Tackling Visual Illumination Variations in Fall Detection for Healthcare Applications

This paper presents an innovative approach for fall detection, a significant concern in elder care, using vision-based techniques and video analysis. By employing and comparing supervised machine learning algorithms for recognising falls, The paper examines the impact of different environmental conditions on the fall detection system, focusing on illumination, an aspect previously overlooked in the field. The study introduces a vision-based fall detection method using Human Pose Estimation (HPE) models, specifically MoveNet, for feature extraction from human gestures and temporal moving features. Selected machine learning algorithms and neural network models are then trained and compared using these features to recognise video events such as falls and non-falls. The presented results show promising 70.6% accuracy and real-time model efficiency. This study’s findings hold significant potential for enhancing timely fall detection in real-world scenarios.