Evidence map›Paper›PMID 37420943›Full record

ArticleMicromachines2023

Design of Wearable Finger Sensors for Rehabilitation Applications.

Beyza Bozali, Sepideh Ghodrat, Kaspar M B Jansen

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2023. 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
0.8field-weighted citation impact, top 33% 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, 7 citations in OpenAlex.

  1. Article
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  4. Article
  5. 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

3 authors at 1 institution in 1 country.

Beyza BozaliFaculty of Industrial Design Engineering, Delft University of Technology, 2628 CE Delft, The Netherlands.ORCID 0000-0002-1229-8700
Sepideh GhodratFaculty of Industrial Design Engineering, Delft University of Technology, 2628 CE Delft, The Netherlands.ORCID 0000-0002-4450-8178
Kaspar M B JansenFaculty of Industrial Design Engineering, Delft University of Technology, 2628 CE Delft, The Netherlands.ORCID 0000-0002-2172-9824
Delft University of Technology · NL

Funding

Republic of Turkey Ministry of National Education Study Abroad Program
6 · The paper itself

Abstract

As an emerging technology, smart textiles have attracted attention for rehabilitation purposes or to monitor heart rate, blood pressure, breathing rate, body posture, as well as limb movements. Traditional rigid sensors do not always provide the desired level of comfort, flexibility, and adaptability. To improve this, recent research focuses on the development of textile-based sensors. In this study, knitted strain sensors that are linear up to 40% strain with a sensitivity of 1.19 and a low hysteresis characteristic were integrated into different versions of wearable finger sensors for rehabilitation purposes. The results showed that the different finger sensor versions have accurate responses to different angles of the index finger at relaxation, 45° and 90°. Additionally, the effect of spacer layer thickness between the finger and sensor was investigated.

Indexed as

knitted strain sensorrehabilitation applicationssmart textileswearable textiles

Identifiers

PMID37420943
PMCPMC10146160
OpenAlexW4360619403

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.