RGB-D Human Pose Estimation to Assess Instability in Parkinson’s Disease: a Multi-center Evaluation

Postural instability is a major source of disability in Parkinson’s disease, as impaired balance increases fall risk and accelerates loss of independence in daily life. In clinical practice, instability is commonly assessed through the pull test during outpatient visits. However, this task is difficult to replicate in telemedicine settings, where patients must perform evaluations at home, often without direct clinical supervision. Therefore, extracting quantitative markers of instability from simple standing tasks may provide a practical proxy for conventional assessment. This approach requires technologies capable of detecting subtle center-of-mass oscillations through non-invasive, easy-to-use systems. In this study, we evaluate a deep learning–based human pose estimation framework using an RGB-D camera to monitor patients during two 60-second standing tasks (eyes open and eyes closed). Kinematic features related to postural sway are estimated from body pose and correlated with reference clinical scales. Data from 40 patients recruited across two clinical centres show moderate agreement with clinical scores (Spearman ρ = 0.49), confirming the efficacy of the acquisition technology. Moreover, results suggest that 20 s recording with eyes closed may be sufficient to capture clinically relevant stability parameters.