SkelMamba: A State Space Model for Efficient Skeleton Action Recognition of Neurological Disorders
This work addresses the problem of efficient and accurate action recognition for clinical diagnostics and general tasks, with incremental improvements in performance and complexity.
The paper tackled skeleton-based human action recognition by introducing a state-space model framework with an anatomically-guided architecture, achieving up to 3.2% accuracy improvements on public benchmarks and lower computational complexity than transformer-based models.
We introduce a novel state-space model (SSM)-based framework for skeleton-based human action recognition, with an anatomically-guided architecture that improves state-of-the-art performance in both clinical diagnostics and general action recognition tasks. Our approach decomposes skeletal motion analysis into spatial, temporal, and spatio-temporal streams, using channel partitioning to capture distinct movement characteristics efficiently. By implementing a structured, multi-directional scanning strategy within SSMs, our model captures local joint interactions and global motion patterns across multiple anatomical body parts. This anatomically-aware decomposition enhances the ability to identify subtle motion patterns critical in medical diagnosis, such as gait anomalies associated with neurological conditions. On public action recognition benchmarks, i.e., NTU RGB+D, NTU RGB+D 120, and NW-UCLA, our model outperforms current state-of-the-art methods, achieving accuracy improvements up to $3.2\%$ with lower computational complexity than previous leading transformer-based models. We also introduce a novel medical dataset for motion-based patient neurological disorder analysis to validate our method's potential in automated disease diagnosis.