An Early Detection of Fall Using Knowledge Distillation Ensemble Prediction Using Classification

As the Global population ages, protecting the welfare of the elderly becomes a more pressing issue. The prompt diagnosis of falls, which are a major cause of injuries and fatalities among older individuals, is a crucial component of geriatric care. Early fall detection (EFD) systems are essential for giving prompt help and raising the standard of living for elderly people. Traditional fall detection algorithms often suffer from false positives, where non-fall events are incorrectly identified as falls, or false negatives, where actual falls are missed. Hence, researchers and developers are increasingly turning to more sophisticated machine-learning techniques to improve the precision and reliability of systems used for fall detection. Advanced machine learning approaches are being used to improve these systems’ accuracy and effectiveness, and one approach that is gaining popularity is the knowledge distillation ensemble. In this paper, we propose early fall detection in elderly people using the knowledge distillation ensemble (KDE) method to ameliorate the reliability and accuracy of the advanced machine learning approaches. We conducted experiments using our proposed method to detect falls using physiological parameters and we evaluated our work using metrics like accuracy, F1-measure, recall, and precision. Our proposed KDE algorithm has achieved 100% accuracy and the perfect score for precision, recall, and F1-measure.