The MICELab group conducts interdisciplinary research on modeling, identification, and control engineering, with a focus on biomedical systems like diabetes technologies. The team has over 12 years experience researching diabetes and has developed tools like glucose prediction models, bolus calculators, closed-loop insulin delivery, and mobile applications to manage diabetes and analyze clinical data. They collaborate with food companies and restaurants to develop products and guidelines to help diabetics control their blood glucose levels in response to meals.

1. JORNADA INTERACTIVA
CAMPUS ALIMENTACIÓ
GASTRONOMIA
UDG - IRTA
Iván Contreras
Modelling, Identification & Control
Engineering Laboratory

2. The MICELab group is an
interdisciplinary research group of
the Institute of Informatics and
Applications of the University of Girona
involved in national and international
research and transfer projects.
The team is composed of experienced
researchers from the control
engineering and computer science fields
with expertise in systems and control
theory, modelling and control of
biomedical systems, uncertain
dynamical systems, robust and
predictive control and decision support
systems.

3. More than 12 years researching in the diabetes
technologies.
The research results are being transferred to clinical
practice as therapies, support systems for the
adjustment of insulin pumps, automatic and semi-
automatic bolus calculators and decision support
systems.
- OVERVIEW AND INFRASTRUCTURE
CLINICAL DATA-BASE
PLATFORM TO MONITOR CLINICAL TRIALS
DIABETIC PATIENT SIMULATOR
• Intra-patient variability
• Library with the effects of eating mixed meals
• Exercise and failures in insulin pumps and sensors
• Application and validation of glucose controls (CL4M controls).
• Already validated and approved by the Spanish Agency of Medicines
and Sanitary Products (AEMPS)
• More than 120 patients
• Over 1,500 hours of continuous monitoring and control
• Open and closed loop systems.
MOBILE PLATFORM PROTOTYPES
• jAP - Mobile artificial pancreas
• Smart Diabetes – Mobile diabetes management

4. • Prediction tools [3,7,9]
– Short and mid term (hours) blood glucose prediction
– Postprandial hypoglycemia risk assessment
– Mid term (months) A1c and risk of hypoglycemia prediction
• Decisions support tools (model and data-driven combined):
– Bolus calculators, Insulin dosage systems for insulin pumps [8, 10]
– Semi-closed loop automatic insulin delivery for pumps [2, 11]
– Bolus supervisors and postprandial risk assessment*
• Analysis tools:
– Profiling tools: identification and clustering of different behaviors and insulin requirements for
individual patients [1]
– Therapy adjustment tools based on patients profiles (and comparison with other patients profiles
with similar behavior).
• Safety tools
– Fault detection (leakages, occlusions) in insulin pumps [4]
– Detection of correct and incorrect measurements in CGM [5]
– Insulin-on-Board limitations according to patient condition [6]
• Exercise management tools (clinical trial recently finished)
– Insulin delivery management
– Hypoglycemia prediction and alarms
– Risk mitigation recommendations before, during and after exercise (including carbs intake)
* not published but clinically validated with retrospective data
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5. 5
Glucose Carbohydrates Insulin
?
o If my lunch is this marvelous paella and I administer
myself 300 insulin units...
o Will I have glucose values under control within one
hour?
PROBLEM: BLOOD
GLUCOSE CONTROL
G(t) + CHO(t) – IN(t)
(t+60)=
We could gather
• Glucose
• Insulin
• Carbohydrates
• Exercise
• …

6. PROBLEM: INTER-PATIENT
VARIABILITY
6
Afraid of
technologies
Lack of diet
education
Good diabetes
management
Elderly
Casual
patient
Expert
patient

7. 7
PROBLEM:
INTRA-PATIENT VARIABILITY
Stressful days Relaxed days

8. PROTOTYPES: SMART DIABETES AND JAP

9. APORTACIONS AL CAMPUS
• Asesoramiento en el desarrollo de nuevos productos para la empresa
alimentaria.
• Análisis del perfil glicémico y absorción de carbohidratos para alimentos y
menús que los haga más aptos para pacientes con diabetes.
• Asesoramiento a empresas de alimentación en el desarrollo de alimentos
con bajo índice glicémico para diabéticos.
• Asesoramiento en el desarrollo de menús y guías de restaurantes para
atender la población diabética.
• Formación dirigida a empresas alimentarias y restauradores sobre los
efectos del comidas en el control de glucemia en diabéticos.
• Herramientas de Inteligencia artificial, análisis de datos, modelos predictivos
e identificación de patrones.
• Experiencia con sensores cuantificadores (glucosa, heart rate, activity, etc.)
• Seguimiento de pacientes / usuarios.

10. MICELab
THANK YOU!
Iván Contreras
 ivancontrerasfd@Gmail.com
Contact

11. 1. Contreras I, C Quirós, M Giménez, I Conget, J Vehi Profiling intra-patient type I diabetes behaviors. Computer
Methods and Programs in Biomedicine 136, 131-141, 2016
2. Leon-Vargas, F.; et al. 2015. Postprandial response improvement via safety layer in closed-loop blood glucose
controllers. Biomedical Signal Processing and Control. Elsevier. 16, pp.80-87.
3. Laguna, A.J.; et al. 2014. Experimental blood glucose interval identification of patients with type 1 diabetes.
Journal of Process Control. 24-1, pp.171-181.
4. P. Herrero, R. Calm, J. Vehi, J. Armengol, P. Georgiou, N. Oliver, C. Tomazou, 2012, Robust fault detection
system for insulin pump therapy using continuous glucose monitoring, Journal of Diabetes Science and
Technology, 6(5), 1131-1141,
5. Leal, Y.; et al. 2013. Detection of correct and incorrect measurements in real-time continuous glucose
monitoring systems by applying a post-processing support vector machine. IEEE Transactions on Biomedical
Engineering. 60-7, pp.1891-1899.
6. Revert, A.; et al. 2013. Safety auxiliary feedback element for the artificial pancreas in type 1 diabetes. IEEE
Transactions on Biomedical Engineering. Institute of Electrical and Electronics Engineers (IEEE). 60-8, pp.2113-
2122. ISSN 0018-9294.
7. García-Jaramillo, R. Calm, J. Bondia, J. Vehí; Prediction of postprandial blood glucose under uncertainty and
intra-patient variability in type 1 diabetes: a comparative study of three interval models; Computer Methods
and Programs in Biomedicine, 108(1), 224-233, 2012
8. M. García-Jaramillo; et al. 2012. Insulin dosage optimization based on prediction of postprandial glucose
excursions under uncertain parameters and food intake. Computer Methods and Programs in Biomedicine.
Elsevier. 105-1, pp.61-69. ISSN 0169-2607. 2
9. Calm, R.; et al. 2011. Comparison of interval and monte carlo simulation for the prediction of postprandial
glucose under uncertainty in type 1 diabetes mellitus. Computer Methods and Programs in Biomedicine.
Elsevier. 104-3, pp.325-332. ISSN 0169-2607.
10. A Revert, R Calm, J Vehí, J Bondia 2011 Calculation of the best basal–bolus combination for postprandial
glucose control in insulin pump therapy Biomedical Engineering, IEEE Transactions on 58 (2), 274-281
11. WO2016120514 (A1) - Computer Program and Method for Determining and Temporally Distributing a Dose of
Insulin to a User
Selected references
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