FLASH: Efficient Impact Fall Detection with Unified Hypergraph State-Space Model

2026-07-28

AI Summary

This work addresses the limitations of existing skeleton-based fall detection methods, which struggle to accurately model the multi-joint coordination patterns at the moment of impact and suffer from high computational costs due to complex temporal modeling, hindering real-time deployment. To overcome these challenges, we propose a novel approach that integrates biomechanically informed hypergraph structures with the Mamba selective state space model. Our method leverages a single-matrix hypergraph guided by biomechanical priors and employs an adaptive feedback mechanism to efficiently capture both joint coordination and temporal dynamics. By uniquely combining biomechanics-driven hyperedges with a linear-complexity state space model, the proposed framework achieves state-of-the-art accuracy on the UP-Fall and UMAFall datasets while maintaining real-time inference capability, low computational overhead, and strong zero-shot cross-dataset generalization performance.

Abstract

Falls represent a critical public health challenge, and accurate detection of the impact moment when an individual hits the ground is crucial for timely intervention. Existing skeleton-based methods rely on graph neural networks modeling only pairwise joint connections, failing to capture multi-joint coordination characteristic of fall impacts, while transformer-based temporal models suffer from quadratic complexity limiting real-time deployment. We propose FLASH, a novel framework integrating single-matrix hypergraph representations with Mamba's selective state-space models through adaptive feedback mechanisms for efficient impact detection. Our approach constructs biomechanically-grounded hyperedges to model functional joint coordination while leveraging Mamba's linear-time complexity to capture temporal dynamics. Experiments on UP-Fall and UMAFall datasets demonstrate that FLASH achieves state-of-the-art accuracy with real-time inference capability and strong zero-shot cross-dataset generalization, while significantly reducing computational cost compared to dual-representation and transformer-based methods. The model provides interpretable feedback through learned attention patterns aligned with biomechanical principles. Code is available at https://github.com/Tresor-Koffi/FLASH-Impact-Fall-Detection.

Problem

• fall detection
• impact moment
• multi-joint coordination
• real-time deployment
• skeleton-based methods

Innovation

• hypergraph
• state-space model
• fall detection
• Mamba
• biomechanical coordination