tm-based adc matlab model Search Results


tm-based adc matlab models  (MathWorks Inc)
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( A ) Average Task-Space Accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_{sum}$$\end{document} A sum (along trials and participants) over the 4 days; ( B ) average Thickness (along trials and participants) over the 4 days, neglecting the discarded outliers; ( C ) Shape Index averaged along trials and participants over the 4 days, neglecting outliers; ( D ) correlation between Thickness and Task-Space Accuracy . Pearson’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R = 0.62$$\end{document} R = 0.62 with a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 3.8 \times 10^{-6}$$\end{document} p = 3.8 × 10 - 6 . Within box-plots ( A – C ), red crosses represent the data labelled as outliers by the <t>MATLAB™</t> function boxplot , net of neglected outliers on the basis of Thickness analysis.
Tm Based Video Game, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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tm -based custom scripts  (MathWorks Inc)
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( A ) Average Task-Space Accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_{sum}$$\end{document} A sum (along trials and participants) over the 4 days; ( B ) average Thickness (along trials and participants) over the 4 days, neglecting the discarded outliers; ( C ) Shape Index averaged along trials and participants over the 4 days, neglecting outliers; ( D ) correlation between Thickness and Task-Space Accuracy . Pearson’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R = 0.62$$\end{document} R = 0.62 with a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 3.8 \times 10^{-6}$$\end{document} p = 3.8 × 10 - 6 . Within box-plots ( A – C ), red crosses represent the data labelled as outliers by the <t>MATLAB™</t> function boxplot , net of neglected outliers on the basis of Thickness analysis.
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based image-processing technique  (MathWorks Inc)
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( A ) Average Task-Space Accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_{sum}$$\end{document} A sum (along trials and participants) over the 4 days; ( B ) average Thickness (along trials and participants) over the 4 days, neglecting the discarded outliers; ( C ) Shape Index averaged along trials and participants over the 4 days, neglecting outliers; ( D ) correlation between Thickness and Task-Space Accuracy . Pearson’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R = 0.62$$\end{document} R = 0.62 with a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 3.8 \times 10^{-6}$$\end{document} p = 3.8 × 10 - 6 . Within box-plots ( A – C ), red crosses represent the data labelled as outliers by the <t>MATLAB™</t> function boxplot , net of neglected outliers on the basis of Thickness analysis.
Based Image Processing Technique, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


( A ) Average Task-Space Accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_{sum}$$\end{document} A sum (along trials and participants) over the 4 days; ( B ) average Thickness (along trials and participants) over the 4 days, neglecting the discarded outliers; ( C ) Shape Index averaged along trials and participants over the 4 days, neglecting outliers; ( D ) correlation between Thickness and Task-Space Accuracy . Pearson’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R = 0.62$$\end{document} R = 0.62 with a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 3.8 \times 10^{-6}$$\end{document} p = 3.8 × 10 - 6 . Within box-plots ( A – C ), red crosses represent the data labelled as outliers by the MATLAB™ function boxplot , net of neglected outliers on the basis of Thickness analysis.

Journal: Scientific Reports

Article Title: Wrist redundancy management during pointing tasks remains stable over time and in presence of a visuomotor perturbation

doi: 10.1038/s41598-023-33531-2

Figure Lengend Snippet: ( A ) Average Task-Space Accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_{sum}$$\end{document} A sum (along trials and participants) over the 4 days; ( B ) average Thickness (along trials and participants) over the 4 days, neglecting the discarded outliers; ( C ) Shape Index averaged along trials and participants over the 4 days, neglecting outliers; ( D ) correlation between Thickness and Task-Space Accuracy . Pearson’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R = 0.62$$\end{document} R = 0.62 with a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 3.8 \times 10^{-6}$$\end{document} p = 3.8 × 10 - 6 . Within box-plots ( A – C ), red crosses represent the data labelled as outliers by the MATLAB™ function boxplot , net of neglected outliers on the basis of Thickness analysis.

Article Snippet: Pointing tasks were implemented through an interactive MATLAB TM -based video-game, in which participants could see the pointed target in real-time and control the cursor position with wrist rotations.

Techniques: