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linear encoder 3d  (MOCAP Inc)

 
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    MOCAP Inc linear encoder 3d
    Linear Encoder 3d, supplied by MOCAP Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/3d+encoder/linear+encoder+3d/pm37440324-195-17-44
    Average 90 stars, based on 1 article reviews
    linear encoder 3d - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Devices to measure calf raise test outcomes: A narrative review.
    Article Snippet: Authors (year of publication) Participants Reliability or concurrent validity of outcomes InterpretationaCRT devices Andreasen et al. (2020) Linear encoder 3D MOCAP Patients with acute achilles tendon rupture (n = 45, 36 males and 9 females) Total work measurement error (%) Linear encoder heel versus MOCAP heel Injured side = 1.5% (p = 0.163) Non‐injured side = 2.9% (p < 0.0001) Linear encoder heel versus MOCAP pelvis injured side = 21% (p < 0.0001) Non‐injured side = 24.7% (p < 0.0001) Total work concurrent validity (linear regression slope [95% CI]) Linear encoder heel versus MOCAP heel Injured side = 0.95 [0.91, 1.00] Non‐injured side = 1.00 [0.98, 1.03] Lienar encoder heel versus MOCAP pelvis Injured side = 0.79 [0.70, 0.87] Non‐injured side = 0.92 [0.86, 0.97] Limb symmetry index (linear regression slope [95% CI]) Linear encoder heel versus MOCAP heel 0.98 [0.92; 1.02] Lienar encoder heel versus MOCAP pelvis 1.03 [0.90, 1.09] The CRT performed using the heel as a surrogate for centre of body mass overestimates the total work by 21.0%–24.7% versus the gold standard (MOCAP pelvis), but can precisely detect the relative difference between limbs.



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    Ablation study: performance measures ± standard error of the mean. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark$\end{document} ’ indicates included, ‘×’ indicates excluded model components; the first two rows are the complete CANDELA model with the two different pre-training stragies. Models marked ‘ † ’ are significantly worse than CANDELA using only one pre-training task. Models marked ‘*’ are significantly better (pairwise t -test, α = 0.05, corrected for multiple testing using Holm–Šídák). Bold values indicate the best configuration

    Journal: NAR Genomics and Bioinformatics

    Article Title: Cancer drug sensitivity estimation using modular deep Graph Neural Networks

    doi: 10.1093/nargab/lqae043

    Figure Lengend Snippet: Ablation study: performance measures ± standard error of the mean. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark$\end{document} ’ indicates included, ‘×’ indicates excluded model components; the first two rows are the complete CANDELA model with the two different pre-training stragies. Models marked ‘ † ’ are significantly worse than CANDELA using only one pre-training task. Models marked ‘*’ are significantly better (pairwise t -test, α = 0.05, corrected for multiple testing using Holm–Šídák). Bold values indicate the best configuration

    Article Snippet: As an alternative to pre-training the drug encoder on matabolite properties and toxicity, we have studied the use of the 3D Infomax encoding for drugs.

    Techniques: