Augmenting Subjective Assessments with Objective Metrics for Neuromuscular Disorders

Spasticity is a debilitating neuro-muscular disorder which is characterized by involuntary muscle movements and stretch reflexes. There is a large population of the world who suffers with spasticity due to various diseases like Cerebral Palsy, Multiple Sclerosis, Spinal Cord Injury, Traumatic Brain Injury etc. The diagnosis standards of spasticity for treatment prescription are highly subjective, most of them either heavily based on the clinicians’ “feel”, on voluntary movements by patients or mounting sensors on patients with no defined correlation to the assessment standards. Hence, they have high inter- and intra-rater variability. Spasticity is diagnosed every few weeks whereas extent of spasticity can vary more frequently. Thus, a subjective assessment which does not account for the dynamic nature of spasticity is not a good measure for treatment. Moreover, the account of patients and their family affect the treatment as well. Treatment effectiveness and costs can vary highly based on inaccurate assessment. This calls for a dire need of an objective, consistent and repeatable scale for spasticity assessment. For this purpose, we have developed an instrumented glove in hopes that it will give such an assessment. The research of this Thesis describes the development of this glove, the algorithms to obtain an assessment measure and techniques to validate said glove. We also intend to make this glove so it instruments the clinicians or raters rather than the patients. This comes out of a consideration of both convenience to the patients and their finances