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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Hafsa, Mariem Bchir, Ons Bader, Oumaima Ben Amara, Najoua Essoukri Kanoun, Olfa Université de Sousse Ecole Nationale d' Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany University of Sousse National EngineeringSchool of Sousse Computer Engineering Department Sousse Tunisia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical Impedance Tomography (EIT) is a non-invasive, non-ionizing medical imaging method that has been on the rise in biomedical field. For lung monitoring, electrodes are attached around patient's thorax. Several boundary voltages are measured on the surface of these electrodes, which result from the injection of a small alternating current. Based on these measurements, the inner conductivity distribution of the patient's lung can be determined and used for monitoring and diagnosis. EIT can be used for extended periods of time, which makes it ideal for continuous monitoring, especially for intensive care patients. Several stationary EIT-based lung monitoring platforms have been developed for this reason. In this work, however, we propose a web and cloud-based monitoring solution that allows remote access for the doctors and medical staff. The implemented solution provides real-time, continuous monitoring as well as a visualization of extracted lung features that aid the diagnosis, © 2022 IEEE.

关键词： Electrodes

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Post-processing for Thorax Imaging based on Electrical Impedance Tomography using Deep Learning 19

Post-processing for Thorax Imaging based on Electrical Imped...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Haddad, Hamdi Hafsa, Mariem Bader, Oumaima Ben Amara, Najoua Essoukri Kanoun, Olfa University of Sousse National Engineering School of Sousse Computer Engineering Department Sousse Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical Impedance Tomography (EIT) for lung monitoring is a non-ionizing medical imaging technique that is on the rise in biomedical engineering research in the recent decades. It reflects the inner conductivity distribution of biological tissue based on boundary voltages that appear on the electrodes, after injecting an alternating current of a certain amplitude into the human thorax. EIT, however, has a low spatial resolution and is susceptible to errors caused by electrode movement and poor contact quality, which can lead to the generation of blurry images. Post-processing imaging techniques can, in fact, be a beneficial tool for fine-tuning this issue. The focus of this work is to implement a U-NET multi-class semantic segmentation deep learning model to post-process reconstructed EIT thorax images to enhance its characteristics such as contrast, resolution... The model has achieved an overall accuracy of 72.43%. The resulting reconstructed images have reflected the physical changes during the breathing cycle, have deleted artifacts, and have made the shapes of the lungs and heart clearer. © 2022 IEEE.

关键词： Electrodes

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Exploring the Real-Time Capability of Electrical Impedance Tomography for Hand Sign Recognition in Robotic Hand Control

Exploring the Real-Time Capability of Electrical Impedance T...

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International Workshop on Robot Sensing (ROSE)

作者： Bilel Ghoul Bilel Ben Atitallah Rim Barioul Ahmed Fakhfakh Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Technopole of Sfax Ons City Tunisia

ISBN: (数字)9798350362367

ISBN: (纸本)9798350362374

Electrical impedance tomography (EIT) can monitor the influence of the muscle activity on the conductivity distribution across the forearm and is therefore suitable for Hand Sign Recognition (HSR). However, the method is relatively complex, so realizing real-time classification based on EIT measurements is still considered a major challenge. In addition, the accuracy of EIT for HSR is highly dependent on the injection configuration parameters applied. In this study, we investigate the influence of the injection configuration on the achieved accuracy of the hand robot control and the feasibility of a real-time classification based on EIT measurements. To assess real-time feasibility, experimental data have been collected for a sign set of 36 American Sign Language (ASL) performed by one healthy subject. Moreover, a comparative study of the adjacent, opposite, and cross injection configurations has been conducted on 10 subjects performing the ASL set. An ambiguity study based on the measured signals was performed to assess the discrimination potential between the hand signs. The adjacent EIT configuration exhibited higher sensitivity in discriminating hand signs. For a real-time classification, we propose the use of a Support Vector Machine (SVM) model realizing an overall accuracy of 71.38%. To validate the system's real-time behavior, the classification result is visualized by a robotic hand reproducing 6 different hand signs from the 36 ASL set reaching an accuracy of 80%. The results demonstrate the potential of EIT to facilitate real-time Hand Sign Recognition for robot control applications.

关键词： Support vector machines Electrical impedance tomography Surface impedance Accuracy Robot control Muscles Robot sensing systems

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Comparative Study of Data Reduction Methods in Electrical Impedance Tomography For Hand Sign Recognition

Comparative Study of Data Reduction Methods in Electrical Im...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Bilel Ghoul Bilel Ben Atitallah Salwa Sahnoun Ahmed Fakhfakh Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Technopole of Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Ons City Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Several studies have shown that electrical impedance tomography (EIT) on the human forearm can be used to classify hand signs without the need for cameras or gloves. However, the amount of measured data is in relatively high, leading to long execution times and high processing complexity. In this study, we investigate the influence of reducing the number of EIT measurements on the classification accuracy. Four different algorithms were used to halve the number of measurements, namely Principal Component Analysis (PCA), Chi-square tests, Minimum Redundancy Maximum Relevance (MRMR) and Laplacian scores. These algorithms were applied to a dataset of forearm EIT measurements corresponding to the practice of American Sign Language (ASL) in a healthy subject. The results show a slight drop in accuracy when using the quadratic support vector machine (SVM) classifier, with accuracy dropping from 95.3% to 93.4% in the best cases. A method based on a genetic algorithm (GA) was also used. This approach exploits the strengths of genetic algorithms in exploring a large solution space and converging on the near-optimal combination. This approach resulted in an increase in accuracy to 96.7%. The study demonstrated that the number of EIT measurements can be reduced without compromising overall accuracy.

关键词： Support vector machines Electrical impedance tomography Sign language Accuracy Image recognition Laplace equations Assistive technologies

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Parameters Estimation of Cole-Cole Bio-Impedance Model with a Minimum Number of Frequencies

Parameters Estimation of Cole-Cole Bio-Impedance Model with ...

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IEEE Instrumentation and measurement technology Conference

作者： Nour Ammar Cherif Ouni Ahmed Yahia Kallel Ahmed Fakhfakh Nabil Derbel Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Control and Energy Management Laboratory National Engineering School of Sfax University of Sfax Sfax Tunisia Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Sfax Tunisia

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Wearable bioimpedance devices are becoming increasingly popular due to their portability and non-invasiveness. However, these devices consume a lot of energy during measurement time, limiting the operating time. The number of frequencies must be reduced to reduce the measurement time and energy. In this paper, we propose using three frequencies and investigating their optimal values for accurate measurement. The remaining two frequencies at high frequencies are chosen by eliminating the high frequencies that have a high deviation. Lastly, a third frequency is optimally chosen based on a combination of the frequencies whose non-linear least-squares fitting parameters results lead to the smallest deviations. First, a simulation is used to validate the results, where a Cole-Cole model is used with different noise levels. Two high frequencies in combination with a frequency close to the characteristic frequency yielded the best results, regardless of the presence of noise. Second, the measurement was experimentally validated in STM32 with a physical Cole-Cole model, which confirmed the results, where 7.94 kHz, close to the characteristic frequency, had mean deviations of 2.43% in $R_{l}$ , 3.7% in $R_{E}$ , and 2.55% in C. Using three points reduces the measurement time from 9.7 ms to 0.41 ms.

关键词： Time-frequency analysis Accuracy Biological system modeling Fitting Energy measurement Time measurement Frequency estimation

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Research on Sensing Characteristics of High Q-Factor All-Dielectric Metasurfaces Based on Geometric Breaking

SSRN

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SSRN 2024年

作者： Chen, Ying Zhao, Guoting Wang, Zhongyao Shi, Moqing Zhu, Qiguang Hebei Province Key Laboratory of Test/Measurement Technology and Instrument School of Electrical Engineering YanShan University Hebei Qinhuangdao066004 China Hebei Province Key Laboratory of Special Fiber and Fiber Sensor School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China

The bound states in the continuum (BIC) are widely used in designing metamaterials with high quality factor (Q-factor) resonances due to their perfect localization in the continuous spectral range coexisting with expanding waves. In this study, a T-shaped nanohole all-dielectric metasurface structure is proposed based on the unique electromagnetic properties of all-dielectric metasurfaces, and the fundamental reason of its coupled state and the theoretical basis of BIC excitation are deeply investigated based on the theory of group theory, and the geometric symmetry is broken from multiple angles, and the coupling relationship between discrete eigenmodes and the external continuum is specifically analyzed during the change of symmetry, its spectral response and near-field distribution are numerically calculated. In addition, based on the modulation mechanism of the incident light polarization angle, the three-channel resonance with simultaneous tuning of the two resonances and independent tuning of the single resonance is realized under the condition that the resonance position and the Q-factor are not affected. This study not only provides a theoretical basis for the excitation conditions and evolution regularity of symmetry-protected BIC, but also solves the problem in the traditional analysis of resonance phenomena without taking the high Q-factor and stability into account, which can provide new designing methods for optical metasurface devices with multi-channel resonance. © 2024, The Authors. All rights reserved.

关键词： Tuning

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A Low-Power Voltage-Controlled Current Source for Wearable Bioimpedance Applications

A Low-Power Voltage-Controlled Current Source for Wearable B...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Cherif Ouni Bilel Ghoul Hamdi Al-Shaikhli Jumana Ma'touq Ahmed Fakhfakh Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Sfax Tunisia Department of Biomedical Engineering School of Applied Medical Sciences (SAMS) German Jordanian University Amman Jordan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Bioimpedance spectroscopy (BIS) has gained prominence as a non-invasive diagnostic tool for characterizing biological tissues, offering critical insights into physiological and pathological conditions. At the core of BIS systems lies the Voltage-Controlled Current Source (VCCS), which is essential for ensuring precise and stable current delivery across varying load conditions. This paper investigates a modified dual Howland configuration with load-in-loop compensation designed for low-power applications. By operating at reduced supply voltages (5V) instead of the conventional ±12V, the proposed design addresses the limitations of existing VCCS circuits in portable and wearable medical devices. The study evaluates the performance of the proposed VCCS circuit in a wide range of frequencies (50 kHz to 1 MHz), load conditions (1000 to 18kO), and input signal types (sine, multi-sine, and sine-sweep). Key findings demonstrate stable current output, efficient power consumption, and robust performance at reduced power levels, making it suitable for bioimpedance applications requiring portable operation.

关键词： Performance evaluation Spectroscopy Pathology Power demand Medical devices Circuits Bioimpedance Physiology Wearable devices Voltage control

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A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction

A Robust Convolutional Neural Network for Electrical Impedan...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Mariem Hafsa Kailun Lin Najoua Essoukri Ben Amara Olfa Kanoun Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Department of Mechanical Engineering Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging.

关键词： Training Image quality Electrical impedance tomography Voltage measurement Impedance measurement Imaging Robustness Convolutional neural networks Spatial resolution Image reconstruction

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Uncertainty Budget in the Ldv-Based Radius measurement

SSRN

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SSRN 2024年

作者： Shi, Shengyu Wu, Yongwei Li, Xiang Yin, Xiaochun Kuschmierz, Robert Czarske, Jürgen National Engineering Research Center of Novel Equipment for Polymer Processing Key Laboratory of Polymer Processing Engineering Ministry of Education Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing School of Mechanical and Automobile Engineering South China University of Technology Guangzhou510640 China Laboratory for Measurement and Sensor System Techniques Technische Universität Dresden Dresden01069 Germany

Laser Doppler velocimeter (LDV) allows for the measurement of the lateral velocity of a rotating cylindrical workpiece, thus, enabling the absolute radius measurement, showing great application prospect. However, several adverse factors exist that decrease the measurement accuracy, for example, the variation of the fringe spacing, the variation of the rotating frequency, the spindle error motions, and the Doppler frequency estimation, which, unfortunately, have been seldom studied. In this context, this paper comprehensively analyzes the uncertainty in the LDV-based radius measurement. First, we construct an LDV sensor and calibrate its fringe spacing. Then, the LDV-based radius measurement is modeled as an algebraic equation, suggesting that the radius is indirectly measured from several input quantities, including the Doppler frequency, the fringe spacing, and the rotation frequency, each of which owns an input uncertainty, propagating into the measurement result. Ultimately, a detailed budget for the measurement uncertainty is provided. © 2024, The Authors. All rights reserved.

关键词： Laser Doppler velocimeters

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sensory Modulation of Gait Balance in Human Locomotion: A Neuromusculoskeletal Modeling Study

Sensory Modulation of Gait Balance in Human Locomotion: A Ne...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Vahid Firouzi Omid Mohseni Oskar Von Stryk Andre Seyfarth Maziar A. Sharbafi Laufiabor Locomotion Laboratory Centre for Cognitive Science Technische Universität Darmstadt Darmstadt Germany Department of Computer Science Technische Universität Darmstadt Darmstadt Germany Department of Electrical Engineering and Information Technology Measurement and Sensor Technology Group Technische Universität Darmstadt Darmstadt Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Maintaining balance during gait relies on intricate sensorimotor processes, engaging compensatory muscle actions and reflex modulations to counteract external perturbations and prevent falls. This study aims to identify possible muscle reflex parameters that are modulated to maintain balance in the face of external perturbations through neuromechanical simulations. A reflex-based neuromusculoskeletal model is utilized and subjected to four distinct sagittal-plane perturbation types (two whole body linear momentum (WBLM) and two whole body angular momentum (WBAM) applied to the torso at five different timings. Analysis of the correlation between reflex parameters and their impact on perturbation recovery indicates seven key reflex parameters as strong candidates for modulating gait balance. For WBLM perturbations, our findings indicated the contribution of both stance and swing phase strategies by modulating Soleus positive force feedback and Iliopsoas activity. In WBAM perturbations, Vasti positive force feedback emerged as a crucial reflex contributing to knee joint stability in backward torque perturbation. The study suggests leg adjustments as a primary strategy for recovering from forward torque perturbations, achieved by modulating hamstring positive force feedback and increasing Iliopsoas muscle activation during the swing phase. These findings provide new insights into the underlying mechanisms of reflex modulation and its significance in balancing and recovering from perturbations during locomotion.

关键词： Torso Legged locomotion Torque Perturbation methods Force feedback Modulation Muscles Robot sensing systems Stability analysis Timing

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