Pose-Based Fall Detection With Robust Feature Analysis and Privacy-Aware Edge-Fog-Cloud Deployment

Abstract

Falls among older adults can lead to severe injury when assistance is delayed. Vision-based monitoring offers a contactless means of detecting falls, but deploying such systems in indoor environments raises important privacy and implementation challenges. This study presents a privacy-aware fall detection framework that combines pose-derived biomechanical features, wearable inertial sensing, and deployment-aware computation within an edge–fog–cloud architecture. The proposed approach is evaluated on three public datasets (UR Fall, UP-Fall, and Le2i). Static biomechanical features achieve an F1-score of 99.95% on UR Fall and above 91% on Le2i, whereas performance on the more challenging UP-Fall dataset reaches approximately 80% F1-score. The effect of temporal aggregation is found to depend on dataset characteristics, with larger temporal windows improving performance in some cases while providing little benefit in others. Cross-dataset evaluation reveals substantial performance degradation under dataset shift, indicating limited generalization across different recording conditions and participant populations. To investigate this behaviour, feature distributions are analysed using Jensen–Shannon divergence and Wasserstein distance, and robustness-oriented feature selection improves transfer performance compared with using the complete temporal feature set. Multimodal fusion of pose-based predictions with inertial sensing further improves recall and F1-score on the UP-Fall dataset. End-to-end deployment profiling on a Raspberry Pi 5 demonstrates that the complete inference pipeline, including pose estimation, biomechanical feature extraction, temporal feature aggregation, and classification, operates in real time on resource-constrained hardware. Deployment analysis using iFogSim further shows that privacy-aware placement strategies substantially reduce network communication while providing favourable trade-offs among latency, privacy, and resource utilization compared with cloud-centric execution. Overall, the study demonstrates that combining robust biomechanical features with multimodal sensing and deployment-aware resource management provides an effective foundation for practical privacy-preserving fall detection.