Multi-perspective videos and 3D poses capturing multi-task Parkinson’s disease assessments and deep brain stimulation treatment effects

Quantitative motor analysis is increasingly central to Parkinson's disease (PD) research and has the potential to transform PD clinical care. However, the field remains bottlenecked by datasets targeting coarse, manually-annotated PD severity scales rather than accurate PD motor feature identification or patient outcome prediction. Here, we introduce TULIP-Bench, the first PD movement dataset and benchmark to combine raw multi-perspective videos, triangulated markerless 3D pose ground truth, both fine and gross motor tasks with clinical ratings (bilateral hand movements and gait), and within-patient treatment responses to deep brain stimulation (DBS). The TULIP-Bench benchmark comprises tasks evaluating pose lifting and direct single-camera 3D pose tracking approaches in terms of clinical kinematic feature errors, quantities we found to be weakly associated with standard geometric (millimeter) error measures. We identified a critical perspective-dependent performance gap between single- and multi-camera PD assessments and illustrate how conventional PD clinical scores are unable to resolve DBS-induced gait changes. We also developed a self-supervised cross-cohort model for DBS response scoring based on representations learned from non-DBS PD patients. TULIP-Bench will catalyze a new generation of methods that emphasize clinical deployability, sensitive treatment-focused measurement, and patient outcome prediction.