Evidence map›Paper›PMID 34899980›Full record

ArticleApplied bionics and biomechanics2021

Quantitative Investigation of Hand Grasp Functionality: Hand Joint Motion Correlation, Independence, and Grasping Behavior.

Yuan Liu, Bo Zeng, Ting Zhang, Li Jiang, Hong Liu, Dong Ming

Open access · goldAbstract read
In one paragraph

Article in Applied bionics and biomechanics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 53% of its field
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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 4 institutions in 1 country.

Yuan LiuTianjin University, Academy of Medical Engineering and Translational Medicine (AMT), Tianjin, China.ORCID https://orcid.org/0000-0002-9703-7357
Bo ZengBeijing Institute of Precision Mechatronics and Controls, Laboratory of Aerospace Servo Actuation and Transmission, Beijing, China.ORCID https://orcid.org/0000-0003-3765-9936
Ting ZhangSoochow University, College of Mechanical and Electrical Engineering, Robotics and Microsystems Center, Suzhou, China.
Li JiangHarbin Institute of Technology, State Key Laboratory of Robotics and System, Harbin, China.
Hong LiuHarbin Institute of Technology, State Key Laboratory of Robotics and System, Harbin, China.
Dong MingTianjin University, Academy of Medical Engineering and Translational Medicine (AMT), Tianjin, China.
Harbin Institute of Technology · CNTianjin University · CNBeijing Jingshida Electromechanical Equipment Research Institute · CNSoochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modeling and understanding human grasp functionality are fundamental in prosthetics, robotics, medicine, and rehabilitation, since they contribute to exploring motor control mechanism, evaluating grasp function, and designing and controlling prosthetic hands or exoskeletons. However, there are still limitations in providing a comprehensive and quantitative understanding of hand grasp functionality. After simultaneously considering three significant and essential influence factors in daily grasping contained relative position, object shape, and size, this paper presents the tolerance grasping to provide a more comprehensive understanding of human grasp functionality. The results of joint angle distribution and variance explained by PCs supported that tolerance grasping can represent hand grasp functionality more comprehensively. Four synergies are found and account for 93% ± 1.5% of the overall variance. The ANOVA confirmed that there was no significant individual difference in the first four postural synergies. The common patterns of grasping behavior were found and characterized by the mean value of postural synergy across 10 subjects. The independence analysis demonstrates that the tolerance grasping results highly correlate with unstructured natural grasping and more accurately correspond to cortical representation size of finger movement. The potential for exploring the neuromuscular control mechanism of human grasping is discussed. The analysis of hand grasp characteristics that contained joint angle distribution, correlation, independence, and postural synergies, presented here, should be more representative to provide a more comprehensive understanding of hand grasp functionality.

Identifiers

PMID34899980
PMCPMC8660235
OpenAlexW4200254383

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.