Evidence map›Paper›PMID 33668101›Full record

ArticleSensors (Basel, Switzerland)2021

Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion.

Jeffrey Henderson, Joan Condell, James Connolly, Daniel Kelly, Kevin Curran

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

5 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. State-of-the-Art Sensors Research in Ireland.Sensors (Basel, Switzerland) · 2022
    Article
  4. Review
  5. Design of Wearable Electrolarynx with Automatic Control.Journal of medical signals and sensors
    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

5 authors at 2 institutions in 2 countries.

Jeffrey HendersonSchool of Computing, Engineering and Intelligent System, Ulster University Magee Campus, Northland Rd, BT48 7JL Londonderry, Ireland.ORCID 0000-0001-7466-8263
Joan CondellSchool of Computing, Engineering and Intelligent System, Ulster University Magee Campus, Northland Rd, BT48 7JL Londonderry, Ireland.
James ConnollySchool of Science, Letterkenny Institute of Technology, Port Rd, Gortlee, F92 FC93 Letterkenny, Ireland.ORCID 0000-0002-7970-1699
Daniel KellySchool of Computing, Engineering and Intelligent System, Ulster University Magee Campus, Northland Rd, BT48 7JL Londonderry, Ireland.
Kevin CurranSchool of Computing, Engineering and Intelligent System, Ulster University Magee Campus, Northland Rd, BT48 7JL Londonderry, Ireland.ORCID 0000-0001-5237-5355
University of Ulster · GBLetterkenny Institute of Technology · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Capturi ng hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person's finger joint. Due to the modular design, the ECSMS's sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers.

Indexed as

Gloves, ProtectiveHandRange of Motion, ArticularSmart MaterialsHumansReproducibility of ResultsTechnologySmart Materialsdata glovesjoint measurementmeasurement systemParkinson’s diseaserehabilitationrheumatoid arthritissmart sensingstroke recovery

Identifiers

PMID33668101
PMCPMC7956630
OpenAlexW3133388523

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.