Continuous Identification of Freezing of Gait in Parkinson’s Patients Using Artificial Neural Networks and Instrumented Shoes

Abstract

Freezing of gait is an episodic phenomena faced by many patients with Parkinson’s disease. It is characterized by episodes during which patients are unable to generate effective forward stepping movements, despite absence of motor deficits. During the onset of the event, the patients are less stable with statistically different stride width, toe in/out angle and center of pressure distance. It has been postulated that the degree of freezing can be reduced by providing external sensory feedback to the patients during the event. However, this intervention could be facilitated by accurate identification of freezing events in real-time. This manuscript presents an Artificial Neural Network model which uses signals recorded by an instrumented footwear to predict if a walking subject is having a freezing episode. Our model presented in this paper is capable of continuously predicting freezing of gait events at a high temporal resolution of 50 Hz, using a 0.5 second window of data recorded by the instrumented shoes, with a sensitivity of 96.0±2.5%, a specificity of 99.6 ± 0.3%, a precision of 89.5 ± 5.9%, and an accuracy of 99.5 ± 0.4%. This algorithm was tested with data collected from 10 patients with Parkinson’s disease with frequent freezing of gait episodes.