Towards a Comprehensive Solution for a Vision-based Digitized Neurological Examination

TL;DR

This paper introduces a versatile, smartphone-based digital neurological exam system called DNE that captures, analyzes, and visualizes movement data to aid clinical assessment and monitoring of neurological conditions.

Contribution

It presents a modular, accessible software solution for digitizing neurological exams using video capture and pose estimation, expanding clinical and at-home assessment options.

Findings

Achieved over 90% accuracy in classifying upper-limb movements.

Achieved over 80% accuracy in stand-up and walk tests.

Demonstrated the system's effectiveness on a dataset of 21 subjects.

Abstract

The ability to use digitally recorded and quantified neurological exam information is important to help healthcare systems deliver better care, in-person and via telehealth, as they compensate for a growing shortage of neurologists. Current neurological digital biomarker pipelines, however, are narrowed down to a specific neurological exam component or applied for assessing specific conditions. In this paper, we propose an accessible vision-based exam and documentation solution called Digitized Neurological Examination (DNE) to expand exam biomarker recording options and clinical applications using a smartphone/tablet. Through our DNE software, healthcare providers in clinical settings and people at home are enabled to video capture an examination while performing instructed neurological tests, including finger tapping, finger to finger, forearm roll, and stand-up and walk. Our modular design of the DNE software supports integrations of additional tests. The DNE extracts from the recorded examinations the 2D/3D human-body pose and quantifies kinematic and spatio-temporal features. The features are clinically relevant and allow clinicians to document and observe the quantified movements and the changes of these metrics over time. A web server and a user interface for recordings viewing and feature visualizations are available. DNE was evaluated on a collected dataset of 21 subjects containing normal and simulated-impaired movements. The overall accuracy of DNE is demonstrated by classifying the recorded movements using various machine learning models. Our tests show an accuracy beyond 90% for upper-limb tests and 80% for the stand-up and walk tests.