Evidence map›Paper›PMID 41807417›Full record

ArticleScientific data2026

A hand biomechanics dataset of kinematics, kinetics, electromyography, and imaging in healthy adults.

Maximillian T Diaz, Alexis R Benoit, Kalyn M Kearney, Troy F Kelly, Erica M Lindbeck, Isaly Tappan, William S Bowers, Lavanya Durai, Justin B Nunag, Michael B Officer and 2 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Benchmarking Neural Network Personalized Musculoskeletal Hand Models Against Current Personalization Standards Using Experimental Magnetic Resonance Imaging and Fine-Wire Electromyography.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Maximillian T DiazJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Alexis R BenoitJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Kalyn M KearneyJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Troy F KellyJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Erica M LindbeckDepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL, USA.
Isaly TappanDepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL, USA.
William S BowersJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Lavanya DuraiJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Justin B NunagJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Michael B OfficerJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Joel B Harley *Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL, USA.
Jennifer A Nichols *J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA. jnichols@bme.ufl.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developing musculoskeletal hand models requires a variety of experimental biomechanics data. However, collecting robust biomechanics hand data is a time intensive process leading to a lack of widely available datasets. To address this issue the biomechanics hand modeling database (BHaM) was made as a collection of experimental data to aid the development, testing, and validation of musculoskeletal models and simulations. BHaM includes two datasets: (1) a population dataset (n = 726 adults) describing hand strength (pinch and grip), self-reported hand function (Michigan Hand Questionnaire), and anthropometric measurements (from photographs), and (2) a biomechanics dataset (n = 30 adults) describing kinematics (marker-based motion capture), kinetics (isometric and isokinetic data), and electromyography (surface and fine wire) during 19 tasks across the elbow, wrist, and hand. A subset of the biomechanics dataset (n = 15 adults) also includes magnetic resonance imaging of the shoulder through wrist. Participants for both datasets were recruited to represent a diverse population of healthy adults, ranging from 18 to 91 years.

Indexed as

HandAdolescentAdultAgedAged, 80 and overBiomechanical PhenomenaElectromyographyFemaleHand StrengthHumansKineticsMagnetic Resonance ImagingMaleMiddle AgedMotion CaptureYoung Adult

Identifiers

PMID41807417
PMCPMC13111597

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.