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

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Methods › Per‑patient ranking via dynamic weighting ↔ StimVision/dbs_analysis_library/analysis.py, lines 39–110 · score 0.56 · Dynamically Weighted Improvement, Responsiveness scores, workflow, shrinkage, Raw, ranked
  2. [2] § Methods › Per‑patient ranking via dynamic weighting ↔ StimVision/dbs_analysis_library/analysis.py, lines 39–110 · score 0.54 · uniform weights, blends, shrinkage, Rankings, dynamic

Paper

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The authors' code

Python · 110 lines · 4.8 KB · no license · 2 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Core analysis functions for calculating improvement, dynamic weights, and
  5. ranking of DBS settings based on kinematic data.
  6. """
  7. import pandas as pd
  8. import numpy as np
  9. from .processing import sort_condition_key
  10. from .utils import parameter_directions
  12. def aggregate_task_data_by_hand(data_for_task, hand):
  13. """
  14. Aggregates kinematic data from multiple CSVs for a specific hand,
  15. averaging values across trials for each condition.
  16. """
  17. aggregated_data = {}
  18. if not isinstance(data_for_task, dict):
  19. return pd.DataFrame()
  21. for condition, hand_dict in data_for_task.items():
  22. if isinstance(hand_dict, dict) and hand in hand_dict:
  23. series_list = []
  24. for df in hand_dict[hand]:
  25. if isinstance(df, pd.DataFrame) and 'Attribute' in df.columns and 'Value' in df.columns:
  26. s = pd.to_numeric(df['Value'], errors='coerce')
  27. s.index = df['Attribute']
  28. series_list.append(s)
  30. if series_list:
  31. aggregated_data[condition] = pd.concat(series_list, axis=1).mean(axis=1)
  33. if not aggregated_data:
  34. return pd.DataFrame()
  35. summary_df = pd.DataFrame(aggregated_data).T
  36. summary_df = summary_df.reindex(sorted(summary_df.index, key=sort_condition_key)).dropna(axis=0, how='all')
  37. return summary_df
  39. def perform_full_hand_analysis(data_by_condition, hand, baseline, shrinkage_lambda=0.1):
  40. """
  41. Performs the complete analysis pipeline for a single hand using a robust,
  42. integrated workflow with regularized dynamic weighting.
  44. Args:
  45. data_by_condition (dict): Nested dict of data: {condition: {hand: [DataFrames]}}.
  46. hand (str): 'Left' or 'Right'.
  47. baseline (str): The name of the baseline condition (e.g., "Med Off - DBS Off").
  48. shrinkage_lambda (float): Regularization parameter (0 to 1). A small value
  49. like 0.1 (default) blends data-driven weights with
  50. uniform weights to prevent over-fitting to noise.
  52. Returns:
  53. dict: A dictionary containing all intermediate and final analysis results.
  54. """
  55. summary_df = aggregate_task_data_by_hand(data_by_condition, hand)
  57. results = {
  58. 'summary_df': summary_df, 'improvement_df': pd.DataFrame(),
  59. 'responsiveness_scores': pd.Series(dtype=float), 'final_dynamic_weights': pd.Series(dtype=float),
  60. 'dynamic_scores': pd.Series(dtype=float),
  61. 'dynamic_ranking_results': {'ranked_names': [], 'ranked_scores': []}
  62. }
  64. if summary_df.empty or baseline not in summary_df.index:
  65. print(f"Warning: No summary data for hand '{hand}' or baseline '{baseline}' not found. Aborting analysis for this hand.")
  66. return results
  68. on_conditions = summary_df.index.drop(baseline, errors='ignore')
  69. if on_conditions.empty:
  70. print(f"Warning: No 'ON' conditions found to compare against baseline for hand '{hand}'.")
  71. return results
  73. baseline_series = summary_df.loc[baseline]
  74. raw_change_df = summary_df.loc[on_conditions].subtract(baseline_series, axis=1)
  76. improvement_df = raw_change_df.copy()
  77. for param, direction in parameter_directions.items():
  78. if param in improvement_df.columns and direction == 'lower':
  79. improvement_df[param] *= -1
  80. results['improvement_df'] = improvement_df
  82. responsiveness_scores = improvement_df.std().fillna(0)
  83. responsiveness_scores = responsiveness_scores[responsiveness_scores > 1e-9] # Filter non-variable parameters.
  84. results['responsiveness_scores'] = responsiveness_scores
  86. if responsiveness_scores.empty or responsiveness_scores.sum() == 0:
  87. print(f"Warning: No responsive parameters found for hand '{hand}'. Cannot calculate dynamic scores.")
  88. return results
  90. # Apply regularized dynamic weighting.
  91. data_driven_weights = responsiveness_scores / responsiveness_scores.sum()
  92. n_responsive_params = len(data_driven_weights)
  93. uniform_weights = pd.Series(1.0 / n_responsive_params, index=data_driven_weights.index)
  95. final_dynamic_weights = (1 - shrinkage_lambda) * data_driven_weights + shrinkage_lambda * uniform_weights
  96. results['final_dynamic_weights'] = final_dynamic_weights
  98. # Compute the final Dynamically Weighted Improvement Score (DWIS).
  99. common_params = improvement_df.columns.intersection(final_dynamic_weights.index)
  100. dynamic_scores = improvement_df[common_params].dot(final_dynamic_weights[common_params])
  101. results['dynamic_scores'] = dynamic_scores
  103. # Rank conditions by DWIS.
  104. ranked_scores = dynamic_scores.sort_values(ascending=False)
  105. results['dynamic_ranking_results'] = {
  106. 'ranked_names': ranked_scores.index.tolist(),
  107. 'ranked_scores': ranked_scores.values.tolist(),
  108. }
  110. return results

analysis.py at commit 376f189, no license · at the source

Overview

Authors: Florian Lange1, Philipp Köberle1, Gamze Adaçay1, Diego L. Guarin2, Jens Volkmann1, Robert Peach1, Martin M. Reich1
  1. Department of Neurology, University of Würzburg,Würzburg, Germany
  2. Movement Estimation and Analysis Laboratory, Department of Applied Physiology and Kinesiology, University of Florida,Gainesville, FL USA
Institutions: University of Würzburg (Germany); University of Florida (United States)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 100
Dates: received 13 December 2025; accepted 18 March 2026; published online 20 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41531-026-01335-6 · PMID 42009683 · PMCID PMC13096112 · OpenAlex W7154937198
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), Parkinson's (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: Computational biology and bioinformatics, Neurology, Neuroscience
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (424778381, TRR 295); Interdisziplinäres Zentrum für Klinische Forschung, Universitätsklinikum Würzburg (Z-3BC/19)
Citations: not cited yet (Europe PMC); 13 references in the paper

Abstract

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Flolan2/StimVision_data

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 376f189775f0657d5a9edcf4a3d2c42dcd633d62, 24 February 2026
Languages: Python (5)
Size: 15 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: environment (StimVision/requirements.txt)
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (4 files), pandas (4 files), Matplotlib (1 file), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
5 files
At the source: github.com/Flolan2/StimVision_data

Code availability

The core VisionMD computer vision pipeline used for feature extraction is open-source and available at https://www.visionmd.ai/. The StimVision analysis pipeline used in this study is publicly available at https://github.com/Flolan2/StimVision_data.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The derived kinematic datasets and analysis matrices generated during the current study are publicly available at: https://github.com/Flolan2/StimVision_data.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 2 funders, 11 references.

Cite

This paper

Lange, F., Köberle, P., Adaçay, G., Guarin, D. L., Volkmann, J., Peach, R., & Reich, M. M. (2026). StimVision: smartphone video kinematics to optimize DBS programming in Parkinson's disease. NPJ Parkinson's disease, 12(1), 100. https://doi.org/10.1038/s41531-026-01335-6

BibTeX

@article{lange2026stimvision,
author = {Lange, Florian and Köberle, Philipp and Adaçay, Gamze and Guarin, Diego L. and Volkmann, Jens and Peach, Robert and Reich, Martin M.},
title = {{StimVision: smartphone video kinematics to optimize DBS programming in Parkinson's disease}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = apr,
volume = {12},
number = {1},
pages = {100},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01335-6},
url = {https://doi.org/10.1038/s41531-026-01335-6},
pmid = {42009683},
pmcid = {PMC13096112}
}

RIS

TY - JOUR
AU - Lange, Florian
AU - Köberle, Philipp
AU - Adaçay, Gamze
AU - Guarin, Diego L.
AU - Volkmann, Jens
AU - Peach, Robert
AU - Reich, Martin M.
TI - StimVision: smartphone video kinematics to optimize DBS programming in Parkinson's disease
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/04/20
VL - 12
IS - 1
SP - 100
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01335-6
UR - https://doi.org/10.1038/s41531-026-01335-6
LA - en
ER -

CSL-JSON

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"author": [
{
"family": "Lange",
"given": "Florian"
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"date-parts": [
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]
]
}
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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