Parkinson's Disease Detection from Gait Patterns

Parkinson's disease (PD) patients display abnormal gait patterns with impairments and postural instability. In this paper, we propose an automatic system for extracting gait parameters. Various features were extracted from force sensors and analyzed using a threshold-based algorithm and machine learning techniques with the objective to identify the most significant features that would best characterize the presence of the disease. A machine learning algorithm using support vector machine method was developed to identify the presence of the disease. The analyses of the results show that the machine learning algorithm has the best accuracy of 100% in distinguish between the two groups when looking at features based on stride, swing and stance phases.