A Cross-Dataset Adaptive Hybrid Ensemble Framework for Robust Inertial Fall Detection

Falls remain a major healthcare concern, particularly among older adults, since they frequently result in serious injuries, prolonged rehabilitation, and reduced quality of life. Although numerous inertial sensor-based fall detection methods have been reported, many are validated on a single dataset, limiting their ability to generalise across diverse sensing environments in real-world applications. This study proposes a cross-dataset Adaptive Hybrid Ensemble Framework (AHEF) for robust wearable fall detection. The framework combines heterogeneous Machine Learning (ML) classifiers Random Forest (RF), XGBoost (XGB), Extra Trees (ET), and Elastic Net Logistic Regression (ENet-LR) within a probability-calibrated stacking architecture, followed by adaptive decision refinement using activity-aware thresholds and temporal confirmation. Performance was evaluated using three publicly available benchmark datasets (SisFall, MobiFall & KFall) under both subject-independent cross-validation and Leave-One-Dataset-Out (LODO) protocols. Experimental results indicate that the proposed framework achieved 98.7% accuracy, 99.1% sensitivity, and 98.3% specificity during within-dataset evaluation while maintaining strong cross-dataset performance, with average sensitivity exceeding 98% across unseen datasets. The framework also reduced the False Positive Rate (FPR) to 1.7%, compared with 4.0–6.4% obtained by the individual base classifiers, without compromising detection sensitivity. Overall, these findings demonstrate that combining cross-dataset evaluation with adaptive ensemble learning improves both prediction reliability and generalisation, supporting deployment of wearable fall detection systems in heterogeneous real-world environments.