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<p>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 &amp; 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Florian Lange,
  • Philipp Köberle,
  • Gamze Adaçay,
  • Diego L. Guarin,
  • Jens Volkmann,
  • Robert Peach,
  • Martin M. Reich

摘要

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.