Real-time Streaming of Gait Assessment for Parkinson's Disease

Abstract

Patients with progressive neurological disorders such as Parkinson's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS) suffer both chronic and episodic difficulties with locomotion. Real-time assessment and visualization of sensor data can be valuable to physicians monitoring the progression of these conditions. We present a system that utilizes the attention based bi-directional recurrent neural network (RNN) presented in [2] to evaluate foot pressure sensor data streamed directly from a pair of sensors attached to a patient. The demonstration also supports indirect streaming from recorded sessions, such as those stored in a FHIR [1] enabled electronic medical records repository, for post-hoc evaluation and comparison of a patient's gait over time. The system evaluates and visualizes the streamed gait in a real time web interface to provide a personalized normality rating that highlights the strengths and weaknesses of a patient's gait.