Evidence map›Paper›PMID 40102456›Full record

ArticleScientific data2025

A high-resolution and whole-body dataset of hand-object contact areas based on 3D scanning method.

Zelin Chen, Hanlu Chen, Yiming Ouyang, Chenhao Cao, Wei Gao, Qiqiang Hu, Hu Jin, Shiwu Zhang

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zelin ChenInstitute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
Hanlu ChenInstitute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
Yiming OuyangInstitute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China. oyym@ustc.edu.cn.
Chenhao CaoInstitute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
Wei GaoInstitute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.ORCID http://orcid.org/0000-0001-7806-896X
Qiqiang HuDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong.
Hu JinInstitute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China. jhrdsp@ustc.edu.cn.ORCID http://orcid.org/0000-0002-3108-875X
Shiwu ZhangInstitute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) Nos. U21A20119, 51705495
6 · The paper itself

Abstract

Hand contact data, reflecting the intricate behaviours of human hands during object operation, exhibits significant potential for analysing hand operation patterns to guide the design of hand-related sensors and robots, and predicting object properties. However, these potential applications are hindered by the constraints of low resolution and incomplete capture of the hand contact data. Leveraging a non-contact and high-precision 3D scanning method for surface capture, a high-resolution and whole-body hand contact dataset, named as Ti3D-contact, is constructed in this work. The dataset, with an average resolution of 0.72 mm, contains 1872 sets of texture images and 3D models. The contact area during hand operation is whole-body painted on gloves, which are captured as the high-resolution original hand contact data through a 3D scanner. Reliability validation on Ti3D-contact is conducted and hand movement classification with 95% precision is achieved using the acquired hand contact dataset. The properties of high-resolution and whole-body capturing make the acquired dataset exhibit a promising potential application in hand posture recognition and hand movement prediction.

Indexed as

HandImaging, Three-DimensionalHumansMovement

Identifiers

PMID40102456
PMCPMC11920513

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LicenceCC BY-NC-ND
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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.