ABSTRACT
Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.
Subject(s)
Algorithms , Blood Glucose , Exercise , Heart Rate , Neural Networks, Computer , Humans , Exercise/physiology , Heart Rate/physiology , Blood Glucose/analysis , Blood Glucose Self-Monitoring/methods , Male , Diabetes Mellitus/diagnosis , Female , Adult , ROC Curve , Diabetes Mellitus, Type 2/diagnosis , Artificial Intelligence , Diabetes Mellitus, Type 1/physiopathology , Middle AgedABSTRACT
Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate-the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity.
Subject(s)
Blood Glucose Self-Monitoring , Blood Glucose , Humans , Blood Glucose Self-Monitoring/methods , Heart Rate , Artificial Intelligence , Machine Learning , ExerciseABSTRACT
The discovery of human mobility patterns of cities provides invaluable information for decision-makers who are responsible for redesign of community spaces, traffic, and public transportation systems and building more sustainable cities. The present article proposes a possibilistic fuzzy c-medoid clustering algorithm to study human mobility. The proposed medoid-based clustering approach groups the typical mobility patterns within walking distance to the stations of the public transportation system. The departure times of the clustered trips are also taken into account to obtain recommendations for the scheduling of the designed public transportation lines. The effectiveness of the proposed methodology is revealed in an illustrative case study based on the analysis of the GPS data of Taxicabs recorded during nights over a one-year-long period in Budapest.
Subject(s)
Goals , Mobile Applications , Cities , Cluster Analysis , Humans , Transportation/methodsABSTRACT
Given the rising popularity of robotics, student-driven robot development projects are playing a key role in attracting more people towards engineering and science studies. This article presents the early development process of an open-source mobile robot platform-named PlatypOUs-which can be remotely controlled via an electromyography (EMG) appliance using the MindRove brain-computer interface (BCI) headset as a sensor for the purpose of signal acquisition. The gathered bio-signals are classified by a Support Vector Machine (SVM) whose results are translated into motion commands for the mobile platform. Along with the physical mobile robot platform, a virtual environment was implemented using Gazebo (an open-source 3D robotic simulator) inside the Robot Operating System (ROS) framework, which has the same capabilities as the real-world device. This can be used for development and test purposes. The main goal of the PlatypOUs project is to create a tool for STEM education and extracurricular activities, particularly laboratory practices and demonstrations. With the physical robot, the aim is to improve awareness of STEM outside and beyond the scope of regular education programmes. It implies several disciplines, including system design, control engineering, mobile robotics and machine learning with several application aspects in each. Using the PlatypOUs platform and the simulator provides students and self-learners with a firsthand exercise, and teaches them to deal with complex engineering problems in a professional, yet intriguing way.
Subject(s)
Brain-Computer Interfaces , Robotics , Electromyography , Humans , Robotics/methods , Software , Support Vector MachineABSTRACT
Összefoglaló. Bevezetés: A HbA1c integrált retrospektív mutatója az elmúlt idoszak vércukrának, rendszeres vizsgálata a cukorbetegek anyagcserekontrolljának megítélésében elengedhetetlen. Helyes értékelése azonban nem egyszeru, mert a HbA1c és a vércukor közötti összefüggés nem lineáris. A mérést közvetlenül megelozo hyperglykaemiás epizódok hatása a HbA1c szintjére nagyobb, mint azoké, amelyek régebben történtek. A jelenségre a glikáció biokinetikus modellje ad magyarázatot. Célkituzés: A mért és a biokinetikus modell alapján számított HbA1c közötti egyezés, illetve diszkordancia vizsgálata. Módszer: A vizsgálatokat 157, 1-es és 2-es típusú cukorbeteg 1793, laboratóriumban mért éhomi vércukor- és 511 HbA1c-adatából végeztük. A különbséget a glikációs index segítségével számítottuk, amely a mért és a számított HbA1c-érték aránya. Eredmények: Egyezést mindössze a vizsgált betegek kevesebb mint egyötödödében találtunk, 60%-ban az index értéke alacsony (<0,95) és 21%-ban magas (>1,05) volt. Az adatok részletes analízise szerint jó anyagcserekontroll esetében gyakoribb a vártnál magasabb, mért HbA1c-érték, mint a biokinetikus egyenlet által számítotté, és rosszabb kontroll (magasabb átlagos vércukor) esetében ez fordítva van. Egyezés esetén a regressziós egyenlet együtthatói gyakorlatilag azonosak a modell alapján számított értékekkel. Következtetés: Vizsgálataink felvetik azt a lehetoséget, hogy a biokinetikus modell magyarázatot adhat a vércukor és a HbA1c közötti diszkordanciára. Orv Hetil. 2021; 162(41): 1652-1657. INTRODUCTION: HbA1c is an integrated retrospective marker of previous blood glucose concentrations and its regular measurement is indispensable in the assessment of glycaemic compensation of diabetic patients. However, its proper interpretation is not simple becasuse the relationship between HbA1c and average glycemia is not a linear one. Hyperglycemic episodes occuring immediately before the measurement have greater impact on the HbA1c level as compared with those taking place earlier. OBJECTIVE: Assessment of concordance and discordance between measured and according to the biokinetic model calculated values of HbA1c. METHOD: The calculations were made from averages of 1793 fasting blood glucose and 511 HbA1c of 157, type 1 and 2 diabetic patients. The glycation index is the quotient between measured and calculated HbA1c. RESULTS: Agreement was found in less than one fifth of the 157 patients; in 60% the value of glycation was low (<0.95) and in 21% high (>1.05). Analysis of the glycation index according to the level of glycemic compensation revealed that in patients with good compensation, the measured HbA1c value was more often higher than the expected and in patients with unsatisfactory compensation the opposite was true. CONCLUSION: These results raise the possibility that the discordance between average glycemia and measured HbA1c can be explained by the biokinetic model. Orv Hetil. 2021; 162(41): 1652-1657.
