Hierarchical multi-task learning for comprehensive gait assessment using wearable inertial sensors

Quantitative gait analysis with wearable inertial sensors holds substantial promise for scalable screening, diagnosis, and monitoring of neurological and musculoskeletal disorders. However, existing approaches predominantly adopt a single-task learning (STL) paradigm, training separate models for each clinical objective, which limits multi-objective coverage, parameter efficiency, prediction coherence, and cross-domain generalisability. Here we present H-MTL, to our knowledge the first hierarchical multi-task learning framework to unify as many as ten heterogeneous gait analysis tasks (pathological screening, multi-granularity disease classification, severity regression, and demographic estimation) within a single 0.61-million-parameter model, enforcing prediction coherence through a hierarchy consistency loss aligned with the clinical diagnostic tree. Under subject-wise 10-fold cross-validation on 260 subjects with seven gait pathologies, H-MTL achieves 85.0 ± 4.1% screening accuracy with approximately 14-fold fewer parameters than ten separate models. Cross-dataset validation on four external cohorts (456 subjects, 34 endpoints) spanning diverse sensors, modalities, and populations reveals selective transfer, with gains concentrated on motor-severity and neurodegenerative classification while demographic endpoints remain scratch-competitive. These results position H-MTL as a lightweight, coherence-aware methodological proof-of-concept, providing a principled foundation for comprehensive clinical gait assessment from body-worn inertial sensors.