OSCR

Coordinated hand movement sensation revealed through an implanted magnetic prosthetic kinesthetic interface.

Code ↔ Paper

8 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 8 matches
  1. [1] § MATERIALS AND METHODS › Minimum pulse duration experiment ↔ TimeVibrations/PerceptionTimeAnalysis.m, lines 1–8 · score 0.71 · minimum perceptible pulse, adaptive sequence, staircase, duration, vibrations
  2. [2] § RESULTS › The character of perceived kinesthetic sensation ↔ TimeVibrations/PerceptionTimeAnalysis.m, lines 1–8 · score 0.66 · minimum perceptible pulse, adaptive sequence, duration threshold, staircase, vibration
  3. [3] § MATERIALS AND METHODS › Data analysis of the kinematics data from MKk-P1, TMRk-P2, and TMRk-P5 ↔ HandRenderings/mainSimulator.m, lines 90–130 · score 0.63 · joint angles, ab, imported, DIP, PIP, MCP
  4. [4] § MATERIALS AND METHODS › Data analysis of the kinematics data from MKk-P1, TMRk-P2, and TMRk-P5 ↔ HandRenderings/mjhaptix150/apicpp/haptix.h, lines 226–305 · score 0.56 · relative positions, distance, angular, kinematic, vectors, frame
  5. [5] § MATERIALS AND METHODS › Exploring the kinesthetic perception when MKk-P1 was naïve to magnetically induced stimulation ↔ KinestheticHandEvents/HandSensationSearchSpaceComplete.m, lines 1–9 · score 0.53 · search space, S2A, hotspots, event, Gaussian, kinesthetic
  6. [6] § MATERIALS AND METHODS › Minimum pulse duration experiment ↔ TimeVibrations/PerceptionTimeAnalysis.m, lines 44–91 · score 0.52 · pulse duration threshold, sequence, stimulation, median, vibration
  7. [7] § MATERIALS AND METHODS › Exploring the kinesthetic perception when MKk-P1 was naïve to magnetically induced stimulation ↔ KinestheticHandEvents/HandSensationSearchSpaceComplete.m, lines 30–35 · score 0.52 · square wave, search space, sensation, kinesthetic
  8. [8] § MATERIALS AND METHODS › Statistical analysis ↔ Likert/LikertCombined.m, lines 1–11 · score 0.51 · Wilcoxon signed rank, nonparametric, median

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 213 lines · 7.8 KB · no license · 3 matches

  1. %% FINAL SCRIPT ANALYSIS – Time Thresholds
  2. % Computes minimum perceptible pulse duration thresholds per site from
  3. % staircase/adaptive sequences; visualizes sequences and summarizes samples.
  4. %
  5. % This code generates Fig. 5A,b and fig. S5
  7. clc; clear; close all;
  8. warning off % Not ideal — prefer fixing root causes or using warning('once',...)
  10. %% ---------------- Figure / Graph Settings ----------------
  11. % External viz toolbox (daboxplot)
  12. addpath(genpath("frank-pk-DataViz-3.2.3.0"));
  14. % Color palettes per site (proximal / distal) as HEX
  15. FCUhex = ["#AF3C60", "#DF483A"];
  16. FPLhex = ["#F3C23A", "#277778"];
  17. EDhex = ["#265F79", "#82B4BC"];
  19. % Convert HEX -> RGB triplets for plotting robustness
  20. FCUcolor = [hex2rgb(FCUhex(1)); hex2rgb(FCUhex(2))];
  21. FPLcolor = [hex2rgb(FPLhex(1)); hex2rgb(FPLhex(2))];
  22. EDcolor = [hex2rgb(EDhex(1)); hex2rgb(EDhex(2)) ];
  24. % Row order: FCU-P, FPL-P, ED-P, FCU-D, FPL-D, ED-D
  25. Colors = [FCUcolor(1,:); FPLcolor(1,:); EDcolor(1,:); ...
  26. FCUcolor(2,:); FPLcolor(2,:); EDcolor(2,:)];
  28. % greys and accents
  29. grey = [0.3 0.3 0.3];
  30. forestgreen = [0 0.6 0.5];
  31. skyblue = [0.35 0.7 0.9];
  32. vermilion = [0.8 0.4 0.0]; %#ok<NASGU>
  34. SiteNames = ["FCU-P"; "FPL-P"; "ED-P"; "FCU-D"; "FPL-D"; "ED-D"];
  35. numSites = numel(SiteNames);
  37. % Plot axis defaults for sequence panels (tweak as needed)
  38. xlimSeq = [0 60];
  39. ylimSeq = [0 550];
  41. %% ---------------------- Load Data Path -------------------
  42. addpath(genpath("StimulationData"));
  44. %% -------------------- Threshold Analysis -----------------
  45. NumMin = 3; % number of final minima used
  46. samplesPer = 2*NumMin; % minima and their preceding samples
  48. thArray = NaN(numSites,1); % one threshold per site
  49. samplArray = NaN(samplesPer, numSites); % 6×6: rows=samples, cols=sites
  51. % Tiled layout: 6 sequence panels + 1 legend row
  52. figure('Units','normalized','Position',[0.1 0.2 0.7 0.6], 'Color', 'w');
  53. t1 = tiledlayout(7,3);
  54. title(t1, "Pulse Duration Adaptive Sequences");
  55. t1.Title.FontName = 'times';
  56. t1.Title.FontSize = 18;
  57. t1.Title.FontWeight= 'bold';
  59. for i = 1:numSites
  60. fileName = "StimulationTimingsSite" + num2str(i) + ".mat";
  61. S = load(fileName); % expects variable 'StimulationTimings'
  62. data = S.StimulationTimings(:).'; % ensure row vector
  64. [th, samplIdx] = ComputeThreshold(data, NumMin);
  65. thArray(i) = th;
  66. samplArray(:,i)= data(samplIdx).'; % store the 2*NumMin sample values
  68. % Sequence panel
  69. graph_sequence(data, th, samplIdx, Colors(i,:), SiteNames(i), xlimSeq, ylimSeq);
  71. % Hide y-axis for columns 2 and 3 (keep column 1)
  72. if mod(i,3) ~= 1
  73. ax = gca; ax.YAxis.Visible = 'off';
  74. end
  75. end
  77. % Legend row
  78. nexttile([1 3]);
  79. plot(nan, 'LineStyle','-','Color', grey, 'LineWidth',0.5, 'Marker','o', ...
  80. 'MarkerFaceColor', grey, 'MarkerEdgeColor', grey, 'DisplayName',"Sequence");
  81. hold on
  82. scatter(nan, nan, 500, 'filled', 'Marker','square', ...
  83. 'MarkerEdgeColor', grey, 'MarkerFaceColor', grey, 'DisplayName',"Samples");
  84. yline(nan, 'Color', [0.6 0.6 0.6], 'LineStyle','--', 'LineWidth',1, 'DisplayName',"Threshold");
  85. hold off
  86. axis off
  87. legend('Orientation','horizontal','Location','north', ...
  88. 'FontSize',14,'FontName','times');
  90. fprintf('Mean of the duration thresholds: %.2f ms\n', mean(thArray,'omitnan'));
  91. fprintf('Median of the duration thresholds: %.2f ms\n', median(thArray,'omitnan'));
  93. %% -------------------- Summary Boxplots --------------------
  94. % Group index: 6 groups (sites), 6 samples per group
  95. group_inx = reshape(repmat(1:numSites, samplesPer, 1), [], 1);
  96. dataVec = samplArray(:);
  98. % Colors for the 6 groups (numeric RGB, matching earlier order)
  99. ColorsBox = [ ...
  100. 0.6862745098039216 0.23529411764705882 0.3764705882352941; % FCU-P
  101. 0.9529411764705882 0.7607843137254902 0.22745098039215686; % FPL-P
  102. 0.14901960784313725 0.37254901960784315 0.4745098039215686; % ED-P
  103. 0.8745098039215686 0.2823529411764706 0.22745098039215686; % FCU-D
  104. 0.15294117647058825 0.4666666666666667 0.47058823529411764; % FPL-D
  105. 0.5098039215686274 0.7058823529411765 0.7372549019607844]; % ED-D
  107. figure('Units','normalized','Position',[0.2 0.2 0.25 0.5], 'Color','w');
  109. % Right y-axis: cycles at baseHz (default 90 Hz)
  110. % baseHz = 90; % adjust if needed
  111. % yyaxis right
  112. % ylabel('# cycles','FontSize',12)
  113. % ax = gca; ax.FontName = 'times';
  114. % % Show 0..5 cycles mapped into ms (1 cycle = 1000/baseHz ms)
  115. % msTicks = (0:5) * (1000/baseHz);
  116. % ylim([0 62]); yticks(msTicks); yticklabels(string(0:5));
  118. % Left y-axis: durations
  119. % yyaxis left
  120. daboxplot(dataVec, 'groups', group_inx, 'colors', ColorsBox, ...
  121. 'whiskers', 0, 'scatter', 1, 'jitter', 0, ...
  122. 'scattersize', 13, 'scatteralpha', 0.5, 'mean', 1, 'boxwidth', 1.5);
  123. ylabel('Duration (ms)','FontSize',12)
  124. ylim([0 62]); yticks(0:10:60);
  125. ax = gca; ax.FontName = 'times';
  126. title('Samples around last minima (per site)');
  128. %% ----------------------- Helper functions ------------------------
  130. function [th, samplIdx] = ComputeThreshold(data, NumMin)
  131. % Compute threshold from the last NumMin local minima and their preceding samples.
  132. % RETURNS:
  133. % th = median of the 2*NumMin samples (ms)
  134. % samplIdx = 1×(2*NumMin) indices used to compute threshold
  136. if nargin < 2, NumMin = 3; end
  137. data = data(:).'; % row vector
  139. % Local minima indices
  140. mins = find(islocalmin(data));
  142. % Guard: need at least NumMin minima
  143. if numel(mins) < NumMin
  144. % Fallback: use last 2*NumMin points if not enough minima
  145. lastN = min(2*NumMin, numel(data));
  146. samplIdx = numel(data)-lastN+1 : numel(data);
  147. th = median(data(samplIdx));
  148. return;
  149. end
  151. % Take the last NumMin minima
  152. mins = mins(end-NumMin+1:end);
  154. % Preceding samples (guard against index 1)
  155. prev = max(mins - 1, 1);
  157. % Combine and sort unique indices
  158. samplIdx = sort(unique([prev, mins]));
  160. % If we somehow got fewer than 2*NumMin (e.g., duplicate at index 1), pad forwards
  161. while numel(samplIdx) < 2*NumMin && samplIdx(end) < numel(data)
  162. samplIdx(end+1) = samplIdx(end) + 1; %#ok<AGROW>
  163. end
  165. th = median(data(samplIdx));
  166. end
  168. function graph_sequence(data, th, samplIdx, color, titlestr, xlimSeq, ylimSeq)
  169. % Plot a single sequence panel in the tiled layout.
  170. t = nexttile([3,1]);
  171. plot(data, 'LineStyle','-', 'Color',color, 'LineWidth',0.5, ...
  172. 'Marker','o', 'MarkerSize',3, ...
  173. 'MarkerFaceColor', color, 'MarkerEdgeColor', color, ...
  174. 'DisplayName',"Sequence"); hold on
  175. scatter(samplIdx, data(samplIdx), 60, 'filled', ...
  176. 'Marker','square', 'MarkerEdgeColor', color, ...
  177. 'MarkerFaceColor', color, 'MarkerFaceAlpha', 0.5, ...
  178. 'DisplayName',"Samples");
  179. yline(th, 'Color', color, 'Linestyle','--', 'LineWidth',1, ...
  180. 'DisplayName',"Threshold");
  181. hold off
  183. xlabel('Stimulus number','FontSize',12)
  184. ylabel('Duration (ms)','FontSize',12)
  185. title(titlestr)
  187. % Safe label positions (handle short sequences gracefully)
  188. tailN = min(30, numel(data));
  189. tailMax = max(data(max(1, end-tailN+1):end));
  190. text(numel(data), tailMax + 0.22*range(ylimSeq), ...
  191. "Median = " + num2str(th,'%.1f'), ...
  192. "HorizontalAlignment","right","FontSize",8,"Color",[0.6 0.6 0.6]);
  193. text(numel(data), tailMax + 0.08*range(ylimSeq), ...
  194. "Deviation = " + num2str(max(data(samplIdx)) - min(data(samplIdx)),'%.1f'), ...
  195. "HorizontalAlignment","right","FontSize",8,"Color",[0.6 0.6 0.6]);
  197. ylim(ylimSeq); xlim(xlimSeq);
  198. % t.YGrid = 'on';
  199. ax = gca; ax.FontName = 'times';
  200. end
  202. function rgb = hex2rgb(hex)
  203. % Convert "#RRGGBB" (or "RRGGBB") to [r g b] in 0..1
  204. hex = char(hex);
  205. if hex(1) == '#', hex = hex(2:end); end
  206. if numel(hex) ~= 6, error('hex2rgb: bad hex color "%s"', hex); end
  207. r = hex(1:2); g = hex(3:4); b = hex(5:6);
  208. rgb = [hexpair2num(r), hexpair2num(g), hexpair2num(b)] / 255;
  209. end
  211. function out = hexpair2num(p)
  212. out = hex2dec(p);
  213. end

PerceptionTimeAnalysis.m at commit 2c16efa, no license · at the source

Overview

Authors: Federico Masiero1,2,3, Mattia Gentile1,2, Marta Gherardini1,2, Eliana La Frazia1,2, Charles H Moore4, Ülgen Kilic4,5, Valerio Ianniciello1,2, Roberta Reho1,2, Tommaso Mori1,2, Flavia Paggetti1,2, Lorenzo Andreani6, Simon A Whitton4, Paul D Marasco4,7, Christian Cipriani1,2
ORCID iDs: Federico Masiero, Mattia Gentile, Marta Gherardini, Eliana La Frazia, Charles H Moore, Ülgen Kilic, Valerio Ianniciello, Roberta Reho, Tommaso Mori, Lorenzo Andreani, Simon A Whitton, Paul D Marasco, Christian Cipriani
  1. The BioRobotics Institute Scuola Superiore Sant’Anna, Pisa 56127, Italy
  2. Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa 56127, Italy
  3. Technical University of Munich, Germany; TUM School of Computation, Information and Technology, Department of Computer Engineering, Chair of Intelligent Bio-Robotic Systems; Munich Institute of Robotics and Machine Intelligence (MIRMI), Garching 85748, Germany
  4. Laboratory for Bionic Integration, Department of Biomedical Engineering, Cleveland Clinic Research, 9500 Euclid Avenue, ND20, Cleveland, OH 44195, USA
  5. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029,USA
  6. Orthopaedics and Traumatology Unit, University Hospital of Pisa, Pisa 56124, Italy
  7. Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Desk S-51, Cleveland, OH 44195, USA
Institutions: Scuola Superiore Sant'Anna (Italy); Technical University of Munich (Germany); Cleveland Clinic (United States); Icahn School of Medicine at Mount Sinai (United States); University of Pisa (Italy); Azienda Ospedaliera Universitaria Pisana (Italy)
Journal: Science advances, volume 12, issue 26, article eadx5046
Dates: received 17 March 2025; accepted 13 May 2026; published online 24 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.adx5046 · PMID 42341125 · PMCID PMC13292950 · OpenAlex W7165743648
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Connectivity
MeSH: Hand*, Kinesthesis*, Prostheses and Implants*, Sensation*, Humans, Movement (* major topic)
Topic: Muscle activation and electromyography studies (Biomedical Engineering, Engineering), according to OpenAlex
Funding: European Research Council (679820)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Muscle contractions to control prosthetic hands do not feel like those to control natural hands because amputation decouples movement sense (kinesthesia) from movement execution. The myokinetic kinesthetic interface (MKkI) uses remote vibration of permanent magnets implanted in amputated forearm muscles to restore kinesthesia. A participant reported coordinated finger movements of hand closing and opening, constrained within physiological bounds, with stereotypical conformations and dynamics. Aggregated unstructured explorations and systematic psychophysics exposed unreported perceptual sensitivity at vibration frequencies that trigger kinesthetic sensations. Complex coordinated grip sensations elicited from single forearm muscles reveal that kinesthetic brain representations are likely rooted in perception of synergistic movement production. By leveraging the natural synergistic features of kinesthesia, the MKkI will help link perception and action to functionally elevate emerging intuitive bidirectional human-machine interfaces.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

Zenodo 17260640

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (10 files), Curve Fitting Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
76 files
At the source: dx.doi.org/10.5281/zenodo.17260640

apeparrot/myokinetic-kinesthetic-interface

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2c16efad4731f06211f7e1d606dad7c1da4e9c74, 3 October 2025
Languages: MATLAB (72), C++ (2), C/C++ (1)
Size: 218 files, 75 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: Statistics and Machine Learning Toolbox (10 files), Curve Fitting Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
76 files
At the source: github.com/ApeParrot/Myokinetic-Kinesthetic-Interface/tree/1.0.0

The paper's code and data availability statement is in the Data section.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 150 scripts, each with its path and the digest of its content;
  • 8 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, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. Additional data and analysis codes have been deposited in Zenodo under the DOI: 10.5281/zenodo.17260640 (http://dx.doi.org/10.5281/zenodo.17260640) (https://zenodo.org/records/17260640). This study did not generate new materials.

Reproduced under the paper's license (CC BY-NC), 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 2, 28 September 2026

  • Authors: added Ülgen Kilic (0000-0002-4096-8417); removed Ülgen Kilic

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 6 MeSH terms, 1 funder, 47 references.

Cite

This paper

Masiero, F., Gentile, M., Gherardini, M., La Frazia, E., Moore, C. H., Kilic, Ü., Ianniciello, V., Reho, R., Mori, T., Paggetti, F., Andreani, L., Whitton, S. A., Marasco, P. D., & Cipriani, C. (2026). Coordinated hand movement sensation revealed through an implanted magnetic prosthetic kinesthetic interface. Science advances, 12(26), eadx5046. https://doi.org/10.1126/sciadv.adx5046

BibTeX

@article{masiero2026coordinated,
author = {Masiero, Federico and Gentile, Mattia and Gherardini, Marta and La Frazia, Eliana and Moore, Charles H and Kilic, Ülgen and Ianniciello, Valerio and Reho, Roberta and Mori, Tommaso and Paggetti, Flavia and Andreani, Lorenzo and Whitton, Simon A and Marasco, Paul D and Cipriani, Christian},
title = {{Coordinated hand movement sensation revealed through an implanted magnetic prosthetic kinesthetic interface}},
journal = {Science advances},
year = {2026},
month = jun,
volume = {12},
number = {26},
pages = {eadx5046},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adx5046},
url = {https://doi.org/10.1126/sciadv.adx5046},
pmid = {42341125},
pmcid = {PMC13292950}
}

RIS

TY - JOUR
AU - Masiero, Federico
AU - Gentile, Mattia
AU - Gherardini, Marta
AU - La Frazia, Eliana
AU - Moore, Charles H
AU - Kilic, Ülgen
AU - Ianniciello, Valerio
AU - Reho, Roberta
AU - Mori, Tommaso
AU - Paggetti, Flavia
AU - Andreani, Lorenzo
AU - Whitton, Simon A
AU - Marasco, Paul D
AU - Cipriani, Christian
TI - Coordinated hand movement sensation revealed through an implanted magnetic prosthetic kinesthetic interface
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/06/24
VL - 12
IS - 26
SP - eadx5046
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adx5046
UR - https://doi.org/10.1126/sciadv.adx5046
LA - en
ER -

CSL-JSON

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"id": "10.1126/sciadv.adx5046",
"type": "article-journal",
"title": "Coordinated hand movement sensation revealed through an implanted magnetic prosthetic kinesthetic interface",
"container-title": "Science advances",
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