Evaluation of a Novel Statistical Method for EMG-to-Moment Estimation During Gait

Neuromusculoskeletal models have the potential to improve the design of clinical interventions for disorders such as stroke and osteoarthritis that affect walking function. Application of such models to clinical problems will likely require model customization to the unique anatomical and neurological conditions of each patient. Unfortunately, current modeling methods make model customization to patient data difficult, especially for model parameter values related to muscle-tendon models and musculoskeletal geometry [1]. Improved methods are needed so that patient-specific neural control capabilities and limitations can be easily incorporated into predictive gait optimizations to be used for intervention planning purposes.