Open Access

ARTICLE

Automated Patient Discomfort Detection Using Deep Learning

Imran Ahmed¹, Iqbal Khan¹, Misbah Ahmad¹, Awais Adnan¹, Hanan Aljuaid^2,*

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:

(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. https://doi.org/10.32604/cmc.2022.021259

Received 28 June 2021; Accepted 30 August 2021; Issue published 07 December 2021

Abstract

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.

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Keywords

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