Decoding gait in individuals with spinal cord injury: From explainable AI to predictive simulations

QC260113

Abstract

While current biomechanics research based on normal models and assumptions of normalcy has substantial merit, it fails to reliably describe individuals with impairments. Spinal cord injury (SCI), whether traumatic or nontraumatic, can partially or completely damage sensorimotor pathways, leading to heterogeneous gait abnormalities. A substantial knowledge gap exists regarding biomechanical and neurological movement strategies in this population due to complex, interacting factors including age, weight, time since injury, pain, sensorimotor impairment, and spasticity. The ASIA Impairment Scale, while recommended for classifying injury severity, was not designed to characterize individual ambulatory capacity. Other standardized assessments based on subjective ratings or timing/distance measures have limited ability to characterize functional capacity in this population comprehensively.

This thesis therefore aims to create computational frameworks for studying walking strategies in individuals with SCI, particularly incomplete SCI (iSCI), through two complementary approaches: developing machine learning algorithms that link individual characteristics to gait outcomes, and individualizing objective functions and constraints in predictive simulations using neuromusculoskeletal modeling.

Study I proposed and evaluated a framework applying Gaussian Process Regression and SHapley Additive exPlanations (SHAP) to quantify how neurological impairments and other demographic and anthropometric factors contribute to walking speed and net Oxygen cost during a six-minute walk test. Individual SHAP analyses quantified how these factors influenced walking performance for each participant, informing personalized rehabilitation targeting areas with the most potential for improvement.

Study II stratified gait heterogeneity in individuals with iSCI by deriving clusters with similar gait patterns without a priori parameter identification and assessed clinical correlations within the derived clusters. Six distinct gait clusters were identified and characterized among 280 iSCI gait cycles, informing more individualized rehabilitation.

Study III characterized margin of stability, temporospatial parameters, and joint mechanics in four iSCI subgroups from Study II compared to participants without disability, identifying how gait adaptations evolve as muscle weakness affects major muscle groups. Gait patterns remained normal with isolated mild plantarflexor weakness but deteriorated with combined hip muscle weakness and severe plantarflexor weakness.

Study IV developed a bilevel optimization framework using Bayesian optimization to automatically identify optimal objective weights for predictive gait simulations in individuals with iSCI. Tested on one female participant with asymmetric muscle weakness, the framework successfully automated weight identification in 9-12 days and demonstrated that simulations with optimized weights outperformed literature-based reference weights for predicting kinematics, kinetics, and ground reaction forces, showing promise for systematically exploring personalized compensatory gait strategies with predictive simulations.

These findings demonstrate the potential of advanced data-driven and simulation techniques to address gait complexity in individuals with SCI, with broader applicability to other clinical populations.

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