STAG-Net: A Spatial–Temporal Attention-Guided Network for Pathological Gait Classification

Abstract

Human gait is a complex motor process regulated by the nervous system, where even subtle alterations in stride symmetry, stance duration or joint coordination directly reflect underlying motor and neurological impairments. Consequently, gait analysis provides a highly sensitive, objective, and non-invasive approach for clinical diagnosis and continuous monitoring. With the increasing global prevalence of neurological and motor impairments, there is a rising need for scalable objective methods capable of supporting diagnosis, monitoring progression and guiding rehabilitation. In this work, we present a Spatial–Temporal Attention-Guided Network (STAG-Net) for pathological gait classification using the GAIT-IT dataset, including five gait types associated with different problems: scissor, spastic, steppage, propulsive, and normal. The dataset provides a challenging benchmark characterised by class imbalance, high inter-class similarity, and significant intra-subject variability. To address these challenges, we develop a hybrid architecture combining Convolutional Neural Networks for spatial feature extraction with Recurrent Neural Networks to model temporal gait dynamics. We propose a Bidirectional Long Short-Term Memory module that captures gait dynamics, enabling the model to better represent cyclical and symmetrical gait characteristics. Furthermore, we integrate a temporal attention mechanism to dynamically weight salient gait phases, supporting the network’s ability to discriminate between visually similar gait abnormalities. The resulting STAG-Net framework achieves 96.82% classification accuracy, demonstrating robustness in capturing gait differences across simulated pathological gait categories and highlighting its potential as a foundation for future extension toward real-world gait monitoring applications, pending validation on clinical patient populations.