Evidence map›Paper›PMID 36616450›Full record

ArticlePolymers2022

Data Glove with Self-Compensation Mechanism Based on High-Sensitive Elastic Fiber-Optic Sensor.

Hui Yu, Daifu Zheng, Yun Liu, Shimeng Chen, Xiaona Wang, Wei Peng

Abstract read
In one paragraph

Article in Polymers, 2022. 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. Article
  2. Review
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.

Hui YuSchool of Physics, Dalian University of Technology, Dalian116024, China.
Daifu ZhengSchool of Physics, Dalian University of Technology, Dalian116024, China.
Yun LiuSchool of Physics, Dalian University of Technology, Dalian116024, China.ORCID 0000-0001-9384-4912
Shimeng ChenMarine Engineering College, Dalian Maritime University, Dalian 116026, China.
Xiaona WangSchool of Physics, Dalian University of Technology, Dalian116024, China.
Wei PengSchool of Physics, Dalian University of Technology, Dalian116024, China.

Funding

Fundamental Research Funds for the Central Universities 3132022220Fundamental Research Funds for the Central Universities DUT21LK20National Natural Science Foundation of China 61727816
6 · The paper itself

Abstract

With the development of virtual reality (VR) interaction technology, data glove has become one of the most popular devices for human-computer interaction. It's valuable to design high-sensitive and flexible sensor for data glove. Therefore, a low-cost data glove based on self-compensating elastic optical fiber sensor with self-calibration function is proposed. The tunable and stretchable elastic fiber was fabricated by a simple, economical and controllable method. The fiber has good flexibility and high stability under stretching, bending and indentation deformation. The optical fibers are installed in the sensor in a U shape with a bending radius of 5 mm. Compared with the straight fiber, the response sensitivity of the U-shaped fiber to deformation is increased by about 7 times at most. The reference optical fiber is connected to the sensor, which effectively improves the stability and accuracy of the sensor system. In addition, the sensors are easy to install so that the data gloves can be customized for different hand shapes. In the gesture capture test, it can respond quickly and guide the manipulator to track the gesture. This responsive and stable data glove has broad development potential in motion monitoring, telemedicine and human-computer interaction.

Indexed as

data gloveelastic fiberflexible sensorhuman-computer interactionself-compensation

Identifiers

PMID36616450
PMCPMC9824815

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.