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Advancing Personalized Diabetes Management: Enabling Research-Driven Closed-Loop control with AndroidAPS 12

Advancing Personalized Diabetes Management: Enabling Researc...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Szasz, Laszlo Simon, Barbara Denes-Fazakas, Lehel Szilagyi, Laszlo Kovacs, Levente Eigner, Gyorgy Obuda Univ Univ Res & Innovat Ctr Physiol Controls Res Ctr Budapest Hungary Obuda Univ John Neumann Fac Informat Biomat & Appl Artificial Intelligence Inst Budapest Hungary Obuda Univ Appl Informat & Appl Math Doctoral Sch Budapest Hungary Sapientia Hungarian Univ Transylvania Fac Tech & Human Sci Targu Mures Romania

Type 1 Diabetes Mellitus (T1DM) presents a significant health challenge that requires innovative solutions for effective and personalized management. Automated insulin delivery systems based on the artificial pancreas (AP) concept are the most advanced treatment solutions today. However, these certified systems are closed commercial systems that do not allow the implementation of AP research results on them. The Android Artificial Pancreas System (AndroidAPS) is an open-source AP software that implements the concept of artificial pancreas. Using the AndroidAPS the newest T1DM research results can be implemented, however, closed-loop control is not available in this tool. In this study, we introduce a new feature connected to the AndroidAPS. This module facilitates continuous software manipulation of input data and enables the seamless feedback of output parameters, specifically the insulin rate realizing a research-oriented closed-loop control testing-ready open-source environment. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Tele-medicine T1DM Type 1 diabetes mellitus Cyber-Physical systems Hardware-in-the-loop simulation

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An information theory approach for recursive LPV-ARX model identification via LS-SVM 20

An information theory approach for recursive LPV-ARX model i...

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20th ifac symposium on System Identification (SYSID)

作者： Corrini, F. Mazzoleni, M. Ferracuti, F. Cavanini, L. Previdi, F. Univ Bergamo Dept Management Informat & Prod Engn Bergamo Bg Italy Univ Politecn Marche Dept Informat Engn I-60131 Ancona Italy Ind Syst & Control Ltd Culzean House36 Renfield St Glasgow G2 1LU Lanark Scotland

When modeling a dynamical system, linear models are always the first choice due to their simplicity. However, many times the system is too complex so that employing linear models results in poor performances. In this context, Linear Parameter Varying (LPV) models allow to represent non-linear input/output relationships while preserving the simple structure of linear models. The method of Least Squares Support Vector Machines (LS-SVM) is one of the most common approaches to identify a LPV model in an ARX formulation (LPV-ARX). However, due to its computational cost, it is difficult to identify a LPV-ARX model using LS-SVM in online applications, where the model must be updated every time new data are collected. An efficient update algorithm has been presented for online identification of such models, where the idea is to update the model only upon certain data that are considered informative. However, this approach requires the tuning of some hyperparameters and in certain conditions can stop updating the model even when data are informative. This paper proposes an information-based algorithm to overcome these drawbacks. Evaluation on simulated and experimental data show the effectiveness of the proposed approach on both identification and computational sides. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licneses/by-nc-nd/4.0/)

关键词： Online system identification LPV models LS-SVM methods

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Safe Multi-patient Ventilator use: Custom control with the Novel ACTIV System. 12

Safe Multi-patient Ventilator use: Custom Control with the N...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Holder-Pearson, Lui Chase, J. Geoffrey Shaw, Geoffrey Univ Canterbury Ctr Bioengn Canterbury New Zealand Christchurch Hosp Dept Intens Care Christchurch New Zealand Univ Otago Dept Med Otago New Zealand

The COVID-19 pandemic placed unprecedented demand on ventilated bedspaces, particularly in low-resource regions. Sharing a mechanical ventilator between multiple patients was strongly discouraged because of the inability to individually monitor and customise ventilation. Using an active switching valve, ventilation can be diverted between two respiratory circuits, and external sensors and pressure-reducing valves provide fully customisable, patient-specific ventilation. However, custom ventilation control across different, variable patients with such a system has not been demonstrated. Using a mechanical test lung setup, the balancing process for two lungs with compliance 0.10 L . cmH(2)O(-1) and 0.05 L . cmH(2)O(-1)to achieve equal tidal volumes is presented. The system demonstrates the ability to have differing driving and end-expiratory pressures of at least 20 cmH(2)O. The balancing process is done in a manner protecting the more compliant lung from potential hyperinflation injury. The system is designed to interface with a range of devices, and be readily manufactured in low-resource regions. Mitigation of safety concerns are discussed, such as cross-contamination, sterility, and specific ventilator settings are discussed. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Mechanical ventilation customisation personalised control Venturi parallel ventilation

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An adaptive control structure to mitigate lack of feasibility in systems with poor identifiability. Case Study on General Anesthesia. 12

An adaptive control structure to mitigate lack of feasibilit...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Ionescu, Clara M. De Keyser, Robin Yumuk, Erhan Copot, Dana Neckebroek, Martine Univ Ghent Dept Electromech Syst & Met Engn Technol Pk 125 B-9000 Ghent Belgium Ghent Univ Hosp Dept Anesthesia Corneel Heymanslaan 10 B-9000 Ghent Belgium

Poor identifiability in medical systems is due to ethical and regulatory limitations in free decisions regarding design of excitation signals and application of persistent excitation to human body areas of interest. Furthermore, drug-dose effect relationship is o particular challenge due to inter and intra patient variability. Complexity of interacting biological self- and extra- regulatory networks of vital processes in humans add more to the uncertainty limiting the feasibility of obtaining a digital twin of the patient for better clinical outcome. Epistemic and aleatory uncertainty is clustered around the problem of identifiability. This is furthermore strengthen by the lack of suitable instrumentation to measure the necessary information, rather making available inferences and surrogate infometrics. In this paper we propose a theoretical framework build on a commonly encountered problem of model mismatch in closed loop control of anesthesia. The solution uses ratio control of two drug co-administration in an online model estimation with adaptive setpoint strategy to achieve general anesthesia. Our simulations indicate that the proposed solution helps in calibrating the dose-effect response model to actual response of the patient in online setting and uses setpoint adaptation to guide the induction of anesthesia. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： ratio control human in the loop semantics decision making systems uncertainty identification nonlinear systems gain scheduling PID control closed loop control of anesthesia

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Online Lithium-ion Battery Modeling and State of Charge Estimation via Concurrent State and Parameter Estimation 20

Online Lithium-ion Battery Modeling and State of Charge Esti...

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20th ifac symposium on System Identification (SYSID)

作者： Li, Jimei Wang, Yang Ferrari, Riccardo M. G. Swevers, Jan Ding, Feng Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Katholieke Univ Leuven Dept Mech Engn MECO Res Team Flanders Make KU Leuven B-3001 Leuven Belgium Delft Univ Technol Delft Ctr Syst & Control Mekelweg 2 NL-2628 CD Delft Netherlands

This paper develops a novel approach for online Lithium-ion (Li-ion) battery model identification and state of charge (SOC) estimation. To account for the SOC-dependent battery dynamics and the static nonlinearity between the open-circuit voltage (OCV) and SOC, we formulate a grey box nonlinear state-space model, in which elements depend on SOC in a polynomial way. For model identification, we propose an online concurrent state and parameter estimation by alternating the recursive least squares algorithm and particle filter;the SOC is computed via Coulomb counting during the modeling. The identified grey box model is then applied for SOC estimation using the particle filter. Simulation with real-world battery measurements demonstrates the effectiveness of the model structure and the estimation approach, which is reflected in accurate terminal voltage estimation and nonlinear OCV-SOC relation, and superior performance regarding SOC estimation compared to state-of-the-art approaches. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licneses/by-nc-nd/4.0/)

关键词： Li-ion battery modeling equivalent circuit model nonlinear system identification SOC estimation least squares particle filter

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A Mechanomyography-Based System for Application and Tuning of Transcutaneous Spinal Cord Stimulation 12

A Mechanomyography-Based System for Application and Tuning o...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Spieker, E. L. Dvorani, A. Salchow-Hommen, C. Wiesener, C. Voigt, H. Wenger, N. Schauer, T. Charite Univ Med Berlin Dept Neurol Charitepl 1 D-10117 Berlin Germany Tech Univ Berlin Control Syst Grp Einsteinufer 17 D-10587 Berlin Germany TU Berlin SensorStim Neurotechnol GmbH Einsteinufer 17 D-10587 Berlin Germany

For patients with lesions in the upper motor neurons, transcutaneous spinal cord stimulation is a promising therapy technique to reduce spasticity and enhance voluntary movement. However, identifying an individual suitable therapy intensity and electrode position requires a time-consuming tuning procedure. This process typically entails the interpretation of electromyographic responses of leg muscles to double stimulation pulses to ensure the activation of targeted structures. To simplify and automate this process, we introduce a system consisting of a stimulator, a tablet, and four inertial measurement units used for mechanomyography (MMG). A pre-trained machine learning model is applied to the MMG signal and employs the response characteristics. The system derives a suitable therapy intensity for the selected electrode position from the model's results and assesses the suitability of this position with a score. Transferring classic EMG measurements to MMG-based systems and the automation of intensity identification might ease application and reduce time constraints in clinical settings. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Measurement and instrumentation Bio-signals analysis and interpretation Machine Learning Spinal Cord Stimulation Neurotechnology Rehabilitation Engineering

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A Simplified Hyper-Viscoelastic modelling Approach for Creep response of Urethra Based on Ex-vivo Tissue Data 12

A Simplified Hyper-Viscoelastic Modelling Approach for Creep...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Bhave, A. Joshi, J. Yache, V Rupitsch, S. Moeller, K. Furtwangen Univ Inst Tech Med Jakob Kienzle Str 17 D-78054 Villingen Schwenningen Germany Furtwangen Univ Jakob Kienzle Str 17 D-78054 Villingen Schwenningen Germany Dept Microsyst Engn Georges Kohler Allee 106 D-79110 Freiburg Germany

Hyper-viscoelastic models are shown to better replicate the tissue biomechanical behaviour. In this study we aimed to model the urethral behaviour through a hyper-visco elastic implementation. First we used a 2-parameter Demiray model for identification of urethra l in -situ for several specimens for the static inflation tests. Attempting the approach by Holzapfel et al., the average ratio of the elastic and dynamic modulus was computed to determine the stress contribution of the viscoelastic branch for the standard linear solid (maxwell). For the first step regarding the identified Demiray parameters, a goodness off it (Adjusted R-2) output above 0.9 was obtained for all samples while Adjusted R-2 value of above 0.99 for more than half of the samples were obtained. This indicated good fitting metric for static inflation tests. For the hyper-viscoela stic implementation, the retardation constants of 10s and 100s performed well (Adjusted R-2) for most sample s with time constant of 10s performing much better. The above partial identification technique and the hyper-viscoela stic in-silico approaches show that simplification done in our approach fares suitably for the creep response characterization of the urethra. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Biomechanics Viscoelasticity Urethra Creep Hyperelasticity Hyper-viscoelasticity Maxwell Standard linear solid

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Safe and Effective Glycaemic control for Minimal Workload in Critically Ill Patients: Virtual trials analysis on performance and safety 12

Safe and Effective Glycaemic Control for Minimal Workload in...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Seret, Marie Uyttendaele, Vincent Desaive, Thomas Chase, J. Geoffrey Univ Liege GIGA In Silico Med Liege Belgium Univ Canterbury Dept Mech Engn Christchurch New Zealand

STAR is a glycaemic control (GC) framework using a unique model-based and risk-based dosing approach. STAR modulates both insulin and nutrition inputs to mitigate the risk of hypo- and hyperglycaemia. This protocol, accounting for both inter- and intra-patient variability successfully manages to provide safe, effective control to all patients, regardless of their condition. In a recent clinical trial in Belgium, workload was pointed as a potential barrier for clinical adoption despite clear benefit for patients. Clinical burden is a key factor in compliance and uptake. This study assesses the impact on GC outcomes when increasing measurement intervals from 1-3 hourly to 1-6 hourly in the STAR GC framework. Retrospective data from 606 critically ill patients totalling over 59,000 hours of control are used to create virtual patients. Insulin sensitivity is identified for each patient using a validated physiological model, and new stochastic predictive models are built to forecast variability up to 6-hourly. Five-fold cross validation is used to build the models on 80% of data and simulate virtual trials on the remaining 20%. Safety, performance, nutrition intake and workload are compared and analysed. Results showed similar, very high safety and performance regardless of the measurement intervals, showing STAR GC framework robustness in controlling patients. However, there was a clear risk and reward tradeoff between the increased risk of hypoglycaemic event (from 12 to 23 patients between 1-3 hourly and 1-6 hourly protocols) and reduced nutrition intake (from 100 [85- 100] to 85 [70- 95] % GF) for the benefit of significant lower workload (from 12.1 to 8.3 measurement per day), closer to clinical practice. These promising results should be confirmed in clinical trials but offer possibilities to adapt measurement strategy based on local ICU practice and clinical burden. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https

关键词： Glycaemic control Insulin Therapy Intensive Care Stochastic modelling Hyperglycaemia

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A performant PID autotuner based on a short relay test 4

A performant PID autotuner based on a short relay test

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4th ifac Conference on Advances in Proportional-Integral-Derivate control (PID)

作者： De Keyser, Robin Birs, Isabela Ionescu, Clara Muresan, Cristina Univ Ghent Dept Electromech Syst & Met Engn Tech Lane Sci Pk 125 B-9052 Ghent Belgium Tech Univ Cluj Napoca Dept Automat Memorandumului 28 Cluj Napoca 400114 Romania

Industrial applications encompass a wide range of fields, including chemical plants, biomedical engineering, vibration control, and mechanical systems, among others. Frequently, controller tuning is performed using a process model as a basis. Determining such a model can be a time-consuming and challenging task, requiring proper identification. Nonlinear processes often have a limited number of relevant operating points. Autotuning methods that collect pertinent process information at specific operating points and determine controller parameters automatically streamline the controller design process by eliminating the need for process modelling. This paper presents a novel relay based autotuning methodology for Proportional Integral Derivative (PID) controllers highly suitable for Industry 5.0 applications that is easily implemented. The methodology is successfully validated on two numerical examples consisting of a highly oscillatory process and a process with large time delay. Additionally, two experimental validations are presented for a mass-spring-damper system and a 2-tank process.

关键词： PID control industrial control relay autotuner

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Causal-Transformer: Spatial-temporal causal attention-based transformer for time series prediction 12

Causal-Transformer: Spatial-temporal causal attention-based ...

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12th ifac symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Zhu, Yaqi Yang, Fan Torgashov, Andrei Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China Russian Acad Sci Far Eastern Branch Inst Automat & Control Proc Proc Control Lab Vladivostok 690041 Russia

Real-time monitoring and accurate prediction of key variables are indispensable to ensure industrial production activities proceed as expected. With the increase in measurement data volume and the improvement of hardware computing power, the Transformer and its variants, due to their excellent capability in extracting global dependencies, are playing an increasingly important role among deep learning-based multidimensional time series prediction models. In addition, from the perspective of causality, cause variables contain parts of information in effect variables and can reduce the uncertainty of effect variables, which is beneficial for prediction. However, there has been relatively limited research on combining the Transformer and causal feature analysis. To fully use both advantages, this paper introduces the Causal-Transformer (CT) model, which utilizes semi-orthogonal projection to extract causal features from multiple input variables. A multi-head spatial-temporal causal attention mechanism is designed in the encoder block based on the classical Transformer model to simultaneously reduce feature dimensions and extract implicit causal features in both the temporal and spatial dimensions. The CT also utilizes the Granger causality analysis to select the causal teaching indicators of target variables to provide stable assistance by injecting explicit causality into the inputs of the decoder block. By leveraging more condensed and independent causal features, the CT possesses inherent advantages in predicting time series variables. Case study results show that the CT model outperforms the other models on the diesel refinery dataset, especially with a reduction of 46.0% and 30.4% in MSE towards the classic Transformer and informer in five-step prediction. Copyright (C)2024 The Authors. This is an open access article under the CC BY-NC-ND license (htips://***/licenses/by-nc-nd/4.0/)

关键词： Machine learning Time series modelling Transformer Causal analysis Attention machenism

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