Design and Implementation of a Wearable System for Non-Invasive Glucose Level Monitoring

Islam, Md. Muntasir; Maniur, Sultan Mohammad

doi:10.1109/becithcon48839.2019.9063193

Cited by 17 publications

(11 citation statements)

References 7 publications

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“…We were left with 37 studies, and this number remained unchanged even after performing backward and forward reference list checking. The synthesis included a total of 37 articles ( Multimedia Appendix 4 [ 7 , 8 , 13 , 14 , 22 - 54 ]).…”

Section: Resultsmentioning

confidence: 99%

“…This can help to understand and draw meaningful information from the gathered data and provide advanced and clinically meaningful analytics. Many researchers have adapted existing WDs not originally intended for diabetes management and adapted the sensory information for use in diabetes-related metrics, and some have created prototypes especially designed for diabetes [ 13 , 14 ]. WDs are used for a variety of reasons, including monitoring, prevention, glucose estimation, diagnostics, classification, and prevention, but the number of studies that are reported are low in comparison with those that make use of smartphones for example.…”

Section: Introductionmentioning

confidence: 99%

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Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review

Ahmed¹,

Aziz²,

Abd‐Alrazaq³

et al. 2022

J Med Internet Res

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Background Prevalence of diabetes has steadily increased over the last few decades with 1.5 million deaths reported in 2012 alone. Traditionally, analyzing patients with diabetes has remained a largely invasive approach. Wearable devices (WDs) make use of sensors historically reserved for hospital settings. WDs coupled with artificial intelligence (AI) algorithms show promise to help understand and conclude meaningful information from the gathered data and provide advanced and clinically meaningful analytics. Objective This review aimed to provide an overview of AI-driven WD features for diabetes and their use in monitoring diabetes-related parameters. Methods We searched 7 of the most popular bibliographic databases using 3 groups of search terms related to diabetes, WDs, and AI. A 2-stage process was followed for study selection: reading abstracts and titles followed by full-text screening. Two reviewers independently performed study selection and data extraction, and disagreements were resolved by consensus. A narrative approach was used to synthesize the data. Results From an initial 3872 studies, we report the features from 37 studies post filtering according to our predefined inclusion criteria. Most of the studies targeted type 1 diabetes, type 2 diabetes, or both (21/37, 57%). Many studies (15/37, 41%) reported blood glucose as their main measurement. More than half of the studies (21/37, 57%) had the aim of estimation and prediction of glucose or glucose level monitoring. Over half of the reviewed studies looked at wrist-worn devices. Only 41% of the study devices were commercially available. We observed the use of multiple sensors with photoplethysmography sensors being most prevalent in 32% (12/37) of studies. Studies reported and compared >1 machine learning (ML) model with high levels of accuracy. Support vector machine was the most reported (13/37, 35%), followed by random forest (12/37, 32%). Conclusions This review is the most extensive work, to date, summarizing WDs that use ML for people with diabetes, and provides research direction to those wanting to further contribute to this emerging field. Given the advancements in WD technologies replacing the need for invasive hospital setting devices, we see great advancement potential in this domain. Further work is needed to validate the ML approaches on clinical data from WDs and provide meaningful analytics that could serve as data gathering, monitoring, prediction, classification, and recommendation devices in the context of diabetes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review

Ahmed¹,

Aziz²,

Abd‐Alrazaq³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Wearable components for noninvasive glucose level monitoring [40] Used in real-time monitoring of the devices glucose…”

Section: Risk Prediction Modelmentioning

confidence: 99%

Ambient Assistive Living for Monitoring the Physical Activity of Diabetic Adults through Body Area Networks

Srinivasu¹,

JayaLakshmi

Jhaveri

et al. 2022

Mobile Information Systems

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The adequate aging hypothesis seeks to help people live longer, healthy lives. Diabetic patients who stay remotely need an infrastructure to monitor them continuously and provide timely treatment. Ambient assisted living (AAL) encourages the establishment of solutions that may help optimize older people’s assistive environment while also reducing their impairments. The blood glucose levels of diabetic patients are continuously monitored by gold oxide sensors placed over the human body. The signals associated with the glucose levels in the human body are plotted over a spectrogram image using the short-time Fourier transform, which is further classified using the deep learning model based on finetuned AlexNet, which has employed random oversampling and batch normalization for better precision in the results. The model classifies the spectrogram images as low and high glucose levels and normal glucose levels. Thereby alarming the caretakers for effective treatment of the individuals. Body area networks (BANs) gather information from biosensors and send it to a domain controller to assist caretakers and physicians in recommending the physical exercises for their clients. Evaluation criteria such as sensitivity and specificity, precision, and Mathew’s correlation coefficient are used to assess the effectiveness of the proposed model in this current diabetes study. The cross-validation of the model at multiple folds is being evaluated to analyze the performance. It is evident from the obtained results that the proposed model has exhibited an acceptable performance in precisely sensing the individuals with abnormal glucose levels.

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“…These systems leverage advanced algorithms to process data from various sensors embedded in wearable devices, such as strain gauges, plastic optical fibers, actuators, and electrochemical sensors, to provide personalized health insights and interventions [1][2][3][4]. The use of machine learning allows these devices to classify and predict various health-related parameters, including blood glucose levels, blood pressure, stress levels, and physical activity, tailored to individual users' needs and health conditions [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Olyanasab,

Annabestani

2024

JPM

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This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer’s physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual’s physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.

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Design and Implementation of a Wearable System for Non-Invasive Glucose Level Monitoring

Cited by 17 publications

References 7 publications

Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review

Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review

Ambient Assistive Living for Monitoring the Physical Activity of Diabetic Adults through Body Area Networks

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

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