UCLA

Degenerative spinal disorders such as cervical spondylotic myelopathy (CSM) or lumbar spinal stenosis (LSS) are caused by many factors including prolonged and inappropriate sitting positions, heavy labor, and genetically inherited predisposition. These ailments are closely related to the aging process of the human body, and their populations are growing rapidly in developed nations. Thus, there is a great need for a simple, inexpensive, reliable, and objective assessment system.

This dissertation introduces a system for assessing hand motor dysfunction, which is a common symptom of CSM, using a mobile handgrip device with simple target tracking tests. Data collected from $30$ CSM patients and $30$ age-matched control subjects is used to validate the system's three fundamental quantification objectives using a reliable feature selection technique: detecting the severity of hand motor deficits, correlation to the patients' perceived motor deficits, and detecting the changes in motor conditions as a result of medical treatment (e.g., surgical operation). Then, this dissertation introduces a more in-depth algorithm that quantifies the level of hyperreflexia, which is an unique symptom of CSM patients closely related to their fine motor controllability. This thesis further discusses a prediction algorithm that estimates the postoperative functional outcomes of CSM patients using their clinical, demographic, and preoperative functional (handgrip) information. The handgrip device has also been tested on patients with other types of neuromotor ailments such as cerebral vascular accident (CVA) and chronic inflammatory demyelinating polyneuropathy (CIDP) through a pilot study. A technical solution to quantification of motor function for patients suffering from lower back spinal disorders (e.g., LSS) is briefly discussed as a future work.