Exploring Characteristic Features in Gait Patterns for Predicting Multiple Sclerosis

Multiple Sclerosis (MS), an autoimmune and demyelinating disease, is one the most prevalent neurological disabilities in young adults. It results in damage of the central nervous system, disrupting communication between the patient's brain, spinal cord and body. Mobility limitations is one of the earliest symptoms and affects a majority of persons with Multiple Sclerosis. We are working towards an effort to characterize individuals with MS, from those without, on the basis of variations in the gait patterns. In the proposed work, statistical methods were used to identify differentiating gait data features for MS characterization. The prediction algorithms built upon these characteristic features will help clinicians develop effective and early cure and therapy designs for persons with Multiple Sclerosis.