NPJ Digital Medicine · Jul 15, 2026

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

By Wu P, Dong H, Ding Z et al.

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.

doi.org/10.1038/s41746-026-02988-6Open accessPubMed 42457912AI & data