Multiple sclerosis (MS) is a leading cause of neurological injury in young adults. Capturing the extent of multiple domains of MS-related disability is critical for effective clinical care and the development of new paradigms for patient-focused therapeutic approaches. To date outcomes research in MS has centered on clinical exams, which may be insensitive over the short term (the 1-2 years of early stage clinical trials) and only capture a single snapshot of the patient's performance.
With the mass production of sensors in the gaming and computer control industry, there is an opportunity to transform the traditional neurological exam with biosensors already in use outside the realm of health applications. The investigators herein propose to use a commercialized wearable electroMYOgraphy sensor (MYO,Thalamic Labs Inc, Kitchener, ON, Canada) for detection of upper and lower limb dysfunction in MS patients. The investigators will determine if the device can differentiate the diseased states, refine signal processing algorithms to create reliable outcomes using this device in MS patients, and determine if these outcomes are strongly associated with patients and physicians reported ambulatory and dexterity metrics. The investigators hypothesize that this digital technology may be introduced in the standard neurological exam technique in a non-disruptive manner and more accurately and potentially remotely detect both physician-reported and patient-reported disability.
In the scope of this study, the investigators will also develop signal processing methodology to comprehensively track ambulation features.
