Internet of things in health: Requirements, issues, and gaps

Calvillo-Arbizu, Jorge; Roman-Martinez, Isabel; Reina‐Tosina, Javier

doi:10.1016/j.cmpb.2021.106231

Cited by 49 publications

(28 citation statements)

References 68 publications

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“…Health care systems have compromised performance or security, resulting in the loss of critical health data and even threats and attacks by hackers, which may affect the life of patients (17). An IoT-oriented health care system generally has three layers, which are the perception layer, fog layer, and cloud layer (43)(44)(45). The layered architecture is shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

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Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document

Khurshid

Imtiaz

Boulila

et al. 2022

Front. Public Health

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Internet of Things (IoT) involves a set of devices that aids in achieving a smart environment. Healthcare systems, which are IoT-oriented, provide monitoring services of patients' data and help take immediate steps in an emergency. Currently, machine learning-based techniques are adopted to ensure security and other non-functional requirements in smart health care systems. However, no attention is given to classifying the non-functional requirements from requirement documents. The manual process of classifying the non-functional requirements from documents is erroneous and laborious. Missing non-functional requirements in the Requirement Engineering (RE) phase results in IoT oriented healthcare system with compromised security and performance. In this research, an experiment is performed where non-functional requirements are classified from the IoT-oriented healthcare system's requirement document. The machine learning algorithms considered for classification are Logistic Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), K-Nearest Neighbors (KNN), ensemble, Random Forest (RF), and hybrid KNN rule-based machine learning (ML) algorithms. The results show that our novel hybrid KNN rule-based machine learning algorithm outperforms others by showing an average classification accuracy of 75.9% in classifying non-functional requirements from IoT-oriented healthcare requirement documents. This research is not only novel in its concept of using a machine learning approach for classification of non-functional requirements from IoT-oriented healthcare system requirement documents, but it also proposes a novel hybrid KNN-rule based machine learning algorithm for classification with better accuracy. A new dataset is also created for classification purposes, comprising requirements related to IoT-oriented healthcare systems. However, since this dataset is small and consists of only 104 requirements, this might affect the generalizability of the results of this research.

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Section: Methodsmentioning

confidence: 99%

“…The fog layer processes the data to produce the required response quickly. The Cloud layer deals with storing the data and big data analytics (44).…”

Section: Methodsmentioning

confidence: 99%

Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document

Khurshid

Imtiaz

Boulila

et al. 2022

Front. Public Health

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“…With the popularity of the internet and the wide application of smartphones, they can use the WeChat 10.3389/fpsyg.2022.982634 platform by allocating their time appropriately for fragmental learning to obtain necessary knowledge and information in urinary stoma care (Cook et al, 2008;Tregoning et al, 2021). Current evidence suggests (Calvillo-Arbizu et al, 2021) that a positive coping mode reduces the care burden of the urinary stoma caregivers, while a negative coping mode increases their care burden. Importantly, the IPHE mode combines education with entertainment by using presentations, comics, videos and interactive games based on an abundant network and multi-media resources (Cohen et al, 2021), which is beneficial to both the patients and caregivers to heighten their knowledge and skills about stoma care and compensate for the deficits of conventional supply-oriented didactic health education, thus making health education more standardized and colorful.…”

Section: Internet Plus Health Education Raises the Positive Pressure ...mentioning

confidence: 99%

Impact of internet plus health education on urinary stoma caregivers in coping with care burden and stress in the era of COVID-19

Fan

Lai

et al. 2022

Front. Psychol.

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ObjectiveTo explore the impact of “Internet Plus Health Education” on coping with care burden and pressure in urinary stoma caregivers in the era of COVID-19.Materials and methodsEighty caregivers of patients with urinary ostomy were equally randomized to experimental and control groups. Caregivers in the experimental group received digital nursing education intervention, which involved nursing intervention of Internet Plus Health Education (IPHE), and those in the control group received conventional care instructions. Six months later, care burden and emotional pressure were assessed in all caregivers using the Zarit Caregiver Burden Interview (ZBI) and the Simplified Coping Style Questionnaire (SCSQ).ResultsBefore the intervention, the ZBI and SCSQ scores were comparable between both groups (p > 0.05). After the intervention, the ZBI scores in the experimental group were significantly higher than in the control group and vice versa for SCSQ scores (p < 0.01). Furthermore, after the intervention, the family care satisfaction scale (FCSS) of the experimental group was significantly higher than the control group.ConclusionProviding “Internet Plus Health Education” to urinary stoma caregivers can reduce their care burden and enhance their pressure-coping ability in the COVID-19 era.

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“…It carries out unified management and planning for design, inspection, purchase, operation, maintenance, and scrap cleaning through the system. In the case that the demand and safe operation are guaranteed, the safety, energy efficiency, and cost control within the whole life cycle of the asset should be optimized [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Adoption of the Internet of Things in Life Cycle Management of Electric Energy Meters

2022

AJBM

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Objective: this research aims to design and implement the whole life cycle management system of electric energy meters based on the Internet of Things. Methods: first, the current development status of power asset management and the Internet of Things industry in China is studied and analyzed, and the existing problems of power asset management are pointed out. Then, the radio frequency identification (RFID) technology used in system design is introduced in detail. After that, the design scheme of the system is introduced from the aspects of the overall design structure of the system and the development and operation environment. Finally, the concrete implementation and research results of the system are introduced and analyzed. Results: the results of actual investigation and analysis show that the system proposed in this research has good practical application value, realizing the full life cycle management of electric energy meters, shortening the time of each stage of asset management, and greatly improving the work efficiency of managers. Conclusion: the Internet of Things technology has high application value and prospects in electric energy meter life cycle management.

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Internet of things in health: Requirements, issues, and gaps

Cited by 49 publications

References 68 publications

Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document

Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document

Impact of internet plus health education on urinary stoma caregivers in coping with care burden and stress in the era of COVID-19

Analysis and Adoption of the Internet of Things in Life Cycle Management of Electric Energy Meters

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