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.
Highlights
The Internet of Things (IoT) begins smart healthcare systems in the medical sector, generally comprised of smart sensors, a remote server, and the network
A patient monitoring system has been gaining the consideration of researchers in the field of advanced computer vision and machine learning
Nanni et al [28] classify pain states by proposing a descriptor named Elongated Ternary Patterns (ELTP), which combines the features of Elongated Binary Pattern (ELBP) [29] and Local Ternary Patterns (LBP)
Summary
The IoT begins smart healthcare systems in the medical sector, generally comprised of smart sensors, a remote server, and the network. Multiple camera posture-based monitoring techniques have been developed, e.g., mainly focusing on the upper body part of the patient. Because of these limitations, a non-invasive discomfort detection system has been proposed in this work, which neither utilizes specialized hardware/sensors nor a line of sight vision devices or any constrained/ specialized environment. The detected keypoints are further utilized to shape six major body organs This formation is based on association rules and part affinity fields. The detected keypoints are converted into six main body parts/organs based on association rules and part affinity fields, and the distance of the following key points is measured using coordinates information,.
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