IoT-Integrated Deep Learning Framework for Automated Human Fall Detection

Falls represent one of the leading causes of injury and mortality among elderly individuals and patients with mobility impairments. Early and accurate detection of fall events is critical to ensure timely medical assistance and reduce severe health consequences. This research proposes an IoT-Integrated Deep Learning Framework for Automated Human Fall Detection, designed to provide real-time monitoring, accurate fall classification, and immediate alert generation. The system combines wearable or vision-based IoT sensors with advanced deep learning models to identify abnormal human posture transitions and sudden impact events. The proposed framework utilizes sensor data such as accelerometer and gyroscope readings, or video streams captured through edge devices, which are transmitted via IoT communication protocols to a processing unit. A deep neural network—such as a Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM) model—is employed to extract spatial and temporal features for reliable fall recognition. The integration of cloud connectivity enables remote monitoring, data storage, and emergency notification to caregivers or healthcare providers. Experimental evaluation demonstrates high detection accuracy, low false alarm rates, and efficient real-time performance under diverse environmental conditions. Compared to traditional threshold-based methods, the proposed deep learning approach significantly improves classification robustness and adaptability. The system is scalable, energy-efficient, and suitable for deployment in smart homes, hospitals, and assisted living environments. Overall, the framework contributes to enhancing elderly care through intelligent, connected, and automated fall detection solutions.