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. 2022 Dec 10;10(12):2504.
doi: 10.3390/healthcare10122504.

An Analysis of Body Language of Patients Using Artificial Intelligence

Rawad Abdulghafor  1 , Abdelrahman Abdelmohsen  1 , Sherzod Turaev  2 , Mohammed A H Ali  3 , Sharyar Wani  1
Affiliations

An Analysis of Body Language of Patients Using Artificial Intelligence

Rawad Abdulghafor et al. Healthcare (Basel). .

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.

Keywords: AI; COVID-19; anomaly detection; body language; body language analysis; epidemic; fall detection; gesture recognition; neocoronal pneumonia; pandemic.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overview of the main forms of nonverbal communication. The figure has been taken from [20].
Figure 2
Figure 2
Face recognition structure. The figure has been taken from [39].
Figure 3
Figure 3
Multilayered architecture of a smart home. The figure was taken from [75].
Figure 4
Figure 4
The overall scheme of the proposed method [76].
Figure 5
Figure 5
The proposed architecture for anomaly detection in smart homes. The figure has been taken from [75].
Figure 6
Figure 6
Example of activities to be detected. These images have been taken from [83].
Figure 7
Figure 7
WS to the health care system has been taken from [84].
Figure 8
Figure 8
The general procedure of AI and non-AI-based applications help general physicians to identify the COVID-19 symptoms. This figure has been taken from [90].
Figure 9
Figure 9
Network architecture for both the optical flow and egomotion and depth networks. This figure has been taken from [95].
Figure 10
Figure 10
Microsoft Bing’s COVID-19 Tracker, note(s): Screenshot of Bing’s COVID-19 Tracker, 9 February 2022.
See this image and copyright information in PMC

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