Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for Sensory Ataxia Diagnosis
This provides an objective diagnostic tool for medical professionals to detect sensory ataxia, though it is incremental as it automates an existing test.
The paper tackled diagnosing sensory ataxia by automating the Romberg Test using a CNN and center of mass analysis, achieving a mean absolute error of 0.2912% for weight distribution and 83.33% accuracy in diagnoses.
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.