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Iterative Feedback Tuning with automated reference model selection 20

Iterative Feedback Tuning with automated reference model sel...

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20th ifac symposium on System Identification (SYSID)

作者： Ickenroth, Tjeerd Breschi, Valentina Oomen, Tom Formentin, Simone Eindhoven Univ Technol Dept Mech Engn NL-5600 MB Eindhoven Netherlands Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Politecn Milan Dip Elettron Informaz & Bioingn Via G Ponzio 34-5 I-20133 Milan Italy

Iterative Feedback Tuning (IFT) is a direct, data-driven control technique, that relies on a reference model to capture the desired behavior of the unknown system. The choice of this hyper-parameter is particularly critical, as it potentially jeopardizes performance and even closed-loop stability. This paper aims to explore the suitability of three search methods (grid search, random search, and successive halving) to automatically tune the reference model from data based on a set of user-defined, soft specifications on the desired closed-loop behavior. To compare the three methods and demonstrate their effectiveness, we consider a benchmark simulation case study on the control of a mass-spring-damper system. From our results, successive halving turns out to be the most efficient way to run IFT with automatic reference model selection on a finite budget of time for data collection. 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： Iterative Feedback Tuning data-driven control model-reference control

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State Derivative Normalization for Continuous-Time Deep Neural Networks 20

State Derivative Normalization for Continuous-Time Deep Neur...

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20th ifac symposium on System Identification (SYSID)

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Goerges, Daniel Toth, Roland Schoukens, Maarten Ruskowski, Martin RPTU Kaiserslautern Chair Machine Tools & Control Syst Kaiserslautern Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany Eindhoven Univ Technol Control Syst CS Grp Dept Elect Engn Eindhoven Netherlands Inst Comp Sci & Control Syst & Control Lab Budapest Hungary RPTU Kaiserslautern Inst Electromobil Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. 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： System Identification Continuous-Time Neural Ordinary Differential Equations Nonlinear State-Space

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Integrating Handwritten Data Entry and Real-Time Data Streaming for Enhanced Medical Device Closed Loop control and Signal Visualization in Digitalized ICU Charts 12

Integrating Handwritten Data Entry and Real-Time Data Stream...

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

作者： Su, Yunpeng Hill, Jordan Guy, Ella F. S. Clifton, Jaimey A. Flett, Isaac L. Summerhays-Sunnex, Dad S. Zhou, Cong Pearson, Lui Holder Chase, J. Geoffrey Univ Canterbury Dept Mech Engn Christchurch New Zealand

This paper presents an innovative method for implementing the intuitive closed-loop control of medical device and signal monitoring and in digital intensive care unit (ICU) environments. It achieves this aim by integrating handwritten data entry, touchscreen intuitive control and real-time data streaming into digitalized ICU charts. By leveraging Microsoft Excel for data entry, device control and monitoring, this approach offers familiarity, customization, and compliance with regulatory standards, thereby facilitating efficient data management. The intuitive user interface of the device, built upon Excel data streamer, enables seamless medical device control, data transfer and visualization, providing options for both wired and wireless connections. Additionally, the integration of novel features enhances the real-time control and monitoring capabilities. Validation tests demonstrate the effectiveness of this integrated approach, indicating potential enhancements to patient care and clinical decision-making in digital ICU settings. This research represents a significant technological advancement in healthcare with promising implications for patient outcomes and healthcare delivery. 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： ICU chart real-time data streaming handwritten data entry medical device signal monitoring

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Minimizing Epistemic Uncertainty for Predictive control of General Anesthesia 12

Minimizing Epistemic Uncertainty for Predictive Control of G...

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

作者： Ionescu, Clara M. De Keyser, Robin Copot, Dana Yumuk, Erhan Birs, Isabela R. Hegedus, Erwin Muresan, Cristina, I Neckebroek, Martine Univ Ghent Dept Electromech Syst & Met Engn Ghent Belgium Tech Univ Cluj Napoca Dept Automat Memorandumului St 20 Cluj Napoca Romania Istanbul Tech Univ Dept Control & Automat Engn TR-34469 Istanbul Turkiye Ghent Univ Hosp Dept Anesthesia Corneel Heymanslaan 10 B-9000 Ghent Belgium

Epistemic uncertainty refers to the lack of information from knowledge and in medical field it is often paired with decision making situations and systems largely dependent on context and expertise. Our approach is uncertainty-centric and digitalizes knowledge with high certainty level, in order to improve the efficacy of knowledge-driven decision support system for anesthesia regulation. We propose to digitize some of surgical actions as part of minimizing uncertainty in closed loop control. Notable disturbances are those induced by the surgery actions, but they abide clinical protocols and semantic transformation enables their use in computers. We introduce a predictive control methodology to accommodate the augmented information of the system and evaluate its impact on the closed loop performance. Simulations of closed loop control of anesthesia for various surgery protocols indicate a high relevance and applicability of proposed approach. The proposed control structure allows the use of medical semantics in decision making process of anesthesia regulation. 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： closed loop control of anesthesia digitalized disturbance predictive control human in the loop medical semantics decision making systems epistemic uncertainty

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System Identification of User Engagement in mHealth Behavioral Interventions 20

System Identification of User Engagement in mHealth Behavior...

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20th ifac symposium on System Identification (SYSID)

作者： El Mistiri, Mohamed De La Torre, Steven Tung, Karine Marlin, Benjamin M. Pavel, Misha Klasnja, Predrag Spruijt-Metz, Donna Rivera, Daniel E. Arizona State Univ Control Syst Engn Lab Tempe AZ 85281 USA Univ Calif San Diego Herbert Wertheim Sch Publ Hlth & Human Longev Sci La Jolla CA 92093 USA Univ Massachusetts Amherst Manning Coll Informat & Comp Sci Amherst MA 01003 USA Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA Northeastern Univ Bouve Coll Hlth Sci Boston MA 02115 USA Univ Michigan Sch Informat Div Biomed & Hlth Informat Ann Arbor MI 48109 USA Univ Southern Calif Dept Psychol & Populat & Publ Hlth Sci USC Dornsife Ctr Econ & Social Res Los Angeles CA 90089 USA

Digital behavior change interventions (DBCIs) such as "just-in-time" adaptive interventions (JITAIs) have demonstrated efficacy for increasing physical activity behavior. However, the effectiveness of these interventions is heavily dependent upon user engagement. Despite the inherent dynamic nature of engagement, as it varies over time based on an individual's changing environment, context, and psychological state, the current understanding of engagement primarily comes from static snapshots of the behavior. The availability of intensive longitudinal data from JITAIs provides a unique opportunity to build and test dynamic models of behavior change from a process systems lens, relying on prediction-error methods from system identification. However, data missingness is a significant practical consideration in this process. Therefore, in this work we address missingness using a Bayesian imputation approach, which we evaluate using data from the HeartSteps II JITAI. Ultimately, the methods presented support the discovery of key factors that impact engagement behavior over time and can play an important role in the development of large-scale personalized interventions. 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： eHealth Bayesian methods Computational Social Sciences Time series modelling

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A latent representation of brain networks based on EEG 20

A latent representation of brain networks based on EEG

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20th ifac symposium on System Identification (SYSID)

作者： Falconi, Lucia Cisotto, Giulia Zorzi, Mattia Swiss Fed Inst Technol Automat Control Lab Phys Str 3 CH-8092 Zurich Switzerland Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Univ Padua Dept Informat Engn Padua Italy

Electroencephalography (EEG) is one of the most popular techniques to investigate normal as well as pathological cerebral mechanisms, as it allows to measure, non-invasively and in real-time, the brain activity. However, modeling EEG is still extremely challenging, because of its high-dimensionality, low signal-to-noise ratio, and high individual variability. This paper proposes a novel latent representation to study brain networks using EEG by means of a robust dynamic factor analysis (RDFA) approach. We investigate the ability of this latent representation to discriminate between two groups of subjects, i.e. alcoholic and healthy. By RDFA, we can extract a limited number of highly explanatory factors, as low as 8, significantly discriminating between the two groups. Also, we show that different brain patterns can be identified across different stimulation scenarios and EEG locations. Although preliminary, this work could give support to domain experts while providing some clinically-meaningful insights to identify common patterns as well as individual characteristics in different groups of healthy and pathological subjects. 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： EEG system identification dynamic factor analysis

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Closed-Loop control of Arterial CO2 for Neonatal Mechanical Ventilation: In-Vivo Interaction with Spontaneous Breathing 12

Closed-Loop Control of Arterial CO2 for Neonatal Mechanical ...

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

作者： Pfannschmidt, Valerie Buglowski, Mateusz Huetten, Matthias Kowalewski, Stefan Schoberer, Mark Stollenwerk, Andre Rhein Westfal TH Aachen Informat Embedded Software 11 Aachen Germany RWTH Aachen Univ Hosp Dept Paediat & Adolescent Med Neonatol Sect Aachen Germany Maastricht Univ Dept Pediat Sch Oncol & Dev Biol GROW NL-6202 AZ Maastricht Netherlands

Mechanical ventilation of preterm neonates requires tight regulation of the arterial partial pressure of carbon dioxide (PaCO2), which can be achieved by a controller. A core challenge of such a controller is the interaction between automatically controlled mechanical ventilation and physiologically regulated spontaneous breathing. To include spontaneous breathing in the control loop, we extend our previously developed controller by the principle of Mandatory Minute Ventilation. In preliminary experiments with preterm lambs the controller behavior was adequate. Further evaluation and a more accurate estimation of PaCO2 are needed. 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： Physiological closed-loop control critical care experimental validation preterm neonates mechanical ventilation end-tidal carbon dioxide

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Identification of Deformable Linear Object Dynamics from Input-output Measurements in 3D Space 20

Identification of Deformable Linear Object Dynamics from Inp...

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20th ifac symposium on System Identification (SYSID)

作者： Floren, Merijn Mamedov, Shamil Noel, Jean-Philippe Swevers, Jan Katholieke Univ Leuven MECO Res Team Leuven Belgium Flanders Make KU Leuven Leuven Belgium

controlling deformable linear objects requires reliable models that capture their complex and high-dimensional dynamics. This paper aims to obtain accurate state-space models of such objects from input-output measurements only, tailored for the use in a model predictive control setting. The proposed identification approach is initialized by a physics-inspired baseline model which approximates a deformable linear object by a serial chain of rigid bodies connected via elastic joints. The nonlinear residual dynamics of the baseline model are then modeled by an artificial neural network, where a key element is the so-called guided residual search that first infers the latent residual and state trajectories in the time domain, in this way facilitating supervised learning of the neural network. The efficacy of the modeling approach is demonstrated on experimental data of the 3D end-point motion of a flexible aluminum rod, excited by a 7-axis Franka Panda robot arm. Compared to a physics-inspired baseline model, the proposed method achieves a 14% prediction performance increase, and compared to a fully neural network-based approach with a similar number of parameters, it achieves a 58% improvement, while also reporting a training time that is more than 5 times faster. 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： Deformable linear objects Nonlinear system identification State-space models Neural networks Optimization Optimal control theory

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Enhancing Prosthetic control with Ultrasound Images: A Convolutional Neural Network Approach for Hand Gesture Recognition 4

Enhancing Prosthetic Control with Ultrasound Images: A Convo...

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4th Modeling, Estimation, and control Conference (MECC)

作者： Chen, Yun Bao, Xuefeng He, Hongsheng Zhang, Qiang Univ Alabama Dept Mech Engn Tuscaloosa AL 35487 USA Univ Wisconsin Dept Biomed Engn Milwaukee WI 53045 USA Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA

Human hand gesture recognition using biological signals from the forearm is an increasingly significant area of research, with implications across various fields such as prosthetic development, rehabilitation, and human-machine interaction. However, traditional hand gesture recognition with surface electromyography (sEMG) technique has some challenges, including cross-talk from neighboring muscles, low signal-to-noise ratio, and inability to measure deep muscles. In the current study, we proposed to use brightness mode (B-mode) ultrasound images from the muscles of the forearm anterior side as an alternative neuromuscular interface to recognize hand gestures. We designed a convolutional neural network (CNN) classifier to build the personalized mapping from static ultrasound images to eight different hand gestures. To evaluate the performance of the proposed CNN classifier, an ultrasound images dataset and labeled gestures from four young healthy participants were collected and analyzed. Results from offline intra-subject (personalization) validation, quasi-real-time validation, and real-time validation showed high classification accuracy of 99.65%, 97.47%, and 90.83%, respectively. In addition, real-time hand gesture recognition could be executed within 50 ms per image frame by using the proposed CNN classifier. Our findings demonstrated promising real-time hand gesture recognition with high accuracy by using B-mode ultrasound images and the proposed CNN classifier for prosthetic hand control. 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： biomedical and medical image processing and systems Rehabilitation engineering and healthcare delivery Bio-signals analysis and interpretation Man-machine interfaces Bioinformatics Estimation and control in biological systems

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Multi-dimensional Quantile Regression Using Polynomial Function Fitting for Insulin Sensitivity Forecasting 12

Multi-dimensional Quantile Regression Using Polynomial Funct...

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

作者： Szabo, Balint Pinter, Petra Antal, Akos Szlavecz, Akos Chase, J. Geoffrey Benyo, Balazs Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Control Engn & Informat Technol Budapest Hungary Semmelweis Univ Fac Dent Dept Oral Diagnost Budapest Hungary Univ Canterbury Christchurch New Zealand

A significant portion of patients in the intensive care unit experience hyperglycemia, meaning abnormally high blood sugar levels. Prolonged hyperglycemia poses numerous risks, leading to organ damage in the medium-term and potentially life-threatening conditions. Therefore, insulin dosing is essential to regulate the blood sugar levels of hyperglycemic patients. The STAR protocol is an insulin dosing support, a so-called tight glycemic control protocol that uses insulin sensitivity among the patient's physiological parameters to characterize their current state. Estimating the patient's future SI is crucial for determining optimal treatment. Various methods exist for this estimation, and different metrics are available for evaluating these methods. The challenge in the estimation task is to determine not the expected value of future SI but its 90% confidence interval, which is necessary for implementing clinical protocols. The research presented in the article developed SI estimation methods using quantile regression, polynomial fitting, and neural network-based approaches. Multiple previous insulin sensitivity values were used as input parameters to improve prediction accuracy. 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： Five to ten keywords preferably chosen from the ifac keyword list

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