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

arXiv: https://arxiv.org/abs/2607.03156 · HTML: https://mlsanity.com/paper/2607.03156/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Video, motion and generative Mamba models, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Placed in

- State-space sequence models (S4, Mamba) / [Video, motion and generative Mamba models](https://mlsanity.com/ssm/video-motion-and-generative-mamba-models/)

## Builds on (most cited first, 10 of 10)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [A Survey of Mamba](https://mlsanity.com/paper/2408.01129/) (2024)
- [A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships](https://mlsanity.com/paper/2408.15178/) (2024)
- [Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow](https://mlsanity.com/paper/2104.05744/) (2021)
- [PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation](https://mlsanity.com/paper/2303.03634/) (2023)
- [Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data](https://mlsanity.com/paper/2401.05407/) (2023)
- [Joint Temporal Pooling for Improving Skeleton-based Action Recognition](https://mlsanity.com/paper/2408.09356/) (2024)
- [An Improved 3D Skeletons UP-Fall Dataset: Enhancing Data Quality for Efficient Impact Fall Detection](https://mlsanity.com/paper/2502.19048/) (2025)
- [SDFA: Structure Aware Discriminative Feature Aggregation for Efficient Human Fall Detection in Video](https://mlsanity.com/paper/2503.07008/) (2025)

## Built on by (most cited first, 1 of 1)

- [FLASH: Efficient Impact Fall Detection with Unified Hypergraph State-Space Model](https://mlsanity.com/paper/2607.25791/) (2026)

## Abstract (arXiv)

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.