Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for Sensory Ataxia Diagnosis

TL;DR

This paper introduces an automated Romberg Test using CNN-based joint detection and center of mass analysis to objectively diagnose sensory ataxia with high accuracy and low error.

Contribution

It presents a novel combination of CNN, bio-mechanical markers, and data filtering techniques for automated sensory ataxia diagnosis, improving objectivity and accuracy.

Findings

Mean absolute error of 0.2912% in weight distribution estimation

83.33% accuracy in diagnosis

Validated with dual weight scales and medical videos

Abstract

This paper proposes a novel method to diagnose sensory ataxia via an automated Romberg Test - the current de facto medical procedure used to diagnose this condition. It utilizes a convolutional neural network to predict joint locations, used for the calculation of various bio-mechanical markers such as the center of mass of the subject and various joint angles. This information is used in combination with data filtering techniques such as Kalman Filters, and center of mass analysis which helped make accurate inferences about the relative weight distribution in the lateral and anterior-posterior axes, and provide an objective, mathematically based diagnosis of this condition. In order to evaluate the performance of this method, testing was performed using dual weight scales and pre-annotated diagnosis videos taken from medical settings. These two methods both quantified the veritable weight distribution upon the ground surface with a ground truth and provided a real-world estimate of accuracy for the proposed method. A mean absolute error of 0.2912 percent was found for the calculated relative weight distribution difference, and an accuracy of 83.33 percent was achieved on diagnoses.