Quantifying Postural Instability with Pose Estimation Software and 3D Depth Extraction

Abstract

Patients who suffer from Parkinson’s Disease are more prone to postural instability, a major risk factor for falls. One of the most common clinical methods of gauging the severity of a patient’s postural instability is with the retropulsion test [1], in which a clinician perturbs the balance of the patient and then rates their response to the perturbation. This test is subjective and largely based on the observations made by the clinician. In order to improve postural instability diagnosis and encourage more meaningful therapies for this cognitive-motor symptom, there is a clinical need to enable more objective, quantifiable approaches to measuring postural instability. In this paper, we describe a novel computational approach to quantifying the number, length, and trajectory of steps taken during a retropulsion test or other type of balance perturbation from a single camera facing the anterior side (front) of the subject. The computational framework involved first analyzing the video data using markerless pose estimation algorithms to track the movement of the subject’s feet. These pixel data were then converted from 2D to 3D using calibrated transformation functions, and then analyzed for consistency when compared to the known step lengths. The testing data showed accurate step length estimation within 1 cm, which suggests this computational approach could have utility in a variety of clinical environments.