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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tamás Karácsony Nicholas Fearns Christian Vollmar Denise Birk Jan Rémi Soheyl Noachtar João Paulo Silva Cunha Center for Biomedical Engineering Research (C-BER) Institute for Systems’ Engineering and Computers Technology & Science (INESC TEC) Porto Portugal Faculty of Engineering (FEUP) University of Porto Porto Portugal Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Neurology Epilepsy Center University of Munich Munich Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Epileptic seizures are clearly characterized by their displayed behavior, the semiology, which is used in diagnosis and classification as a base for therapy. This article presents a novel 4K 3D video recording and reviewing system for epilepsy monitoring, introducing a novel perspective and allowing continuous recording and review of 3D videos in the epilepsy monitoring unit (EMU), providing significantly more detail than the current clinical systems, which can lead to the recognition of more Movements of Interest (MOIs) and may reduce inter-rater variability. To put the system to an initial test in clinical practice the article presents three real-world examples of subtle MOIs, that could only be appreciated on the 4K-video, but not on the VGA-video, recorded as part of the clinical routine. In conclusion, a 4K-RGB recording, 3D cropping, and 3D video playing system was developed, implemented, and tested for realworld clinical scenarios, considering the specific requirements of clinical monitoring in EMUs. The new data acquisition setup can support clinical diagnosis, which may lead to new insights in the field of epilepsy and the development of AI approaches in the future.

关键词： Three-dimensional displays Reviews Epilepsy Medical treatment Semiotics Systems support Artificial intelligence engineering in medicine and biology Monitoring Video recording

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A Multi-metric Modular Framework for Human-like Gait Analysis Based on a Recorded Set of Variable Gait Patterns

A Multi-metric Modular Framework for Human-like Gait Analysi...

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IEEE-RAS International Conference on Humanoid Robots

作者： Stephan Kapteijn Wansoo Kim Laura Marchal-Crespo Luka Peternel Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Department of Robotics Hanyang University Seoul Korea ARTORG Center for Biomedical Engineering Research University of Bern Switzerland

ISBN: (纸本)9798350309805

Walking is an essential part of almost all activities of daily living. We use different gait patterns in different situations, e.g., moving around the house, performing various sports, or when compensating for an injury. However, how humans perform this gait tailoring remains a partially unknown process. To this end, the influence of various performance metrics on the optimality and diversity of gait patterns can provide us with more insight. To analyse gait in terms of pattern diversity and performance metrics related to physical aspects, such as joint torque, fatigue, and manipulability, we propose a multi-metric gait analysis framework that simultaneously accounts for these parameters. We used a recorded set of versatile gait patterns that are already dynamically stable and physiologically feasible. To that end, 45 gait variations-varying in stride length, step height, and walking speed-were recorded in a motion capture experiment. Results of analysis using the recorded dataset are presented for a baseline case (with all optimisation weights set to one), which serves as the first step for future research, in particular giving insights into specific aspects of the gait, e.g., joint loading, long-term performance, and capacity to sustain ground reaction forces.

关键词： Measurement Legged locomotion Torque Loading Humanoid robots Fatigue Physiology

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Autonomous Intelligent Navigation for Flexible Endoscopy Using Monocular Depth Guidance and 3-D Shape Planning

Autonomous Intelligent Navigation for Flexible Endoscopy Usi...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yiang Lu Ruofeng Wei Bin Li Wei Chen Jianshu Zhou Qi Dou Dong Sun Yun-hui Liu Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Biomedical Engineering City University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Recent advancements toward perception and decision-making of flexible endoscopes have shown great potential in computer-aided surgical interventions. However, owing to modeling uncertainty and inter-patient anatomical variation in flexible endoscopy, the challenge remains for efficient and safe navigation in patient-specific scenarios. This paper presents a novel data-driven framework with self-contained visual-shape fusion for autonomous intelligent navigation of flexible endoscopes requiring no priori knowledge of system models and global environments. A learning-based adaptive visual servoing controller is proposed to online update the eye-in-hand vision-motor configuration and steer the endoscope, which is guided by monocular depth estimation via a vision transformer (ViT). To prevent unnecessary and excessive interactions with surrounding anatomy, an energy-motivated shape planning algorithm is introduced through entire endoscope 3-D proprioception from embedded fiber Bragg grating (FBG) sensors. Furthermore, a model predictive control (MPC) strategy is developed to minimize the elastic potential energy flow and simultaneously optimize the steering policy. Dedicated navigation experiments on a robotic-assisted flexible endoscope with an FBG fiber in several phantom environments demonstrate the effectiveness and adaptability of the proposed framework.

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Autonomous Intelligent Navigation for Flexible Endoscopy Using Monocular Depth Guidance and 3-D Shape Planning

arXiv

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arXiv 2023年

作者： Lu, Yiang Wei, Ruofeng Li, Bin Chen, Wei Zhou, Jianshu Dou, Qi Sun, Dong Liu, Yun-Hui T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Biomedical Engineering City University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong

关键词： Endoscopy

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Physics Engine-Based Whole-Hand Haptic Rendering for Sensorimotor Neurorehabilitation

Physics Engine-Based Whole-Hand Haptic Rendering for Sensori...

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World Haptics Conference

作者： Raphael Rätz Laura Marchal-Crespo Motor Learning and Neurorehabilitation Laboratory ARTORG Center for Biomedical Engineering Research University of Bern Switzerland Department of Cognitive Robotics Delft University of Technology Delft Netherlands Department Rehabilitation Medicine Erasmus Medical Center Rotterdam Netherlands

Whole-hand haptic rendering could lead to more naturalistic and intuitive virtual hand-object interactions, which could be especially beneficial for applications such as sensorimotor robotic neurorehabilitation. However, the majority of previously proposed whole-hand haptic rendering algorithms rely on effortful custom implementations or are not suited for the grounded haptic devices often used in neurorehabilitation. Therefore, we suggest a framework for whole-hand haptic rendering based on a readily available physics engine. We employ a bilateral position-position teleoperation framework between a haptic rehabilitation device and a simulated hand avatar with added exercise-specific haptic rendering. Moreover, in consideration of the needs of neurological patients, we introduce an adaptive damping of the haptic device during hand-object interactions for increased stabilization of the patient’s limb. We present the first results of the feasibility of the proposed framework in a haptic rehabilitation exercise. In an ongoing clinical study, the practical application of the presented framework is currently investigated.

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A fully automated pipeline of cam-type FAI parameters measurement from clinical computed tomography (CT) images in asymptomatic patients

A fully automated pipeline of cam-type FAI parameters measur...

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Iranian Conference of biomedical engineering (ICBME)

作者： Sareh Tayyebinezhad Mansoor Fatehi Hossein Arabalibeik Hossein Ghadiri Department of Medical Physics and Biomedical Engineering Tehran University of Medical Science Tehran Iran Department of Imaging Informatics Virtual University of Medical Science Tehran Iran Research Center for Biomedical Technologies and Robotics Tehran University of Medical Science Tehran Iran

A fully automated pipeline, CAM-FAI-CAD, was used to automatically obtain slice selection, segmentation, landmark detection, and measurement of parameters related to femoral deformity to establish an auxiliary measurement algorithm for developmental screening or diagnosis of cam-type FAI using clinical computed abdomen tomography (CT) images of asymptomatic patients. The novel CAM-FAI-CAD pipeline consists of two components: (i) the use of a convolutional neural network (CNN) based on Le-Net to generate accurate slice selection to identify hip in clinical CT images(ii) the use of image processing algorithms for diagnostic landmark detection to quantify cam-type FAI-related parameters in asymptomatic patients. The CAM-FAI-CAD pipeline was used to train CNN for hip slice selection (n=14500 slices) and analyze clinical CT images of the femur to measure the alpha angle (AA) and femoral head-neck offset (FHNO) in male (n=17) and female (n=13) asymptomatic patients. Automatic CAM -FAI-CAD results were obtained for slice selection, segmentation, and center of femoral head detection as demonstrated by the accuracy (98.5%), Dice similarity index (DSI; 0.9 in the axial plane and 0.92 in the coronal plane) and accuracy (94% in the axial plane and 0.96 in the coronal plane) respectively, while the accuracy of the automatically acquired parameters for AA and FHNO was both around 88% compared to the radiologist’s manually calculated parameters. AA and FHNO measurements using the fully automated CAM-FAI-CAD pipeline were highly in agreement with radiologists’ measurements and could be used to assess cam-type FAI with high diagnostic accuracy and efficiency or evolutionary.

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Bio-Signal Activated FPGA-Based System for Robotic-Assisted Rehabilitation

Bio-Signal Activated FPGA-Based System for Robotic-Assisted ...

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robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Hrishikesh H Pillai Lal Priya P S Kavinga Upul Ekanayaka Jackrit Suthakorn Branesh M. Pillai College of Engineering Trivandrum APJ Abdul Kalam Technological University Thiruvananthapuram Kerala India ACCELR Colombo Western Province Sri Lanka Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

This paper presents a pivotal contribution to multi-modal bio-signal processing for robotic control applications, leveraging cutting-edge Field Programmable Gate Array (FPGA) technology to unlock new frontiers in accuracy, efficiency, and real-time control. The comprehensive hardware implementation strategy is characterized by the strategic utilization of a Fast Fourier Transform/ Inverse Fast Fourier Transform (FFT/IFFT) - based filtering algorithm optimized for Electroencephalogra-phy (EEG) signal processing. Concurrently, an inventive movement classification methodology powered by Electrooculography (EOG) signals is introduced. The core proposition of the paper revolves around the implementation of an EEG and EOG-driven Multi Input Single Output (MISO) control system, intended for the control of rehabilitation robots. The effectiveness of the proposed method was verified by simulation in Lab VIEW. The algorithm has been seamlessly integrated into the My Rio 1900 hardware platform, leveraging the immense processing power of a Xilinx FPGA.

关键词： Electrooculography Signal processing algorithms Filtering algorithms MISO communication Control systems Electroencephalography Hardware

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Electromagnets Under the Table: an Unobtrusive Magnetic Navigation System for Microsurgery

arXiv

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arXiv 2023年

作者： Schonewille, Adam He, Changyan Forbrigger, Cameron Wu, Nancy Drake, James Looi, Thomas Diller, Eric Department of Mechanical and Industrial Engineering The University of Toronto Canada PCIGITI center The SickKids Hospital Canada Department of Health Sciences and Technology ETH Zürich Switzerland Robotics Institute Institute of Biomedical Engineering The University of Toronto Canada

Miniature magnetic tools have the potential to enable minimally invasive surgical techniques to be applied to space-restricted surgical procedures in areas such as neurosurgery. However, typical magnetic navigation systems, which create the magnetic fields to drive such tools, either cannot generate large enough fields, or surround the patient in a way that obstructs surgeon access to the patient. This paper introduces the design of a magnetic navigation system with eight electromagnets arranged completely under the operating table, to endow the system with maximal workspace accessibility, which allows the patient to lie down on the top surface of the system without any constraints. The found optimal geometric layout of the electromagnets maximizes the field strength and uniformity over a reasonable neurosurgical operating volume. The system can generate non-uniform magnetic fields up to 38 mT along the x and y axes and 47 mT along the z axis at a working distance of 120 mm away from the actuation system workbench, deep enough to deploy magnetic microsurgical tools in the brain. The forces which can be exerted on millimeter-scale magnets used in prototype neurosurgical tools are validated experimentally. Due to its large workspace, this system could be used to control milli-robots in a variety of surgical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical applications

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Age-Dependent Spatial Patterns of Brain Noise in fMRI Series

Age-Dependent Spatial Patterns of Brain Noise in fMRI Series

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Andrea Scarciglia Vincenzo Catrambone Martina Bianco Claudio Bonanno Nicola Toschi Gaetano Valenza Bioengineering and Robotics Research Center E.Piaggio & Department of Information Engineering School of Engineering University of Pisa Italy Department of Mathematics University of Pisa Italy Department of Biomedicine and Prevention University of Rome ”Tor Vergata” Rome Italy A.A. Martinos Center for Biomedical Imaging MGH and Harvard Medical School Boston MA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Functional Magnetic Resonance Imaging (fMRI) serves as a unique non-invasive tool for investigating brain function by analyzing blood oxygenation level-dependent (BOLD) series. These signals result from the complex interplay between deterministic and stochastic components underpinning biological brain activity. In this context, the quantification of the stochastic component, here defined as brain noise, is challenging without making assumptions on the deterministic dynamics. Leveraging on Approximate Entropy, in this study we present a methodological framework aimed to estimate intrinsic stochastic brain dynamics through fMRI data analysis without making assumption on the deterministic model. We estimated brain noise from fMRI series of 200 participants from the publicly available Cam-CAN dataset, aiming to quantify the amount of stochastic dynamics in different brain regions. Moreover, we hypothesize that a functional relationship exists between intrinsic brain noise and subject’s age. Results indicate that a significant part - approximately 18% to 60% - of the fMRI signal power can be attributed to the intrinsic stochastic dynamics within the brain, and a linear augmentation is reported in association with the maturation process. These findings underscore the physiological importance of characterizing neural noise and its unique distributions across various brain regions.

关键词： Brain Data analysis Noise Stochastic processes Functional magnetic resonance imaging Brain modeling Physiology Entropy Data models Blood

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Fully implantable and battery-free wireless optoelectronic system for modulable cancer therapy and real-time monitoring

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npj Flexible Electronics 2023年第1期7卷 119-130页

作者： Kiho Kim In Sik Min Tae Hee Kim Do Hyeon Kim Seungwon Hwang Kyowon Kang Kyubeen Kim Sangun Park Jongmin Lee Young Uk Cho Jung Woo Lee Woon-Hong Yeo Young Min Song Youngmee Jung Ki Jun Yu Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic EngineeringYonsei University50 Yonsei-roSeodaemun-guSeoul 03722Republic of Korea Center for Biomaterials Biomedical Research InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Fusion Research and Collaboration Biomedical Research InstituteSeoul National University HospitalSeoul 03080Republic of Korea School of Electrical Engineering and Computer Science(EECS) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea PA1 Foundry BusinessSamsung Electronics1 Samsungjeonja-roHwaseong-siGyeonggi-doRepublic of Korea R&D Center DNA Lab PharmaresearchSeongnam 13486Republic of Korea KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoul 136-705Republic of Korea School of Materials Science and Engineering Energy Materials for Soft Electronics LaboratoryPusan National UniversityBusan 46241Republic of Korea IEN Center for Human-Centric Interfaces and Engineering and George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Robotics and Intelligent Machines Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering YU-Korea Institute of Science and Technology(KIST)InstituteYonsei University50Yonsei-roSeodaemun-guSeoul 03722Republic of Korea

Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been developed to resolve the issues of bulky and expensive conventional PDT systems and to implement continuous and repetitive *** implantable PDT systems,however,are not able to perform multiple functions simultaneously,such as modulating light intensity,measuring,and transmitting tumor-related data,resulting in the complexity of cancer ***,we introduce a flexible and fully implantable wireless optoelectronic system capable of continuous and effective cancer treatment by fusing PDT and hyperthermia and enabling tumor size monitoring in *** system exploits micro inorganic light-emitting diodes(μ-LED)that emit light with a wavelength of 624 nm,designed not to affect surrounding normal tissues by utilizing a fully programmable light intensity ofμ-LED and precisely monitoring the tumor size by Si phototransistor during a long-term implantation(2–3 weeks).The superiority of simultaneous cancer treatment and tumor size monitoring capabilities of our system operated by wireless power and data transmissions with a cell phone was confirmed through in vitro experiments,ray-tracing simulation results,and a tumor xenograft mouse model in *** all-in-one single system for cancer treatment offers opportunities to not only enable effective treatment of tumors located deep in the tissue but also enable precise and continuous monitoring of tumor size in real-time.

关键词： optoelectronic enable battery

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