Continuous glucose monitoring sensors: Past, present and future algorithmic challenges

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones

Linear parameter-varying model to design control laws for an artificial pancreas

The contribution of this work is the generation of a control-oriented model for insulin-glucose dynamic regulation in type 1 diabetes mellitus (T1DM). The novelty of this model is that it includes the time-varying nature, and the inter-patient variability of the glucose-control problem. In addition, the model is well suited for well-known and standard controller synthesis procedures. The outcome is an average linear parameter-varying (LPV) model that captures the dynamics from the insulin delivery input to the glucose concentration output constructed based on the UVA/Padova metabolic simulator. Finally, a system-oriented reinterpretation of the classical ad-hoc 1800 rule is applied to adapt the model's gain. The effectiveness of this approach is quantified both in open- and closed-loop. The first one by computing the root mean square error (RMSE) between the glucose deviation predicted by the proposed model and the UVA/Padova one. The second measure is determined by using the ν-gap as a metric to determine distance, in terms of closed-loop performance, between both models. For comparison purposes, both open- (RMSE) and closed-loop (ν-gap metric) quality indicators are also computed for other control-oriented models previously presented. This model allows the design of LPV controllers in a straightforward way, considering its affine dependence on the time-varying parameter, which can be computed in real-time. Illustrative simulations are included. In addition, the presented modeling strategy was employed in the design of an artificial pancreas (AP) control law that successfully withstood rigorous testing using the UVA/Padova simulator, and that was subsequently deployed in a clinical trial campaign where five adults remained in closed-loop for 36 h. This was the first ever fully closed-loop clinical AP trial in Argentina, and the modeling strategy presented here is considered instrumental in resulting in a very successful clinical outcome.Fil: Colmegna, Patricio Hernán. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sánchez Peña, Ricardo S.. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gondhalekar, R.. Harvard University; Estados Unido

Type 1 diabetes patient decision-making modeling for the in silico assessment of insulin treatment scenarios

In type 1 diabetes (T1D) exogenous insulin is administered to compensate for the absence of endogenous insulin production by pancreas beta-cells. T1D subjects must finely tune insulin doses to maintain blood glucose (BG) concentration within the normal range (70-180 mg/dl). For such a purpose, every day, T1D subjects need to frequently monitor their BG concentration and make several treatment decisions, e.g. the calculation of insulin and carbohydrate (CHO) doses to counterbalance, respectively, high and low BG values. The safety and effectiveness of T1D insulin therapies are normally assessed by clinical trials, which unfortunately are usually time-demanding, expensive and often present constraints of low numerosity and short duration, with consequently low probability of observing rare but risky situations, like severe hypoglycemia. These limitations can be overcome by the use of in silico clinical trials, based on computer simulations, that allow to test medical device-based treatments in a large number of subjects, over a long period, under reproducible conditions, at limited costs, and without implicating any risk for real subjects. A popular powerful tool to perform in silico clinical trials in T1D is the UVA/Padova T1D simulator, i.e. a model of glucose, insulin and glucagon dynamics in T1D subjects. However, to test insulin therapies in a real-life scenario, the UVA/Padova T1D simulator alone is not sufficient because a mathematical description of other fundamental components, like the device used for glucose monitoring and the patient's behavior in making treatment decisions, is required. The aim of this thesis is to design a mathematical model of T1D patients making treatment decisions fully usable for the comprehensive in silico assessment of insulin treatment scenarios. In particular, in the first part of the thesis we develop three submodels that the UVA/Padova T1D simulator requires (as complement) to pursue this scope. Specifically, we design a model of self-monitoring of blood glucose (SMBG) device, a model of minimally-invasive sensor for continuous glucose monitoring (CGM), and a model of the patient’s behavior in tuning CHO intakes and insulin doses according to SMBG and/or CGM measurements. The parameters of these models are either fitted on real data or derived from literature studies. The overall model, in the following called T1D decision-making (T1D-DM) model, can be used for several in silico experiments. To demonstrate its usefulness, in the second part of this thesis we apply the T1D-DM model to assess safety and effectiveness of nonadjunctive CGM use, i.e. the use of CGM measurements to make treatment decisions without requiring confirmatory SMBG measurements collected by fingerstick. This specific application is currently of great scientific and industrial interest for the diabetes technology research community because, until clinical evidence of its safety is provided, nonadjunctive CGM use cannot be approved by U.S. regulatory agencies, like the Food and Drug Administration. The thesis is organized in six chapters. In Chapter 1, after introducing T1D therapy, the importance of in silico clinical trials is discussed, both in general and specifically for the assessment of nonadjunctive CGM use. Then, some state-of-art simulation techniques are briefly introduced discussing their open problems. The aim of the thesis is illustrated at the end of the chapter. In Chapter 2, we analyse more in depth the limitations of the approaches currently available in the literature for the assessment of insulin treatments. In particular, we demonstrate that a recently proposed simulation method to "replay" in silico real-life treatment scenarios has domain of validity limited to small adjustments of basal insulin, calling for the development of more sophisticated techniques like that proposed in this thesis. In Chapter 3, our simulation method based on the T1D-DM model is presented. This model allows to simulate, in a real-life scenario, the glucose profiles of T1D subjects using SMBG and/or CGM to make treatment decisions. The T1D-DM model is composed of four components: A) the UVA/Padova T1D simulator, B) a model of glucose monitoring devices, C) a model of patient's behavior and treatment decisions and D) a model of the insulin pump. In particular, as far as B) is concerned, two different SMBG error models are derived by data collected with two popular SMBG devices (One Touch Ultra 2 and Bayer Contour Next USB). Using a recently published methodology which takes into account the main sensor error components, a CGM model is derived from data collected by a state-of-art CGM sensor (Dexcom G5 Mobile). Regarding C), a model of the patient's behavior in making treatment decisions based on SMBG and/or CGM, such as administration of insulin boluses and hypotreatments, is designed to simulate treatments based on i) SMBG, ii) adjunctive CGM, or iii) nonadjunctive CGM. In order to reproduce a real-life scenario, the model includes components describing the mistakes real subjects commonly make, such as miscalculation of meal CHO content and early/delayed insulin administrations. In Chapter 4 and Chapter 5, two in silico trials based on the T1D-DM model are designed to assess nonadjunctive CGM use. In the first trial, nonadjunctive CGM is compared to SMBG and adjunctive CGM over a two-week period in 100 virtual subjects. Results show that the use of CGM (both adjunctive and nonadjunctive) significantly improves glycemic control compared to SMBG, while no significant change is observed between adjunctive CGM and nonadjunctive CGM. This suggests that CGM is ready to substitute SMBG for T1D treatment. In the second trial, the impact of thresholds used for CGM hypo/hyperglycemic alerts on the performance of nonadjunctive CGM use is assessed. Results show that time in hypoglycemia is reduced by nonadjunctive CGM use with any alert setting, while time in hyperglycemia is significantly worsen by nonadjunctive CGM use, compared to SMBG, when the high alert threshold is set to 350 mg/dl or higher. Finally, the major findings of the work carried out in this thesis, its possible applications and margin of improvements are summarized in Chapter 6

Robust strategies for glucose control in type 1 diabetes

[EN] Type 1 diabetes mellitus is a chronic and incurable disease that affects millions of people all around the world. Its main characteristic is the destruction (totally or partially) of the beta cells of the pancreas. These cells are in charge of producing insulin, main hormone implied in the control of blood glucose. Keeping high levels of blood glucose for a long time has negative health effects, causing different kinds of complications. For that reason patients with type 1 diabetes mellitus need to receive insulin in an exogenous way. Since 1921 when insulin was first isolated to be used in humans and first glucose monitoring techniques were developed, many advances have been done in clinical treatment with insulin. Currently 2 main research lines focused on improving the quality of life of diabetic patients are opened. The first one is concentrated on the research of stem cells to replace damaged beta cells and the second one has a more technological orientation. This second line focuses on the development of new insulin analogs to allow emulating with higher fidelity the endogenous pancreas secretion, the development of new noninvasive continuous glucose monitoring systems and insulin pumps capable of administering different insulin profiles and the use of decision-support tools and telemedicine. The most important challenge the scientific community has to overcome is the development of an artificial pancreas, that is, to develop algorithms that allow an automatic control of blood glucose. The main difficulty avoiding a tight glucose control is the high variability found in glucose metabolism. This fact is especially important during meal compensation. This variability, together with the delay in subcutaneous insulin absorption and action causes controller overcorrection that leads to late hypoglycemia (the most important acute complication of insulin treatment). The proposals of this work pay special attention to overcome these difficulties. In that way interval models are used to represent the patient physiology and to be able to take into account parametric uncertainty. This type of strategy has been used in both the open loop proposal for insulin dosage and the closed loop algorithm. Moreover the idea behind the design of this last proposal is to avoid controller overcorrection to minimize hypoglycemia while adding robustness against glucose sensor failures and over/under- estimation of meal carbohydrates. The algorithms proposed have been validated both in simulation and in clinical trials.[ES] La diabetes mellitus tipo 1 es una enfermedad crónica e incurable que afecta a millones de personas en todo el mundo. Se caracteriza por una destrucción total o parcial de las células beta del páncreas. Estas células son las encargadas de producir la insulina, hormona principal en el control de glucosa en sangre. Valores altos de glucosa en la sangre mantenidos en el tiempo afectan negativamente a la salud, provocando complicaciones de diversa índole. Es por eso que los pacientes con diabetes mellitus tipo 1 necesitan recibir insulina de forma exógena. Desde que se consiguiera en 1921 aislar la insulina para poder utilizarla en clínica humana, y se empezaran a desarrollar las primeras técnicas de monitorización de glucemia, se han producido grandes avances en el tratamiento con insulina. Actualmente, las líneas de investigación que se están siguiendo en relación a la mejora de la calidad de vida de los pacientes diabéticos, tienen fundamentalmente 2 vertientes: una primera que se centra en la investigación en células madre para la reposición de las células beta y una segunda vertiente de carácter más tecnológico. Dentro de esta segunda vertiente, están abiertas varias líneas de investigación, entre las que se encuentran el desarrollo de nuevos análogos de insulina que permitan emular más fielmente la secreción endógena del páncreas, el desarrollo de monitores continuos de glucosa no invasivos, bombas de insulina capaces de administrar distintos perfiles de insulina y la inclusión de sistemas de ayuda a la decisión y telemedicina. El mayor reto al que se enfrentan los investigadores es el de conseguir desarrollar un páncreas artificial, es decir, desarrollar algoritmos que permitan disponer de un control automático de la glucosa. La principal barrera que se encuentra para conseguir un control riguroso de la glucosa es la alta variabilidad que presenta su metabolismo. Esto es especialmente significativo durante la compensación de las comidas. Esta variabilidad junto con el retraso en la absorción y actuación de la insulina administrada de forma subcutánea favorece la aparición de hipoglucemias tardías (complicación aguda más importante del tratamiento con insulina) a consecuencia de la sobreactuación del controlador. Las propuestas presentadas en este trabajo hacen especial hincapié en sobrellevar estas dificultades. Así, se utilizan modelos intervalares para representar la fisiología del paciente, y poder tener en cuenta la incertidumbre en sus parámetros. Este tipo de estrategia se ha utilizado tanto en la propuesta de dosificación automática en lazo abierto como en el algoritmo en lazo cerrado. Además la principal idea de diseño de esta última propuesta es evitar la sobreactuación del controlador evitando hipoglucemias y añadiendo robustez ante fallos en el sensor de glucosa y en la estimación de las comidas. Los algoritmos propuestos han sido validados en simulación y en clínica.[CA] La diabetis mellitus tipus 1 és una malaltia crònica i incurable que afecta milions de persones en tot el món. Es caracteritza per una destrucció total o parcial de les cèl.lules beta del pàncrees. Aquestes cèl.lules són les encarregades de produir la insulina, hormona principal en el control de glucosa en sang. Valors alts de glucosa en la sang mantinguts en el temps afecten negativament la salut, provocant complicacions de diversa índole. És per això que els pacients amb diabetis mellitus tipus 1 necessiten rebre insulina de forma exògena. Des que s'aconseguís en 1921 aïllar la insulina per a poder utilitzar-la en clínica humana, i es començaren a desenrotllar les primeres tècniques de monitorització de glucèmia, s'han produït grans avanços en el tractament amb insulina. Actualment, les línies d'investigació que s'estan seguint en relació a la millora de la qualitat de vida dels pacients diabètics, tenen fonamentalment 2 vessants: un primer que es centra en la investigació de cèl.lules mare per a la reposició de les cèl.lules beta i un segon vessant de caràcter més tecnològic. Dins d' aquest segon vessant, estan obertes diverses línies d'investigació, entre les que es troben el desenrotllament de nous anàlegs d'insulina que permeten emular més fidelment la secreció del pàncrees, el desenrotllament de monitors continus de glucosa no invasius, bombes d'insulina capaces d'administrar distints perfils d'insulina i la inclusió de sistemes d'ajuda a la decisió i telemedicina. El major repte al què s'enfronten els investigadors és el d'aconseguir desenrotllar un pàncrees artificial, és a dir, desenrotllar algoritmes que permeten disposar d'un control automàtic de la glucosa. La principal barrera que es troba per a aconseguir un control rigorós de la glucosa és l'alta variabilitat que presenta el seu metabolisme. Açò és especialment significatiu durant la compensació dels menjars. Aquesta variabilitat junt amb el retard en l'absorció i actuació de la insulina administrada de forma subcutània afavorix l'aparició d'hipoglucèmies tardanes (complicació aguda més important del tractament amb insulina) a conseqüència de la sobreactuació del controlador. Les propostes presentades en aquest treball fan especial insistència en suportar aquestes dificultats. Així, s'utilitzen models intervalares per a representar la fisiologia del pacient, i poder tindre en compte la incertesa en els seus paràmetres. Aquest tipus d'estratègia s'ha utilitzat tant en la proposta de dosificació automàtica en llaç obert com en l' algoritme en llaç tancat. A més, la principal idea de disseny d'aquesta última proposta és evitar la sobreactuació del controlador evitant hipoglucèmies i afegint robustesa.Revert Tomás, A. (2015). Robust strategies for glucose control in type 1 diabetes [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/56001TESI

Reinforcement learning application in diabetes blood glucose control: A systematic review

Background: Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen based exclusively on the patient’s own data. Objective: In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in DM patients, reporting successfully implemented RL algorithms in closed-loop, insulin infusion, decision support and personalized feedback in the context of DM. Methods: An exhaustive literature search was performed using different online databases, analyzing the literature from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most relevant papers according to the title, keywords and abstract. Research questions were established and answered in a second stage, using the information extracted from the articles selected during the preliminary selection. Results: The initial search using title, keywords, and abstracts resulted in a total of 404 articles. After removal of duplicates from the record, 347 articles remained. An independent analysis and screening of the records against our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51 relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29 relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. Conclusions: The advances in health technologies and mobile devices have facilitated the implementation of RL algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area, therefore we foresee RL algorithms will be used more frequently for BG control in the coming years. Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform clinical validation of the algorithms

CGM based basal-insulin titration in insulin-naïve type 2 diabetic subjects: an in-silico study

openLa fisiopatologia del diabete di tipo 2 (T2D) consiste in un malfunzionamento dei circuiti di feedback che coinvolgono la secrezione di insulina (disfunzione delle cellule β) e/o l'azione dell'insulina (stato di resistenza insulinica), il quale malfunzionamento porta il soggetto in uno stato di iperglicemia (livello di glucosio nel sangue elevato). Con la progressione della malattia (T2D in stato avanzato), i soggetti possono richiedere la somministrazione di insulina esogena per controllare la glicemia: l’azione combinata di insuline ad azione rapida per il controllo durante i pasti ed insuline ad azione prolungata per il controllo della concentrazione glicemica pre-prandiale e postprandiale. L’attuale procedura per determinare la dose ottimale di insulina basale in soggetti che non hanno mai utilizzato in precedenza l’insulina per il trattamento del diabete (naïve all’insulina) consiste in una regola di titolazione non personalizzata. Secondo le linee guida ADA, i soggetti devono iniziare la titolazione con una dose di insulina bassa che viene fatta variare seguendo incrementi e decrementi predefiniti: tali variazioni sono basate sull’automonitoraggio (SMBG) a digiuno della glicemia; lo scopo è di raggiungere un livello target di glicemia a digiuno (FPG). Lo scopo di questa tesi è sviluppare una regola di titolazione dell'insulina basale personalizzata basata sui bisogni specifici di insulina dei soggetti. In particolare, è stato utilizzato il monitoraggio continuo del glucosio (CGM) e delle relative metriche temporali ricavate da tale segnale, le quali sono utilizzate dai medici per valutare la qualità del controllo glicemico: il tempo speso al di sopra dell'intervallo glicemico target (TAR), tempo speso nell'intervallo glicemico target (TIR) e tempo sotto l'intervallo glicemico target (TBR). La popolazione utilizzata è composta da 300 soggetti virtuali, ai quali è stata somministrata lo stato dell’arte delle regole per la titolazione dell’insulina basale (DUAL I). I soggetti sono stati quindi classificati utilizzando un modello di regressione logistica precedentemente addestrato. In particolare, sono stati divisi in base alla loro dose finale di insulina tra alto fabbisogno insulinico (HIN) e basso fabbisogno insulinico (LIN). I soggetti classificati come HIN sono stati titolati utilizzando i quattro nuovi algoritmi. Le metriche temporali del segnale GCM ottenute dalle nuove regole di titolazione sono state confrontate con quelle ottenute utilizzando DUAL I. Tra i nuovi algoritmi testati il migliore risulta essere la quarta versione. Tale risultato è il prodotto di una selezione che ha considerato la correlazione tra la dose finale di insulina somministrata da DUAL I e quella di ciascun algoritmo di titolazione (per il quarto algoritmo: ρ= 0,82, pvalue<10-8). L’applicazione del nuovo algoritmo ha mostrato un aumento statisticamente e clinicamente significativo del TIR, nonché una diminuzione significativa del TAR accompagnata da una riduzione significativa del FPG. Lo svantaggio principale è stato un aumento statisticamente significativo della TBR fino al terzo mese; tuttavia, dopo questo periodo questa differenza non è risultata più significativa. Nonostante i buoni risultati complessivamente ottenuti, potrebbero essere apportati miglioramenti in futuro. Nella fattispecie, si possono considerare gli altri trend per aggiungere informazioni significative che migliorino il processo decisionale. Inoltre, si potrebbero condurre altri studi su come regolare l’aggressività degli algoritmi oggetto di questa tesi.The pathophysiology of the type 2 diabetes (T2D) consists in a malfunctioning of the feedback loops between insulin secretion (β-cell disfunction) and/or insulin action (insulin resistance state) leading to an abnormally high blood glucose level. With the progression of the disease (advance stage T2D), subjects may need exogenous insulin to control their glycaemia, using fast acting insulins during meals and/or long-acting insulins, to control fasting (pre-breakfast) and postprandial glucose concentration. The current procedure to determine the optimal basal insulin dose in subjects who have never previously used insulin to treat diabetes (insulin-naïve) consists in a non-personalized titration rule. According to ADA guidelines, subjects must start the titration with a low insulin dose that is progressively adjusted, following predefined increments/decrements, based only on self-monitoring blood glucose (SMBG) pre-breakfast measurements (Gpre), to reach a target fasting glucose level. The aim of this thesis is to develop a more personalized basal insulin titration rule based on subjects’ specific insulin needs, continuous glucose monitoring (CGM) and common CGM metrics used by clinician to assess the quality of glucose control i.e., time above range (TAR), time in range (TIR), and time below range (Tb). We used a dataset consisting of 300 in silico subjects who underwent a literature titration rule (DUAL I). Subjects were then classified as high insulin needs (HIN) and low insulin needs (LIN), based on their final insulin dose, using a literature logistic regression model. The classified HIN subjects underwent four new rules and their GCM time metrics were compared with the ones obtained using DUAL I. Among the new tested rules, the best one, which is selected in terms of higher correlation with DUAL I final insulin dose (ρ=0.85, p-value<10-8), showed a statistically and clinically significant increase of TIR, as well as a significant decrease of TAR accompanied by a significant reduction in the GPre. The main drawback was a statistically significant increase in the Tb until the third month, anyway after this period this difference was not significant anymore. Despite the overall good results achieved, improvements could be made in the future, looking if other trends can add significant features which improve the decision process, but also making studies on how to tune the aggressiveness of the rules object of this thesis

Exploring the Power and Promise of In Silico Clinical Trials with Application in COVID-19 Infection

Background: COVID-19 pandemic has dramatically engulfed the world causing catastrophic damage to human society. Several therapeutic and vaccines have been suggested for the disease in the past months, with over 150 clinical trials currently running or under process. Nevertheless, these trials are extremely expensive and require a long time, which presents the need for alternative cost-effective methods to tackle this urgent requirement for validated therapeutics and vaccines. Bearing this in mind, here we assess the use of in silico clinical trials as a significant development in the field of clinical research, which holds the possibility to reduce the time and cost needed for clinical trials on COVID-19 and other diseases. Methods: Using the PubMed database, we analyzed six relevant scientific articles regarding the possible application of in silico clinical trials in testing the therapeutic and investigational methods of managing different diseases. Results: Successful use of in silico trials was observed in many of the reviewed evidence. Conclusion: In silico clinical trials can be used in refining clinical trials for COVID-19 infection. Keywords: in silico, clinical trials, COVID-19, SARS-CoV-2, vaccine Ho

Model-Based Analysis of User Behaviors in Medical Cyber-Physical Systems

Human operators play a critical role in various Cyber-Physical System (CPS) domains, for example, transportation, smart living, robotics, and medicine. The rapid advancement of automation technology is driving a trend towards deep human-automation cooperation in many safety-critical applications, making it important to explicitly consider user behaviors throughout the system development cycle. While past research has generated extensive knowledge and techniques for analyzing human-automation interaction, in many emerging applications, it remains an open challenge to develop quantitative models of user behaviors that can be directly incorporated into the system-level analysis. This dissertation describes methods for modeling different types of user behaviors in medical CPS and integrating the behavioral models into system analysis. We make three main contributions. First, we design a model-based analysis framework to evaluate, improve, and formally verify the robustness of generic (i.e., non-personalized) user behaviors that are typically driven by rule-based clinical protocols. We conceptualize a data-driven technique to predict safety-critical events at run-time in the presence of possible time-varying process disturbances. Second, we develop a methodology to systematically identify behavior variables and functional relationships in healthcare applications. We build personalized behavior models and analyze population-level behavioral patterns. Third, we propose a sequential decision filtering technique by leveraging a generic parameter-invariant test to validate behavior information that may be measured through unreliable channels, which is a practical challenge in many human-in-the-loop applications. A unique strength of this validation technique is that it achieves high inter-subject consistency despite uncertain parametric variances in the physiological processes, without needing any individual-level tuning. We validate the proposed approaches by applying them to several case studies

Modeling and control to improve blood glucose concentration for people with diabetes

Diabetes mellitus is a chronical condition that features either the lack of insulin or increased insulin resistance. It is a disorder in the human metabolic system. To combat insufficiency of insulin released by pancreas, a closed-loop control system, also known as artificial pancreas (AP) in this application, have been created to mimic the functionality of a human pancreas. An AP is used to regulate blood glucose concentration (BGC) by managing the release of insulin. Therefore, an algorithm, which can administer insulin to reduce the variation of BGC and minimize the occurrences of hyper-/ hypoglycemia episodes, is the key component of an AP. The objective of the dissertation is to develop an optimal algorithm to better control BGC for people with diabetes. For people with Type 2 diabetes, prevention or treatment of diabetes mellitus can typically be done via a change of lifestyle and weight management. A virtual sensing system that does not require many manual inputs from patients can ease the burden for people with Type 2 diabetes. This dissertation covers the development of a monitoring system for Type 2 diabetes. To achieve the goal of tighter control of BGC for people with Type 1 diabetes, dynamic modeling methodology for capturing the cause-and-effect relationship between manipulated variable (i.e. insulin) and controlled variable (i.e. BGC) has been developed. Theoretically, this dissertation has established that physiologically based nonlinear parameterized wiener models being superior to nonlinear autoregressive moving average with exogenous inputs (NARMAX) models in capturing dynamic relationships in processes with correlated inputs. Based on these results, wiener models have been applied in the modeling of BGC for real subjects with Type 1 diabetes under free-living conditions. With promising results shown in wiener models, an extended physiologically based model (i.e. semi-coupled model) has been developed from wiener structure, which enables the development of a phenomenologically sound feedforward control law. The feedforward control law based on wiener models has been tested in simulated continuous-stirred-tank reactor (CSTR) that demonstrates tight control of controlled variables. Further simulation runs with a CSTR also shows feedforward predictive control (FFPC) can provide tighter control over model predictive control (MPC). Lastly, for the special application of BGC control for people with Type 1 diabetes, FFPC demonstrates tighter control than MPC under simulation environment. To account for unmeasured disturbances and inaccurate models for manipulated variable in real life scenarios, feedback predictive control (FBPC) is developed and proven to be a more effective control algorithm under both CSTR and diabetes simulation environment, which can establish the foundation for tightening BGC in real subject clinical studies