Blood glucose monitoring techniques: recent advances, challenges and future perspectives

Gamessa, Tadesse Waktola; Suman, Dabbu; Tadesse, Zerihun Ketema

doi:10.19101/ijatee.2018.546008

Cited by 11 publications

(4 citation statements)

References 71 publications

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“…These technologies include wearable sensors, biosensors, environmental sensors, and imaging sensors [8]. Wearable sensors are widely used in activity monitoring [8,36,47,48], sleep tracking [37][38][39][40], fitness tracking [8,16], and health monitoring [8,11,22,23]. Biosensors, on the other hand, can measure various biomarkers, such as glucose, cholesterol, blood pressure, and other vital signs [12,[15][16][17].…”

Section: Digital Health Sensing Technologiesmentioning

confidence: 99%

“…This smart device is small in size and can be connected to a smartphone to monitor blood sugar levels over time, and have the ability to share the information with healthcare providers [22]. There are three CGM modalities based on the method of placing the sensors: non-invasive (based on optical sensors, such as contact lenses that do not require skin puncture), minimally invasive (based on a microneedle-type sensor), and invasive (based on an implantable sensor that is inserted under the skin) [22,23]. Machine Learning (ML), including Deep Learning (DL), methods have been widely employed across diverse domains, particularly in healthcare, to enhance the well-being and safety of individuals.…”

Section: Introductionmentioning

confidence: 99%

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Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Chato,

Regentova

2023

JPM

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Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

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Section: Digital Health Sensing Technologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Chato,

Regentova

2023

JPM

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“…An enhanced sensitivity and specificity of detection can be achieved through functionalized nanomaterials acting as catalysts or immobilization platforms. The use of enzyme-less materials based on straight glucose electro-oxidation led to the fourth-generation glucose biosensor [ 85 , 86 ]. Compared with their enzyme counterparts, these biosensors exhibit considerably enhanced electrocatalytic performance for glucose detection, owing to the incorporation of nanostructured metal or metal oxide on the electrode surfaces [ 87 , 88 , 89 ].…”

Section: Non-enzymatic Electrochemical Biosensors For Glucose Sensingmentioning

confidence: 99%

A Review on Non-Enzymatic Electrochemical Biosensors of Glucose Using Carbon Nanofiber Nanocomposites

Mohammadpour-Haratbar

Zare

et al. 2022

Biosensors

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Diabetes mellitus has become a worldwide epidemic, and it is expected to become the seventh leading cause of death by 2030. In response to the increasing number of diabetes patients worldwide, glucose biosensors with high sensitivity and selectivity have been developed for rapid detection. The selectivity, high sensitivity, simplicity, and quick response of electrochemical biosensors have made them a popular choice in recent years. This review summarizes the recent developments in electrodes for non-enzymatic glucose detection using carbon nanofiber (CNF)-based nanocomposites. The electrochemical performance and limitations of enzymatic and non-enzymatic glucose biosensors are reviewed. Then, the recent developments in non-enzymatic glucose biosensors using CNF composites are discussed. The final section of the review provides a summary of the challenges and perspectives, for progress in non-enzymatic glucose biosensors.

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“…This technique allows for extremely sensitive measurements. Nevertheless, the fluorescence intensity depends on various parameters, including the glucose concentration, skin pigmentation, redness of the skin, and thickness of the epidermis layer; hence, the radiation of ultraviolet light on the tissue causing strong scattering [13,47,48]. The fluorescent spectroscopy system setup is shown in Figure 11.…”

Section: Fluorescent Spectroscopymentioning

confidence: 99%

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

Alsunaidi

Althobaiti

Tamal

et al. 2021

Sensors

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The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

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Blood glucose monitoring techniques: recent advances, challenges and future perspectives

Cited by 11 publications

References 71 publications

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

A Review on Non-Enzymatic Electrochemical Biosensors of Glucose Using Carbon Nanofiber Nanocomposites

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

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