Design Objective: To address the need for accessible take-at-home ND exams, our team is focusing on providing viable alternatives to a portion of the current exam. The portion we are focusing on is a test specifically for motor function because this section of the exam can take advantage of increasingly adaptive machine learning solutions to track patients’ physical movement in a virtual setting. This exam would reduce the time and money patients need to spend to access a doctor, while also giving the doctors accurate measurements to aid in diagnosis for NDs. We aim to create a device that is software-based, potentially with a physical component, accessible, and accurate in the detection of motor changes. Such a technology would speed up consultations and give the neurologist more quantitative information from which to arrive at a diagnosis.
Application is built for cross-platform support using Python and Kivy.
Members: Alex Estrada, Sahand Salari-Namin, Gabriella Bond, Lauren Demele, Kelsey Krist