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Automated Patient Discomfort Detection Using Deep Learning

Imran Ahmed1, Iqbal Khan1, Misbah Ahmad1, Awais Adnan1, Hanan Aljuaid2,*

1 Center of Excellence in Information Technology, Institute of Management Sciences, Peshawar, Pakistan
2 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), Riyadh, Saudi Arabia

* Corresponding Author: Hanan Aljuaid. Email: email

(This article belongs to the Special Issue: Security and Privacy issues for various Emerging Technologies and Future Trends)

Computers, Materials & Continua 2022, 71(2), 2559-2577.


The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.


Cite This Article

APA Style
Ahmed, I., Khan, I., Ahmad, M., Adnan, A., Aljuaid, H. (2022). Automated patient discomfort detection using deep learning. Computers, Materials & Continua, 71(2), 2559-2577.
Vancouver Style
Ahmed I, Khan I, Ahmad M, Adnan A, Aljuaid H. Automated patient discomfort detection using deep learning. Comput Mater Contin. 2022;71(2):2559-2577
IEEE Style
I. Ahmed, I. Khan, M. Ahmad, A. Adnan, and H. Aljuaid "Automated Patient Discomfort Detection Using Deep Learning," Comput. Mater. Contin., vol. 71, no. 2, pp. 2559-2577. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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