Evidence map›Paper›PMID 36554028›Full record

ArticleHealthcare (Basel, Switzerland)2022

An Analysis of Body Language of Patients Using Artificial Intelligence.

Rawad Abdulghafor, Abdelrahman Abdelmohsen, Sherzod Turaev, Mohammed A H Ali, Sharyar Wani

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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

2 citing papers in PubMed.

  1. Review
  2. 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.

Rawad AbdulghaforDepartment of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia.ORCID 0000-0002-0266-1027
Abdelrahman AbdelmohsenDepartment of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia.
Sherzod TuraevDepartment of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates.ORCID 0000-0001-6661-8469
Mohammed A H AliDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.ORCID 0000-0002-5179-7027
Sharyar WaniDepartment of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia.ORCID 0000-0001-6812-0066

Funding

International Islamic University Malaysia Grant RMCG20-023-0023United Arab Emirates University Start-Up Grant 31T137
6 · The paper itself

Abstract

In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in medical applications, with the automated detection of status and feelings becoming a significant study area. As a result, a variety of studies have begun to focus on the automated detection of symptoms in individuals infected with a pandemic or epidemic disease by studying their body language. The recognition and interpretation of arm and leg motions, facial recognition, and body postures is still a developing field, and there is a dearth of comprehensive studies that might aid in illness diagnosis utilizing artificial intelligence techniques and technologies. This literature review is a meta review of past papers that utilized AI for body language classification through full-body tracking or facial expressions detection for various tasks such as fall detection and COVID-19 detection, it looks at different methods proposed by each paper, their significance and their results.

Indexed as

AIanomaly detectionbody languagebody language analysisCOVID-19epidemicfall detectiongesture recognitionneocoronal pneumoniapandemic

Identifiers

PMID36554028
PMCPMC9778650

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.