Towards Safer Aging: A Comprehensive Edge Computing Approach to Unconsciousness and Fall Detection
Ezaz Mahmud Jim, Md. Awinul Hoque Utsha, Fabiha Nawal Aurna, Anuradha Choudhury, Mohammad Akidul Hoque · 2025
Inevitably the population around the world ages, there has become rising concern for the welfare and well-being of seniors. Fall and unconsciousness are perilous problems that can result either serious harm or even fatalities. The short-comings of conventional monitoring techniques, like caregiver supervision, call for the creation of cutting-edge technological alternatives. The current research provides an in-depth approach for recognizing fall and unconsciousness in aged people using edge computing, artificial intelligence (AI), and sensor networks. Among the main problems that the proposed method addresses are reliability, environmental instability, integration of sensors, real time processing, and concerns related to privacy. The system continuously monitors the body and its systems for data and physical movements using a range of sensors, including gyroscopes and accelerometers. The data collected from the sensors is examined using sophisticated machine learning models, such as Support Vector Machine and Logistic Regression, to determine falls and prolonged immobility that are symptomatic of unconsciousness. Through the integration of edge computing, unprocessed information processing takes place locally on edge devices, improving privacy and decreasing latency. The technology instantly sends out alerts upon detecting an occurrence, enabling swift intervention. Delicate information is used ethically and is protected using privacy protection like data anonymization and secure transfer. By providing a scalable and effective way to improve safety and quality of life, this solution marks a significant leap in the care of the elderly and paves the path for the wider acceptance and implementation of AI-driven monitoring technology.