Generating Models of Human Gait in Patients with Parkinson’s Disease

Abstract

Parkinson’s disease is an extremely debilitating condition where the brain is not producing enough dopamine to accurately coordinate movement. One symptom of Parkinson’s disease, freezing of gait, prevents the affected person from either starting to walk or continuing walking. It usually begins in the advanced stages of the disease. The primary medication for Parkinson’s disease, Levodopa, is only partially effective for the treatment of freezing of gait. The dataset studied in this thesis provides time-series gait data of individuals’ gait while performing four different tasks, each having increased complexity over the previous ones. This thesis looks at a time-series gait dataset and performs symbolic regression through genetic programming on that dataset to predict fall likelihood and to create models of the gait of people with and without Parkinson’s disease including people who may be experiencing freezing of gait while factoring in their medication status (ON or OFF). The fall prediction experiment suggests that the GP models can predict the likelihood of falling based on the individual’s gait. The models provide insights into how Parkinson’s disease and freezing of gait impact gait patterns in people who have the disease vs. those who do not and enables us to compare the gait of individuals in different groups. It was found that, as expected, gait was similar within groups and different between groups. We also found that for some individuals it was not possible to distinguish between ON and OFF medication states. Future work might include determining the best models for each individual or group, attempting to find a model that accurately represents the individual or group rather than the individual trials.