Evidence map›Paper›PMID 34812343›Full record

ArticleIEEE access : practical innovations, open solutions2020

Preventing Undesired Face-Touches With Wearable Devices and Haptic Feedback.

Nicole D'Aurizio, Tommaso Lisini Baldi, Gianluca Paolocci, Domenico Prattichizzo

Abstract read
In one paragraph

Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Review
  7. FaceGuard: A Wearable System To Avoid Face Touching.Frontiers in robotics and AI · 2021
    Article
  8. 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

4 authors.

Nicole D'AurizioDepartment of Information Engineering and MathematicsUniversity of Siena 53100 Siena Italy.
Tommaso Lisini BaldiDepartment of Information Engineering and MathematicsUniversity of Siena 53100 Siena Italy.
Gianluca PaolocciDepartment of Information Engineering and MathematicsUniversity of Siena 53100 Siena Italy.
Domenico PrattichizzoDepartment of Information Engineering and MathematicsUniversity of Siena 53100 Siena Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The alarming morbidity of COVID-19 has drawn the attention to the social role of hygiene rules, with a particular focus on the importance of limiting face-touch occurrences. To deal with this aspect, we present No Face-Touch, a system able to estimate hand proximity to face and notify the user whenever a face-touch movement is detected. In its complete setup, the system consists of an application running on the smartwatch and a wearable accessory. Its ease of implementation allows this solution to be ready-to-use and large-scale deployable. We developed two gesture detection approaches compatible with sensors embedded in recent smartwatches, i.e. inertial and magnetic sensors. After preliminary tests to tune target gesture parameters, we tested the two approaches and compared their accuracy. The final phase of this project consisted in exploiting the most robust approach in a daily living scenario during a 6-days campaign. Experimental results revealed the effectiveness of the proposed system, demonstrating its impact in reducing the number of face-touches and their duration.

Indexed as

Haptic interfacesmagnetic sensorswearable sensors

Identifiers

PMID34812343
PMCPMC8545332

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.