The Diabetes Assistant: A Smartphone-Based System for Real-Time Control of Blood Glucose

被引:49
|
作者
Keith-Hynes, Patrick [1 ]
Mize, Benton [1 ]
Robert, Antoine [1 ]
Place, Jerome [2 ]
机构
[1] Univ Virginia, Ctr Diabet Technol, POB 400888, Charlottesville, VA 22903 USA
[2] Univ Montpellier I, Montpellier Univ Hosp, CNRS, INSERM,Inst Funct Genom,Dept Endocrinol Diabet &, Montpellier, France
来源
ELECTRONICS | 2014年 / 3卷 / 04期
关键词
Type 1 Diabetes Mellitus (T1DM); artificial pancreas (AP); closed loop control (CLC); Diabetes Assistant (DiAs); Continuous Glucose Monitoring (CGM);
D O I
10.3390/electronics3040609
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Type 1 Diabetes Mellitus (T1DM) is an autoimmune disease in which the insulin-producing beta cells of the pancreas are destroyed and insulin must be injected daily to enable the body to metabolize glucose. Standard therapy for T1DM involves self-monitoring of blood glucose (SMBG) several times daily with a blood glucose meter and injecting insulin via a syringe, pen or insulin pump. An "Artificial Pancreas" (AP) is a closed-loop control system that uses a continuous glucose monitor (CGM), an insulin pump and an internal algorithm to automatically manage insulin infusion to keep the subject's blood glucose within a desired range. Although no fully closed-loop AP systems are currently commercially available there are intense academic and commercial efforts to produce safe and effective AP systems. In this paper we present the Diabetes Assistant (DiAs), an ultraportable AP research platform designed to enable home studies of Closed Loop Control (CLC) of blood glucose in subjects with Type 1 Diabetes Mellitus. DiAs consists of an Android (Google Inc., Mountain View, CA, USA) smartphone equipped with communication, control and user interface software wirelessly connected to a continuous glucose monitor and insulin pump. The software consists of a network of mobile applications with well-defined Application Programming Interfaces (APIs) running
引用
收藏
页码:609 / 623
页数:15
相关论文
共 50 条
  • [1] Smartphone-based drowsiness detection system for drivers in real-time
    Chatterjee, Iman
    Roy, Sarbani
    13TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED NETWORKS AND TELECOMMUNICATION SYSTEMS (IEEE ANTS), 2019,
  • [2] A Smartphone-Based System for Real-Time Early Childhood Caries Diagnosis
    Zhang, Yipeng
    Liao, Haofu
    Xiao, Jin
    Al Jallad, Nisreen
    Ly-Mapes, Oriana
    Luo, Jiebo
    MEDICAL ULTRASOUND, AND PRETERM, PERINATAL AND PAEDIATRIC IMAGE ANALYSIS, ASMUS 2020, PIPPI 2020, 2020, 12437 : 233 - 242
  • [3] A Real-Time Smartphone-Based Floor Detection System For The Visually Impaired
    Delahoz, Yueng
    Labrador, Miguel A.
    2017 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA), 2017, : 27 - 32
  • [4] Smartphone-Based Real-Time Digital Signal Processing
    Electrical Engineering, University of Texas at Dallas, United States
    Synth. Lect. Signal Process., 1 (1-159):
  • [5] Smartphone-Based Real-Time Digital Signal Processing
    Kehtarnavaz, Nasser
    Parris, Shane
    Sehgal, Abhishek
    Synthesis Lectures on Signal Processing, 2015, 13 : 1 - 157
  • [6] A Smartphone-Based Real-Time Simple Activity Recognition
    Jongprasithporn, Manutchanok
    Yodpijit, Nantakrit
    Srivilai, Rawiphorn
    Pongsophane, Paweena
    2017 3RD INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR), 2017, : 539 - 542
  • [7] Clinical efficacy of a smartphone-based integrated online real-time diabetes care system in Type 2 diabetes patients
    Broz, Jan
    INTERNAL MEDICINE JOURNAL, 2021, 51 (03) : 464 - 464
  • [8] A Smartphone-Based Adaptive Recognition and Real-Time Monitoring System for Human Activities
    Qi, Wen
    Su, Hang
    Aliverti, Andrea
    IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2020, 50 (05) : 414 - 423
  • [9] Impact of the use of a smartphone-based blood glucose management system within a maternal diabetes service
    Hyams, Elizabeth
    Brackenridge, Anna
    White, Sara L.
    PRACTICAL DIABETES, 2024, 41 (03) : 16 - 23
  • [10] COPDTrainer: A Smartphone-based Motion Rehabilitation Training System with Real-Time Acoustic Feedback
    Spina, Gabriele
    Huang, Guannan
    Vaes, Anouk W.
    Spruit, Martijn A.
    Amft, Oliver
    UBICOMP'13: PROCEEDINGS OF THE 2013 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, 2013, : 597 - 606