Quantifying Demographic, Anthropometric, and Neurological Impairment Effects on Walking Performance in People With Spinal Cord Injury.

Spinal cord injury (SCI) can impair sensorimotor pathways and reduce walking ability. The effects of sensorimotor impairments are complex, as they interact with other factors such as age, pain, injury level, and injury severity. Traditional regression analyses have been used to describe relationships, but they frequently assume linearity. Explainable AI methods such as SHapley Additive exPlanations (SHAP), based on cooperative game theory, can reveal feature importance globally and locally. Gaussian Process Regression (GPR) can handle limited datasets, a common challenge in observational clinical studies with small sample sizes. In this study, we proposed and evaluated a framework applying GPR and SHAP to quantify how neurological impairments and other factors, including muscle strength, sensory function, age, pain, spasticity, etc., contribute to walking performance, specifically walking speed and net oxygen cost during a six-minute walk test. This approach estimates each factor's contribution both on a group level and for each individual. Thirty four adults with SCI underwent a clinical assessment and the six-minute walk test with preferred walking aids if relevant. Muscle strength was the most influential factor in both walking speed and net oxygen cost. Male sex, lower age, and less pain were associated with increased walking speed. More pain, higher body mass index, and higher sensory score were associated with lower net oxygen cost. Spasticity, injury level and sensory injury levels had relatively small influence on either outcome measure. Individual SHAP analyses quantified how neurological factors influenced walking performance for each participant. We demonstrate how nonparametric regression and explainable AI provide insights into the complex neurological factors affecting walking ability in persons with SCI.