StimVision: smartphone video kinematics to optimize DBS programming in Parkinson's disease

Deep brain stimulation (DBS) improves motor function in Parkinson's disease, yet programming remains labor-intensive and largely subjective. We evaluated a smartphone video-based kinematic framework (StimVision) for objective, within-session optimization of DBS settings and characterization of therapeutic motor signatures. Fifteen patients with subthalamic DBS performed repetitive hand opening-closing while multiple stimulation programs were tested in the medication-off state. Markerless pose estimation from 60 fps smartphone video generated 23 quantitative kinematic features. A patient-specific Dynamically Weighted Improvement Score (DWIS) ranked programs by composite improvement relative to DBS-off. The framework identified a unique optimal program for each patient, with robust ranking stability. Group-level improvements at the optimal setting were dominated by gains in speed and rhythm metrics, including mean velocity, closing speed, and movement frequency, alongside reduced intra-sequence decay. Sparse principal component analysis revealed three kinematic domains-Movement Speed, Movement Consistency, and Rhythm & Timing. Structural comparison with a levodopa cohort demonstrated substantial overlap in speed and consistency domains but divergence in timing-related features. Smartphone-based kinematics enable objective DBS optimization and provide a shared quantitative framework for comparing electrical and pharmacological therapies.