Evidence map›Paper›PMID 29757261›Full record

ArticleSensors (Basel, Switzerland)2018

Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation.

Bor-Shing Lin, I-Jung Lee, Shu-Yu Yang, Yi-Chiang Lo, Junghsi Lee, Jean-Lon Chen

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
7.6field-weighted citation impact, top 2% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 103 citations in OpenAlex.

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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.

Bor-Shing LinDepartment of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan. bslin@mail.ntpu.edu.tw.
I-Jung LeeDepartment of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan. akino_sumiko@hotmail.com.
Shu-Yu YangDepartment of Physical Medicine and Rehabilitation, Chi-Mei Medical Center, Tainan 71004, Taiwan. yangshuyu1970@gmail.com.
Yi-Chiang LoDepartment of Electrical Engineering, Yuan Ze University, Taoyuan City 32003, Taiwan. alexlo0703@gmail.com.
Junghsi LeeDepartment of Electrical Engineering, Yuan Ze University, Taoyuan City 32003, Taiwan. eejlee@saturn.yzu.edu.tw.ORCID 0000-0002-8273-1119
Jean-Lon ChenDepartment of Physical Medicine & Rehabilitation, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333, Taiwan. bigmac1479@gmail.com.
National Taipei University · TWYuan Ze University · TWChang Gung University · TWChi Mei Medical Center · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Capturing hand motions for hand function evaluations is essential in the medical field. Various data gloves have been developed for rehabilitation and manual dexterity assessments. This study proposed a modular data glove with 9-axis inertial measurement units (IMUs) to obtain static and dynamic parameters during hand function evaluation. A sensor fusion algorithm is used to calculate the range of motion of joints. The data glove is designed to have low cost, easy wearability, and high reliability. Owing to the modular design, the IMU board is independent and extensible and can be used with various microcontrollers to realize more medical applications. This design greatly enhances the stability and maintainability of the glove.

Indexed as

data gloveinertial sensorjoint measurementmotion capturerehabilitation

Identifiers

PMID29757261
PMCPMC5982580
OpenAlexW2799429034

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.