Comprehensive analysis of feature-algorithm interactions for fall detection across age groups via machine learning

Abstract

Fall detection in daily activities hinges on both feature selection and algorithm choice. This study delves into their intricate interplay using the Sisfall dataset, testing 10 machine learning algorithms on 26 features encompassing diverse falls and age groups. Individual feature analysis yields key insights. RFC with the autocorrelation feature outperformed the other classifiers, achieving 97.94% accuracy and 97.51% sensitivity (surpassing F3-SVM at 96.18% and F17-LightGBM at 95.79%). The F3-SVM exhibited exceptional specificity (98.72%) for distinguishing daily activities. Time-series features employed by SVM achieved a peak accuracy of 98.60% on unseen data, exceeding motion, basic statistical, and frequency domain features. Feature combinations further excel: the Quintuple approach, fusing top-performing features, reaches 98.69% accuracy, 98.28% sensitivity, and 99.08% specificity with the ETC, demonstrating notable sensitivity owing to its adaptability. This study underscores the crucial interplay of features and algorithms, with the Quintuple-ETC approach emerging as the most effective. Rigorous hyperparameter tuning strengthens its performance in real-world fall-detection applications. Furthermore, the study investigates algorithm transferability, training models on young participants' data and applying them to the elderly—a significant challenge in machine learning. This highlights the importance of understanding the data transfer between age groups in healthcare, aging management, and medical diagnostics.