Robust 3D Pose estimation and Parkinson’s Disease classification via Dual-Stage Adaptive Temporal Perception and graph topology modeling network

This paper proposes a unified skeleton-based framework for 3D human pose estimation and Parkinson’s disease classification, integrating a Dual-Stage Adaptive Temporal Perception (DATP) strategy and an Adaptive Graph Topology Modeling Network (AGTM-Net). DATP enhances robustness to joint occlusion and sequence degradation through occlusion-aware interpolation, trend-extrapolated frame padding, and multi-scale spatiotemporal modeling. On the MPI-INF-3DHP dataset with 16 missing joints, DATP achieves 77.72 PCK and 43.57 AUC, outperforming state-of-the-art methods. On Human3.6M, DATP also shows strong generalization with MPJPE reduced to 32.68 mm. For clinical classification, AGTM-Net dynamically models skeletal structure variations and achieves an F1-score of 0.898 and accuracy of 0.881 in distinguishing healthy individuals from Parkinson’s patients with a score of 0 based on the “3.9 Arising from Chair” task. Interpretability analyses—based on gradient and perturbation methods—highlight the spine, chest, and hips as decisive joints, aligning with clinical understanding of gait disorders and enhancing the model’s transparency and clinical reliability.