, the system enhances user comfort and reduces the likelihood of false alarms. This innovative approach demonstrates the potential to significantly improve elderly safety and quality of life while providing a scalable framework for future advancements in fall detection technologies.