Camera- and Viewpoint-Agnostic Evaluation of Axial Postural Abnormalities in People with Parkinson’s Disease through Augmented Human Pose Estimation

Abstract

Axial postural abnormalities (aPA) are common features of Parkinson’s disease (PD) and manifest in over 20% of patients during the course of the disease. aPA form a spectrum of functional trunk misalignment, ranging from a typical Parkinsonian stooped posture to progressively greater degrees of spine deviation. Current research has not yet led to a sufficient understanding of pathophysiology and management of aPA in PD, partially due to lack of agreement on validated, user-friendly, automatic tools for measuring and analysing the differences in the degree of aPA, according to patients’ therapeutic conditions and tasks. In this context, human pose estimation (HPE) software based on deep learning could be a valid support as it automatically extrapolates spatial coordinates of the human skeleton keypoints from images or videos. Nevertheless, standard HPE platforms have two limitations that prevent their adoption in such a clinical practice. First, standard HPE keypoints are inconsistent with the keypoints needed to assess aPA (degrees and fulcrum). Second, aPA assessment either requires advanced RGB-D sensors or, when based on the processing of RGB images, they are most likely sensitive to the adopted camera and to the scene (e.g., sensor–subject distance, lighting, background–subject clothing contrast). This article presents a software that augments the human skeleton extrapolated by state-of-the-art HPE software from RGB pictures with exact bone points for posture evaluation through computer vision post-processing primitives. This article shows the software robustness and accuracy on the processing of 76 RGB images with different resolutions and sensor–subject distances from 55 PD patients with different degrees of anterior and lateral trunk flexion.

Botulinum Toxin for Axial Postural Abnormalities in Parkinson’s Disease: A Systematic Review

“…Despite these advancements in consensus-based classification and assessment, the pathophysiological mechanisms driving APAs in PD still need to be completely understood, limiting our knowledge on the development of effective treatment strategies [19,[26][27][28][29][30][31][32][33][34]. The comprehension of APAs pathophysiology in PD is partial, with two main hypothesized mechanisms involving central (e.g., imbalance in basal ganglia functioning leading to dystonia/rigidity, higher cognitive function deficits, and altered sensory-motor integration) and peripheral factors such as myopathy, and alterations in the spine or soft tissues [2,.…”