COMMODITY12: A smart e-health environment for diabetes management

Kafalı, Özgür; Bromuri, Stefano; Sindlár, M; Weide, Tom van der; Pelaez, Eduardo Aguilar; Schaechtle, Ulrich; Alves, Bruno; Zufferey, Damien; Rodriguez‐Villegas, Esther; Schumacher, Michael; Stathis, Kostas

doi:10.3233/ais-130220

Cited by 51 publications

(38 citation statements)

References 54 publications

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“…We have implemented a prototype agent to realize our activity recognition process in GOLEM, 63 the agent platform we used to implement agents in COMMODITY 12 . 3 Our agent can reason about data produced by our activity generator and raises alerts when life-threatening situations are recognized. However, we have not deployed this agent as a stand alone application for a mobile phone that a diabetic patient can use.…”

Section: Discussionmentioning

confidence: 99%

“…From our involvement in the FP7 COMMODITY 12 project, we have been particularly preoccupied with developing a monitoring agent that is a specialized version of the KGP model. 1,2 Such an agent diagnoses, 3 ontologically reasons about 4 and together with specialized agents predict 5 medical emergencies such as hypoglycemia. According to the International Classification of Diseases, hypoglycemia is defined as the patient's glucose level being below a certain threshold value.…”

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confidence: 99%

“…• We provide a specification for an activity recognition capability that is integrated within the logic-based agent architecture discussed in Kafalı et al 3 The activity recognition capability supports reasoning about complex activities from the recognition of basic events. It relies on the identification of an activity life cycle that treats activities as special temporal fluents that can be started, interrupted, suspended, resumed, or completed over time.…”

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confidence: 99%

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Agent‐oriented activity recognition in the event calculus: An application for diabetic patients

Kafalı

Romero

Stathis

2017

Computational Intelligence

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We present a knowledge representation framework on the basis of the Event Calculus that allows an agent to recognize complex activities from low‐level observations received by multiple sensors, reason about the life cycle of such activities, and take action to support their successful completion. Activities are multivalue fluents that change according to events that occur in the environment. The parameters of an activity consist of a unique label, a set of participants involved in the performing of the activity, and a unique goal associated with the activity revealing the activity's desired outcome. Our contribution is the identification of an activity life cycle describing how activities can be started, interrupted, suspended, resumed, or completed over time, as well as how these can be represented. The framework also specifies activity goals, their associated life cycle, and their relation with the activity life cycle. We provide the complete implementation of the framework, which includes an activity generator that automatically creates synthetic sensor data in the form of event streams that represent the everyday lifestyle of a type 1 diabetic patient. Moreover, we test the framework by generating very large activity streams that we use to evaluate the performance of the recognition capability and study its relative merits.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Agent‐oriented activity recognition in the event calculus: An application for diabetic patients

Kafalı

Romero

Stathis

2017

Computational Intelligence

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“…On the other hand, it should be mentioned another approach, called COMMODITY12 [7], which is presented as a personal-health system to assist diabetic patients. This proposition consists of wearable and portable devices that acquire vital body signs, which are interpreted by algorithms based on expert knowledge.…”

Section: Telemedicinementioning

confidence: 99%

“…In this sense, well-known improvements in computer science are able to tackle these challenges, and, at this point, technologies like smartphones and ideas, like the Internet of Things (IoT), Big Data, and Cloud Computing arise, which are powerful options for dealing with a huge quantity of information. In this sense, recent developments have introduced both the idea of a Body Area Network (BAN)-along with some other sensors-to gather relevant information and the use of platforms to display data and ease the diabetes management [7]. This way, the most relevant approaches in this field have been properly summarized by Lanzola et al [8].…”

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Towards an ICT-Based Platform for Type 1 Diabetes Mellitus Management

2018

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Type 1 Diabetes Mellitus (DM1) is a metabolic disease that is characterized by chronic hyperglycemia due to a lack of pancreatic insulin production. This forces patients to perform several blood glucose measurements per day-by means of capillary glucometers-in order to infer a trend and try to predict future values. In this way, a decision about the insulin dosage that has to be exogenously injected to maintain glycemia within the desirable levels is made. Unfortunately, this method usually suffers from relatively high imprecision. However, recent advances in information and communication technologies (ICT), along with novel biosensors that could provide a real-time comprehensive condition of the patient, offer a new perspective in DM1 management. In this sense, new disruptive technologies like Big Data, the Internet of Things (IoT), and Cloud Computing, as well as Machine Learning (ML) can play an important role in managing DM1. In this work, firstly, an analysis of previously published ICT-based methods for the management of diabetes continuous monitoring is carried out. In this way, an assessment of the possible lack of such proposals is presented, along with the challenges to be overcome in forthcoming smart DM1 management systems. Finally, an overview of a holistic ICT-based platform for DM1 management that try to solve the limitations of previous works, while at the same time, taking advantage of the abovementioned disruptive technologies is hereby proposed.Appl. Sci. 2018, 8, 511 2 of 15 either by multiple daily injections or by continuous infusion, using a pump, which permeates under the skin.In this context, new technological possibilities offer a new horizon in diabetes management. Although the first steps towards an artificial pancreas (AP) were undertaken 50 years ago [1], when the concept of a computer-supported system emulating the behavior of a pancreas by using a control algorithm was introduced, promising that technological advances achieved in recent years are supposed to bring a revolution in this field. Nowadays, an AP (which has not been completely achieved yet) is presumed to be composed of a Continuous Glucose-Monitoring (CGM) device aimed at checking the patient's glucose levels in real time (a feature that is already available) and injecting (a feature that is not yet commercially available) insulin (and glucagon, if applicable) into his or her body, along with a control system based on a closed loop, which must decide the amount of hormone (insulin or glucagon) to be injected [2,3].2017 was a particularly significant year regarding the AP [4]. Clinical trials were performed and their results were published, showing a good performance under real and demanding situations. Even a commercial hybrid closed-loop system is commercially available at present, Medtronic MiniMed 670G, which automatically adjusts basal insulin every five minutes based on CGM readings and is able to stop insulin up to 30 min before reaching a preset low limit. It also automatically restarts insulin when patient's lev...

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Pervasive Healthcare System Based on Environmental Monitoring

Sangeetha

Amudha

2019

Intelligent Pervasive Computing Systems for Smarter Healthcare

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COMMODITY12: A smart e-health environment for diabetes management

Cited by 51 publications

References 54 publications

Agent‐oriented activity recognition in the event calculus: An application for diabetic patients

Agent‐oriented activity recognition in the event calculus: An application for diabetic patients

Towards an ICT-Based Platform for Type 1 Diabetes Mellitus Management

Pervasive Healthcare System Based on Environmental Monitoring

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