How Can IoT Improve the Life-quality of Diabetes Patients?

Longva, Å.; Haddara, Moutaz

doi:10.1051/matecconf/201929203016

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…e health sector is considered a signi cant area that has an immense in uence on humans. While IoT technologies may be found in medical distribution networks in the healthcare industry, they have yet to be broadly implemented in other domains and services [7]. Diabetic patients have increased rapidly in the previous four decades, and this trend is expected to continue in the coming decades.…”

Section: Literature Reviewmentioning

confidence: 99%

“…IoT data can be integrated with statistics, "GIS data," "land use information," "social media network warnings," and many other "Virginia Tech Network Dynamics and Simulation Labs feeds," such as Zika, in apps like Health Map and Epic Aster. Keep track of the latest concerns in global health, including H1N1 [7]. Since the "Internet of ings" is a system of interconnected devices or artifacts with sensing devices that can communicate with one another in far-ung areas to collect information without the use of a broader internet, it does not require a larger internet.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Geetha

Krishna

Vyas

et al. 2022

Journal of Food Quality

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Chronic diabetes among adults is a public health concern and clinicians are trying to implement new strategies to effectively manage the disease. Traditionally, healthcare professionals are used to monitor and track the lab reports of patients. After that, they used to provide respective medicines and lifestyle plans to manage the chronic disease. The lifestyle of the patients and access to safe and secure food products is also responsible for developing chronic diseases. Thus, the Internet of Things (IoT) has taken an utmost interest in managing diabetes. This research is going to analyze the accuracy of IoT in assisting chronic diabetes management and determining food safety. To accomplish the research objectives, the researchers performed a linear regression analysis to understand whether IoT devices and Artificial Intelligence (AI) assist in assessing food safety and diabetes management. The independent variables selected were lab test values, treatment records, epoch size of AI, and image resolution of the training dataset. Dependent variables were the accuracy of IoT. Here, the accuracy of IoT and AI has been determined. Moreover, the accuracy of clinicians in diabetes management has been observed. It has been found that clinicians have high variance in accuracy (max 99%) whereas machines have limited variance in accuracy (max. 98%). Secondary research identified that clinicians need to be involved along with IoT devices for better management of this chronic disease and help patients by providing the safest food options.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Geetha

Krishna

Vyas

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…The work was focused more on the regular monitoring of the diabetes condition rather than prescribing any medicines for it. Working in the same context, in the care of patients with diabetes, the authors in [24] discussed procedures that can be simplified and improved with IoT. They recommended practices such as the use of wearable devices, collecting information on the mental and physical health status of patients and recommending a healthier lifestyle for patients through dietary follow-up and day-to-day activities.…”

Section: Related Workmentioning

confidence: 99%

IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation

et al. 2022

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High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.

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“…With the advancement of Internet of Things (IoT), especially medical devices, wearable sensors and smartphones the quality of life for diabetic peoples will be improved [6]. It will improve diabetes care by collecting health records, such as blood pressure and glucose levels, regularly to ensure self-management of diabetes.…”

Section: Introductionmentioning

confidence: 99%

Diabetes emergency cases identification based on a statistical predictive model

et al. 2022

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Diabetes is a chronic metabolic disease which is characterized by a permanently high blood sugar level. A distinction is made between two forms: Type 1 diabetes and Type 2 diabetes. It is believed that there are around 415 million people between the ages of 20 and 79 worldwide who have some form of diabetes illness today. In Europe, over 60 million people are diabetic, a diabetes incidence of 10.3% of men and 9.6% of women is estimated. The prevalence of diabetes is increasing among all ages in the European Region, mainly due to increases in overweight and obesity, unhealthy diet, and physical inactivity. A huge people in this population have type 2 diabetes, and the numbers will continue to rise over the next few years. So one can speak of a real widespread disease. The problem is not only the increased blood sugar, but also complications and accompanying diseases such as heart attack, stroke, or diabetic foot. However, as a type 2 diabetic, we can significantly influence the course of the disease and the success of therapy. To do this, it is important that we early detect the person that have (or likely have) a serious problem or an emergent case, and know about it as fast as possible. Early detection and treatment of this disease are very important to help diabetics live a healthy and near normal life. It can also help to avoid several serious complications. In addition, the evolution of wearable and Internet of Things medical devices can help to collect various health data for diagnosis using machine learning algorithms. In this paper, we present an IoT-based system architecture which ensures the collection of patient data in order to predict serious cases of diabetes. To secure data, Blockchain and IPFS are used, and to analyze data, we propose a statistical-based method for predictions. The process is as follows. First, data were collected from IoT devices, and a dataset was constructed and stored using IPFS. Then, the data will be scaled and filtered using noise-invariant data expansion. Next, an adaptive random forest algorithm is made in order to train data on the training dataset, and people with diabetes were classified using the proposed model. Three datasets were used, namely, the Pima Indian diabetes dataset, the Frankfurt Hospital diabetes dataset, and the last is the fusion of these two datasets. Finally, the performance of the method was evaluated and compared with other recent prediction methods. Based on the experiment result, an accuracy of 85.9%, 99.5%, and 99.8% has been achieved based on the three datasets, respectively. Thus, the model can be used to predict and alert physicians or hospitals serious cases that need urgent reactions.

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How Can IoT Improve the Life-quality of Diabetes Patients?

Cited by 12 publications

References 24 publications

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation

Diabetes emergency cases identification based on a statistical predictive model

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