Automated Patient Discomfort Detection Using Deep Learning  

作　　者：Imran Ahmed Iqbal Khan Misbah Ahmad Awais Adnan Hanan Aljuaid 

机构地区：[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

出　　处：《Computers, Materials & Continua》2022年第5期2559-2577,共19页计算机、材料和连续体（英文）

摘　　要：The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased 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.

关 键 词：Artificial intelligence patient monitoring discomfort detection deep learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]