[HTML][HTML] Machine learning and smart devices for diabetes management: Systematic review

MA Makroum, M Adda, A Bouzouane, H Ibrahim - Sensors, 2022‏ - mdpi.com
(1) Background: The use of smart devices to better manage diabetes has increased
significantly in recent years. These technologies have been introduced in order to make life …

Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective

Y Zou, Z Chu, J Guo, S Liu, X Ma, J Guo - Biosensors and Bioelectronics, 2023‏ - Elsevier
Diabetes and its complications are seriously threatening the health and well-being of
hundreds of millions of people. Glucose levels are essential indicators of the health …

Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework

E Nasarian, R Alizadehsani, UR Acharya, KL Tsui - Information Fusion, 2024‏ - Elsevier
Background Artificial intelligence (AI)-based medical devices and digital health
technologies, including medical sensors, wearable health trackers, telemedicine, mobile …

[HTML][HTML] FLIRT: A feature generation toolkit for wearable data

S Föll, M Maritsch, F Spinola, V Mishra, F Barata… - Computer Methods and …, 2021‏ - Elsevier
Abstract Background and Objective: Researchers use wearable sensing data and machine
learning (ML) models to predict various health and behavioral outcomes. However, sensor …

[HTML][HTML] Overview of artificial intelligence–driven wearable devices for diabetes: sco** review

A Ahmed, S Aziz, A Abd-Alrazaq, F Farooq… - Journal of Medical …, 2022‏ - jmir.org
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 …

Sense and learn: recent advances in wearable sensing and machine learning for blood glucose monitoring and trend-detection

AY Alhaddad, H Aly, H Gad, A Al-Ali… - … in Bioengineering and …, 2022‏ - frontiersin.org
Diabetes mellitus is characterized by elevated blood glucose levels, however patients with
diabetes may also develop hypoglycemia due to treatment. There is an increasing demand …

[HTML][HTML] Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data

A Ahmed, S Aziz, U Qidwai, A Abd-Alrazaq… - Computer Methods and …, 2023‏ - Elsevier
Abstract Introduction Diabetes Mellitus (DM) is characterized by impaired ability to
metabolize glucose for use in cells for energy, resulting in high blood sugar (hyperglycemia) …

[HTML][HTML] Type 1 diabetes hypoglycemia prediction algorithms: systematic review

S Tsichlaki, L Koumakis, M Tsiknakis - JMIR diabetes, 2022‏ - diabetes.jmir.org
Background: Diabetes is a chronic condition that necessitates regular monitoring and self-
management of the patient's blood glucose levels. People with type 1 diabetes (T1D) can …

[HTML][HTML] Physiokit: An open-source, low-cost physiological computing toolkit for single-and multi-user studies

J Joshi, K Wang, Y Cho - Sensors, 2023‏ - mdpi.com
The proliferation of physiological sensors opens new opportunities to explore interactions,
conduct experiments and evaluate the user experience with continuous monitoring of bodily …

[HTML][HTML] A review of methods and applications for a heart rate variability analysis

SK Nayak, B Pradhan, B Mohanty, J Sivaraman… - Algorithms, 2023‏ - mdpi.com
Heart rate variability (HRV) has emerged as an essential non-invasive tool for
understanding cardiac autonomic function over the last few decades. This can be attributed …