DistillH-Mamba: A Hypergraph-Mamba-Based Knowledge Distillation Model for Efficient Impact Fall Detection

2026-07-03

AI Summary

Accurate identification of the precise moment of impact during elderly falls remains challenging, as existing methods often involve high computational complexity and are difficult to deploy in real-time scenarios. This work proposes DistillH-Mamba, a novel architecture that uniquely integrates hypergraph neural networks with the Mamba state space model to effectively capture high-order inter-joint relationships, long-range temporal dependencies, and abrupt motion changes. To further enhance efficiency, the authors introduce a relational knowledge distillation strategy that preserves critical spatiotemporal structural information while significantly compressing the model. Evaluated on the UP-Fall and UMAFall datasets, the proposed method achieves an impact detection accuracy of 97.38% and reduces inference time by 73.8% compared to the teacher model, substantially outperforming current state-of-the-art approaches.

Abstract

Falls among the elderly represent a significant public health concern due to their prevalence, consequences, and societal burden. While deep learning has improved fall detection, accurately identifying impact moments (when an individual hits the ground) remains challenging. Additionally, current algorithms often rely on complex models with high computational demands, limiting real-time deployment feasibility. In this work, we propose DistillH-Mamba, a novel architecture for impact fall detection that addresses these challenges through three key innovations: First, we introduce a hypergraph-based approach that captures higher-order relationships between multiple joints simultaneously, enabling more accurate modeling of complex interactions during impact falls. Second, we integrate the Mamba architecture with hypergraphs for impact detection, significantly accelerating processing speed while efficiently capturing both long-term dependencies and sudden skeletal motion changes. Third, we employ relational knowledge distillation that preserves crucial spatial-temporal relationships while reducing computational demands for real-time impact fall detection. Evaluated on the 3D Skeletons UP-Fall and UMAFall datasets, our DistillH-Mamba model achieves 97.38% accuracy in detecting impact within fall events and 73.8% reduction in inference time compared to its teacher model, outperforming state-of-the-art methods in both precision and efficiency.

Problem

• impact fall detection
• elderly falls
• computational efficiency
• real-time deployment
• deep learning

Innovation

• hypergraph
• Mamba architecture
• knowledge distillation
• impact fall detection
• skeletal motion modeling