Efficient acceleration of deep learning inference on resource-constrained edge devices: A review
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …
in breakthroughs in many areas. However, deploying these highly accurate models for data …
Embedded machine learning using microcontrollers in wearable and ambulatory systems for health and care applications: A review
MS Diab, E Rodriguez-Villegas - IEEE Access, 2022 - ieeexplore.ieee.org
The use of machine learning in medical and assistive applications is receiving significant
attention thanks to the unique potential it offers to solve complex healthcare problems for …
attention thanks to the unique potential it offers to solve complex healthcare problems for …
[PDF][PDF] Control engineering methods for blood glucose levels regulation
In this article, we review recently proposed, advanced methods, for the control of blood
glucose levels, in patients with type 1 diabetes. The proposed methods are based on …
glucose levels, in patients with type 1 diabetes. The proposed methods are based on …
[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 …
management of the patient's blood glucose levels. People with type 1 diabetes (T1D) can …
Glugan: generating personalized glucose time series using generative adversarial networks
Time series data generated by continuous glucose monitoring sensors offer unparalleled
opportunities for developing data-driven approaches, especially deep learning-based …
opportunities for developing data-driven approaches, especially deep learning-based …
Long-term glucose forecasting for open-source automated insulin delivery systems: a machine learning study with real-world variability analysis
Glucose forecasting serves as a backbone for several healthcare applications, including real-
time insulin dosing in people with diabetes and physical activity optimization. This paper …
time insulin dosing in people with diabetes and physical activity optimization. This paper …
Noninvasive diabetes detection through human breath using TinyML-Powered E-Nose
A Gudiño-Ochoa, JA García-Rodríguez… - Sensors, 2024 - mdpi.com
Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers
for disease identification and medical diagnostics. In the context of diabetes mellitus, the …
for disease identification and medical diagnostics. In the context of diabetes mellitus, the …
[HTML][HTML] Empowering Healthcare: TinyML for Precise Lung Disease Classification
Respiratory diseases such as asthma pose significant global health challenges,
necessitating efficient and accessible diagnostic methods. The traditional stethoscope is …
necessitating efficient and accessible diagnostic methods. The traditional stethoscope is …
Population-specific glucose prediction in diabetes care with transformer-based deep learning on the edge
Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG)
forecasting is essential for proactive interventions, playing a crucial role in enhancing the …
forecasting is essential for proactive interventions, playing a crucial role in enhancing the …
Platform for precise, personalised glucose forecasting through continuous glucose and physical activity monitoring and deep learning
Emerging research has demonstrated the advantage of continuous glucose monitoring for
use in artificial pancreas and diabetes management in general. Recent studies demonstrate …
use in artificial pancreas and diabetes management in general. Recent studies demonstrate …