ATLAS: Adaptive Thresholding and Learning Algorithms for Sensor-based Fall Detection Systems.
International Research Journal of Modernization in Engineering Technology and Science · 2025
Falls represent a significant health risk, particularly among elderly populations, with potentially severe consequences including physical injury, psychological trauma, and reduced quality of life.This research introduces ATLAS, a novel approach to fall detection utilizing wearable Inertial Measurement Unit (IMU) sensors combined with adaptive thresholding techniques.Unlike conventional systems with static detection parameters, ATLAS employs dynamic algorithmic adjustments that continually calibrate to individual user movement patterns, significantly reducing false alarms while maintaining high detection sensitivity.Our methodology leverages a multi-stage processing pipeline that combines signal preprocessing, feature extraction, and personalized threshold adaptation through machine learning techniques.ATLAS seamlessly integrates with advanced healthcare systems through standardized communication protocols, enabling automated emergency response coordination and integration with electronic health records for comprehensive patient monitoring.The system provides healthcare professionals with detailed fall analytics and movement pattern data, supporting evidence-based interventions and treatment plans.Experimental validation conducted with participants across diverse age groups demonstrates that the proposed approach substantially outperforms traditional fixed-threshold methods in both sensitivity and specificity metrics.The system's lightweight computational architecture enables real-time processing on resource-constrained wearable devices while maintaining extended battery operation.This work addresses critical limitations in current fall detection systems by implementing a personalized approach that adapts to individual gait patterns and movement characteristics, providing a reliable foundation for preventative healthcare monitoring in both clinical and home environments.