A machine learning-based method that examines walking abnormalities in people with multiple sclerosis (MS) could help identify patients who are at high risk of worsening symptoms, a study suggests.
The study, “Predicting Multiple Sclerosis from Gait Dynamics Using an Instrumented Treadmill – A Machine Learning Approach,” was published in Institute of Electrical and Electronics Engineers Transactions on Biomedical Engineering.
Difficulty walking is one of the most common symptoms of MS. However, walking abnormalities can be difficult to measure objectively — each person’s gait is distinct, as it is affected by the particular mechanics of their body. Plus, walking patterns tend to change with age, and it can be difficult to distinguish age-related changes from disease-driven abnormalities in older people with MS.