Machine Learning-Based Estimation of POMA Scores from EMG and Kinematic Data in Stroke Patients

A stroke is a critical medical disease characterized by the abrupt cessation of blood flow to the brain, resulting in cellular injury or necrosis. The effects of stroke on people might range from minor impairments to significant disabilities. Stroke treatment often requires gait rehabilitation. The efficacy of rehabilitative treatment is often assessed using clinical scales. Among them, the Performance-Oriented Mobility Assessment (POMA) is commonly used for assessing balance and gait in stroke patients. Importantly, evaluating muscle activation and kinematic patterns by electromyography (EMG) and stereophotogrammetry during ambulation might provide insights into gait impairments. This study intends to use a machine learning-based regression to predict the POMA total score using EMG and kinematic data in stroke patients. The model achieved correlations of 0.70 and 0.67 throughout the validation and testing stages, respectively. The t-test indicated no bias between the estimated and measured POMA values, but the Bland-Altmann plot revealed a systematic error in the model. Although preliminary, these results indicate the potential to construct models that might provide significant assistance to clinicians by offering accurate evaluations of motor deficits reported post-stroke.