TSDGNet: Multiscale Temporal and Dual-Graph Learning for Pathological Gait Classification Using Wearable Inertial Sensors

Abstract

Wearable inertial sensors offer a portable means of assessing gait abnormalities associated with neurological and orthopedic disorders. However, intersubject variability, overlapping gait patterns, and class imbalance complicate multiclass recognition. This study presents the Temporal Structural Dual Graph Network (TSDGNet) for pathological gait classification using inertial measurement unit (IMU) signals. Four IMUs positioned on the head, lower back, and both feet provide acceleration and angular velocity signals during walking and turning. The framework combines multiscale temporal feature extraction with graph-based modeling of sequence-level relationships among sensor representations. Two fixed prior graphs encode anatomical and symbiotic relationships, while trainable graph attention and context-guided gated residual refinement adapt sensor-node representations. Class-balanced optimization is incorporated to reduce training bias toward majority classes. Subject-independent evaluation was performed on a public clinical dataset comprising healthy controls and seven neurological or orthopedic disease groups. Comparisons with representative baseline and advanced models showed competitive classification performance. Ablation analyses examined the contributions of temporal modeling, structural relationship learning, and class-balanced optimization. These findings support the potential of combining wearable inertial sensing with temporal and graph-based learning for objective classification of pathological gait.