An Automatic Assessment of Parkinson’s Disease in Arising from Chair Task via Refined Diffusion-based Pose Estimator

Parkinson’s disease (PD) is a progressively common neurodegenerative disorder characterized by a decline in motor function. The diagnosis of PD typically relies on the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), which involves subjective scoring through observation of targeted movements. However, this objective method heavily depends on professional experience and has relatively high misdiagnosis rates. In this paper, we introduce a novel vision-based architecture for automated assessment of the ‘arising from chair’ task, which is one of the key MDS-UPDRS components. First, a diffusion-based 2D pose estimator is proposed to enhance keypoint accuracy by iteratively learning the distribution of ground-truth data and then denoising noisy poses. Second, a keypoint trajectory refinement network is introduced to eliminate the jitter error by considering motion information such as position, velocity, acceleration, and jerk. Finally, based on the predicted skeleton keypoint trajectories, we propose several objective indicators to assess the movement characteristics and perform the final rating using the classifier. The experiment substantiates the proposed algorithm, achieving a precision of 98.7% and an accuracy of 95.8% in classifying the ‘arising from chair’ task. Furthermore, the classification results and the proposed objective indicators have been validated as effective aids for neurologists to provide more precise diagnoses.