Predicating Smartphone Users' Behaviour Towards a Location-Aware IoMT-Based Information System

Keikhosrokiani, Pantea

doi:10.4018/ijea.2021070104

Cited by 12 publications

(2 citation statements)

References 51 publications

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“…IoT has emerged as a new paradigm for improving healthcare. The health industry could potentially realize the advantages of IoT technologies as a result of the digital and information revolution [ 113 , 114 ]. The main benefits of the IoT are providing sustainable healthcare services, well-being, and more cost-effective treatment.…”

Section: Discussionmentioning

confidence: 99%

IoT Adoption and Application for Smart Healthcare: A Systematic Review

Alrawashdeh

Keikhosrokiani

Belaton

et al. 2022

Sensors

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In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.

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Section: Discussionmentioning

confidence: 99%

IoT Adoption and Application for Smart Healthcare: A Systematic Review

Alrawashdeh

Keikhosrokiani

Belaton

et al. 2022

Sensors

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“…Cardiovascular disease is considered as one of the leading causes of death worldwide 1 , 4 . Several advanced techniques such as electrocardiograph (ECG) and computerized tomography are used to detect heart disorders.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

et al. 2023

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Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.

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IoT-Based In-Hospital-In-Home Heart Disease Remote Monitoring System with Machine Learning Features for Decision Making

Keikhosrokiani

Kamaruddin

2022

Studies in Computational Intelligence

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Predicating Smartphone Users' Behaviour Towards a Location-Aware IoMT-Based Information System

Cited by 12 publications

References 51 publications

IoT Adoption and Application for Smart Healthcare: A Systematic Review

IoT Adoption and Application for Smart Healthcare: A Systematic Review

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

IoT-Based In-Hospital-In-Home Heart Disease Remote Monitoring System with Machine Learning Features for Decision Making

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