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

• URL: http://arxiv.org/abs/2607.25791v1
• Date: Tue, 28 Jul 2026 14:43:07 GMT
• Title: FLASH: Efficient Impact Fall Detection with Unified Hypergraph State-Space Model
• Abstract summary: Existing methods rely on graph neural networks modeling only pairwise joint connections.<n>We propose FLASH, a novel framework integrating single-matrix hypergraph representations with Mamba's selective state-space models.<n>Experiments on UP-Fall and UMAFall datasets demonstrate that FLASH achieves state-of-the-art accuracy with real-time inference capability.
• Score: 1.2731778729620478
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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