Design of a Fog Computing, Blockchain and IoT-Based Continuous Glucose Monitoring System for Crowdsourcing mHealth

[Abstract] Diabetes Mellitus, usually called only Diabetes, is a worldwide chronic metabolic disorder that is characterized by abnormal oscillations in blood sugar levels. Such levels should be monitored by diabetes patients, which traditionally have had to take blood samples by finger-pricking, at least between twice and four times a day. Finger-pricking has a number of drawbacks that can be tackled by Continuous Glucose Monitors (CGMs), which are able to determine blood sugar levels throughout the day and not only at specific time instants. In this paper, the design of an IoT CGM-based system is proposed, whose collected blood sugar sample values can be accessed remotely; thus being able to monitor patients, specifically dependent ones (e.g., children, elders, and pregnant women) and warn them in the case where a dangerous situation is detected. In order to create such a system, a fog computing system, based on distributed mobile smart phones, has been devised to collect data from the CGMs. Moreover, the use of a blockchain is proposed, to receive, validate, and store the collected data with the objective of avoiding untrusted sources and, thus, to provide a transparent and trustworthy data source of a population, which can vary in age, ethnicity, psychology, education, self-care, and/or geographic location, in a rapid, flexible, scalable, and low-cost way. These crowdsourced data can enable novel mHealth applications for diagnosis, patient monitoring, or even public health actions, which can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care

[Abstract] Diabetes patients suffer from abnormal blood glucose levels, which can cause diverse health disorders that affect their kidneys, heart and vision. Due to these conditions, diabetes patients have traditionally checked blood glucose levels through Self-Monitoring of Blood Glucose (SMBG) techniques, like pricking their fingers multiple times per day. Such techniques involve a number of drawbacks that can be solved by using a device called Continuous Glucose Monitor (CGM), which can measure blood glucose levels continuously throughout the day without having to prick the patient when carrying out every measurement. This article details the design and implementation of a system that enhances commercial CGMs by adding Internet of Things (IoT) capabilities to them that allow for monitoring patients remotely and, thus, warning them about potentially dangerous situations. The proposed system makes use of smartphones to collect blood glucose values from CGMs and then sends them either to a remote cloud or to distributed fog computing nodes. Moreover, in order to exchange reliable, trustworthy and cybersecure data with medical scientists, doctors and caretakers, the system includes the deployment of a decentralized storage system that receives, processes and stores the collected data. Furthermore, in order to motivate users to add new data to the system, an incentive system based on a digital cryptocurrency named GlucoCoin was devised. Such a system makes use of a blockchain that is able to execute smart contracts in order to automate CGM sensor purchases or to reward the users that contribute to the system by providing their own data. Thanks to all the previously mentioned technologies, the proposed system enables patient data crowdsourcing and the development of novel mobile health (mHealth) applications for diagnosing, monitoring, studying and taking public health actions that can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-045Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

A hybrid automata approach for monitoring the patient in the loop in artificial pancreas systems

The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.Peer ReviewedPostprint (published version

Classification of postprandial glycemic status with application to insulin dosing in type 1 diabetes—an in silico proof-of-concept

In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (± 13.89) to 67.00 (± 11.54; p &lt; 0.01) without increasing hypoglycemia.</jats:p

Implementazione e valutazione di un algoritmo per la rilevazione di malfunzionamenti nei set di infusione per il diabete

Il pancreas artificiale e la pompa di insulina sono dispositivi biomedici che hanno migliorato di molto la qualità della vita dei pazienti malati di diabete di tipo 1. Questi strumenti sono però soggetti ad occasionali malfunzionamenti meccanici che possono incidere gravemente sul controllo del glucosio nel paziente, soprattutto se crede di ricevere regolarmente la terapia. Grazie all'utilizzo di sensori per il glucosio minimamente invasivi abbiamo accesso ad un monitoraggio continuo del glucosio (Continue Glucose Monitoring CGM), ed è quindi possibile implementare degli algoritmi per l'autodiagnostica per allertare il paziente di un possibile guasto. In questo elaborato analizzeremo l'algoritmo per la rilevazione di malfunzionamenti nell'attuazione dell'infusion set o della pompa di insulina ideato da Daniel P. Howsmon ed i suoi collaboratori, per verificarne l'efficacia. Per farlo utilizzeremo i dati di pazienti in silico generati con il simulatore UVa/Padova

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs)

Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis—a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios

Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs)

Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis—a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 407)

This bibliography lists 289 reports, articles and other documents announced in the NASA Scientific and Technical Information System during Nov. 1995. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance