Evidence map›Paper›PMID 38139628›Full record

ArticleSensors (Basel, Switzerland)2023

A Sensorised Glove to Detect Scratching for Patients with Atopic Dermatitis.

Cheuk-Yan Au, Syen Yee Leow, Chunxiao Yi, Darrion Ang, Joo Chuan Yeo, Mark Jean Aan Koh, Ali Asgar Saleem Bhagat

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.0field-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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026
    Review
  2. Review
  3. Gamifying Resistance Training with Wearable Sensors.Sensors (Basel, Switzerland) · 2025
    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

7 authors at 2 institutions in 1 country.

Cheuk-Yan AuInstitute for Health Innovation & Technology (iHealthtech), National University of Singapore (NUS) MD6, 14 Medical Drive, #14-01, Singapore 117599, Singapore.ORCID 0000-0003-3347-9815
Syen Yee LeowDepartment of Dermatology, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore 229899, Singapore.
Chunxiao YiInstitute for Health Innovation & Technology (iHealthtech), National University of Singapore (NUS) MD6, 14 Medical Drive, #14-01, Singapore 117599, Singapore.
Darrion AngInstitute for Health Innovation & Technology (iHealthtech), National University of Singapore (NUS) MD6, 14 Medical Drive, #14-01, Singapore 117599, Singapore.
Joo Chuan YeoInstitute for Health Innovation & Technology (iHealthtech), National University of Singapore (NUS) MD6, 14 Medical Drive, #14-01, Singapore 117599, Singapore.
Mark Jean Aan KohDepartment of Dermatology, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore 229899, Singapore.
Ali Asgar Saleem BhagatInstitute for Health Innovation & Technology (iHealthtech), National University of Singapore (NUS) MD6, 14 Medical Drive, #14-01, Singapore 117599, Singapore.ORCID 0000-0002-4695-8762
National University of Singapore · SGKK Women's and Children's Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, a lightweight compliant glove that detects scratching using data from microtubular stretchable sensors on each finger and an inertial measurement unit (IMU) on the palm through a machine learning model is presented: the SensorIsed Glove for Monitoring Atopic Dermatitis (SIGMA). SIGMA provides the user and clinicians with a quantifiable way of assaying scratch as a proxy to itch. With the quantitative information detailing scratching frequency and duration, the clinicians would be able to better classify the severity of itch and scratching caused by atopic dermatitis (AD) more objectively to optimise treatment for the patients, as opposed to the current subjective methods of assessments that are currently in use in hospitals and research settings. The validation data demonstrated an accuracy of 83% of the scratch prediction algorithm, while a separate 30 min validation trial had an accuracy of 99% in a controlled environment. In a pilot study with children (

Indexed as

Dermatitis, AtopicChildHumansPilot ProjectsPruritusUpper Extremityatopic dermatitisdermatologymedical deviceneural networkscratchingwearable sensors

Identifiers

PMID38139628
PMCPMC10748247
OpenAlexW4389625521

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.