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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Chifuyu Matsumoto Yuka Sone Jinseok Woo Yasuhiro Ohyama Sustainable Engineering Program Graduate School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Department of Mechanical Engineering School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Recently, Japan has been facing various social issues, and to solve these problems, it is necessary to create a society where diverse people can live comfortably. To build such a society, appropriate support tailored to individuals is required, necessitating the analysis of various types of information to understand the people's needs at any given moment. In particular, analyzing non-verbal elements such as posture, facial expressions, and gestures is crucial, as these naturally indicate a person's condition. Therefore, this study focuses on human gestures, one of the non-verbal elements, and proposes a gesture recognition method using Dynamic Time Warping (DTW) and K-Means. We demonstrated that symbolic gesture recognition is possible by recording arm trajectories from skeletal measurements with an RGB-D camera.

关键词： Shape Time series analysis Gesture recognition Cameras Trajectory Recording

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Nonlinear Impedance Matching Approach (NIMA) for Robust Haptic Rendering During Robotic Laparoscopy Surgery

Nonlinear Impedance Matching Approach (NIMA) for Robust Hapt...

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

作者： Aiden Mazidi Andrés C. Ramos Amir Sayadi Javad Dargahi Jake Barralet Liane S. Feldman Amir Hooshiar Mechanical Engineering Department Surgical Robotics Laboratory Concordia University Montreal QC Canada Surgical Performance Enhancement and Robotics (SuPER) Centre Department of Surgery McGill University Montreal QC Canada

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

The long overdue clinical need for haptic-enabled surgical robotic systems has been poorly addressed in the past two decades. Haptic systems have shown high agility and precision, which can translate into less risk to patients. However, regulatory requirements, such as validating haptic systems as “human-in-the-loop” components have impeded their commercialization. The complexity of regulatory validation arises from the “human-in-the-loop” design of the system that complicates the validation of inter-subject dynamics variations. In this study, we have implemented an Impedance Matching Approach (IMA) for force feedback estimation and rendering that does not include the “human” in the loop as a component. In other words, the amount of force feedback rendered at the haptic device does not essentially rely on the human to adequately “resist” the motion of the haptic device. This feature makes our proposed method intrinsically safe against the haptic “kick” which commonly happens if the surgeon releases the handle of the haptic interface while rendering non-zero forces. We have previously shown the accuracy and stability of the linear IMA method for single-force components [1]. In addition, we have implemented a general Non-linear IMA (NIMA) framework that accounts for the impedance parameters when the instrument-tissue interaction is not linear. We now present successful test results on the accuracy of the NIMA method with 3D contact forces and motion commands in a robot-assisted laparoscopy environment.

关键词： Accuracy Torque Three-dimensional displays Biological system modeling Impedance matching Force feedback Resists Rendering (computer graphics) Human in the loop Stability analysis

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Dynamic Heart Simulator for Ultrasound-Guided Pericardiocentesis

Dynamic Heart Simulator for Ultrasound-Guided Pericardiocent...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kim Yan Wanquan Yan Shing Shin Cheng Department of Mechanical and Automation Engineering CUHK T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering CUHK T Stone Robotics Institute Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Multi-Scale Medical Robotics Center Hong Kong

Pericardiocentesis is an important surgical intervention to treat a medical condition called pericardial effusion, during which excessive fluid accumulates around the heart, potentially leading to life-threatening situation. It involves the insertion of a needle and catheter towards the heart into the pericardial space to drain the excessive fluid under ultrasound (US) guidance. The risky procedure requires surgeons to acquire sufficient training to ensure safe execution of the procedure. However, existing heart simulators lack dynamic features, do not offer realistic images under US imaging, and are not reusable. This work presents a dynamic heart simulator (DHS) with pericardial effusion to mimic the beating motion of the human heart and the realistic US imaging results. The beating heart motion is realized using a hydraulic actuation system connected to a double-layer balloon set. The clear and realistic US imaging results are obtained through a unique formula proposed for the chest tissue and the cardiac muscle. A characterization method was also developed to allow customization of important anatomical parameters in the DHS. The experimental results show that the DHS allowed highly realistic simulation of the beating heart, cardiac muscle, and pericardium under US imaging and has been demonstrated to enable successful US-guided pericardiocentesis.

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Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement

arXiv

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arXiv 2024年

作者： Lu, Yiang Li, Bin Chen, Wei Yan, Junyan Cheng, Shing Shin Wang, Jiangliu Zhou, Jianshu Dou, Qi Liu, Yun-Hui T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese 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

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270◦ and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space without interactions as well as constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

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Diagnostic of Injury Risk in the Anterior Cruciate Ligament Based on Shape Context Description of the Intercondylar Notch Curvature

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Journal of engineering and Science in Medical Diagnostics and Therapy 2022年第2期5卷 021001页

作者： Dias, Joaõ Paulo Bhuiyan, Ariful Shamim, Nabila Department of Civil and Mechanical Engineering Shippensburg University of Pennsylvania 1871 Old Main Drive ShippensburgPA17257 United States Mechanical Engineering Program University of Houston Clear Lake 2700 Bay Area Blvd. HoustonTX77058 United States Department of Chemical Engineering Prairie View AandM University 100 University Drive Prairie ViewTX77446 United States

An estimated number of 300,000 new anterior cruciate ligament (ACL) injuries occur each year in the United States. Recent studies have pointed out a correlation between the curvature of the femur intercondylar notch and the risk of noncontact ACL injury. Although several magnetic resonance (MR) imaging-based ACL diagnostics methods have already been proposed in the literature, most of them are based on machine learning or deep learning strategies, which are computationally expensive. In this paper, we propose a diagnostics framework for the risk of injury in the ACL based on the application of the inner-distance shape context (IDSC) to describe the curvature of the intercondylar notch from MR images. First, the contours of the intercondylar notch curvature from 91 MR images of the distal end of the femur (70 healthy and 21 with confirmed ACL injury) were extracted manually using standard image processing tools. Next, the IDSC was applied to calculate the similarity factor between the extracted contours and reference standard curvatures. Finally, probability density functions of the similarity factor data were obtained through parametric statistical inference, and the accuracy of the ACL injury risk diagnostics framework was assessed using receiver operating characteristic analysis (ROC). The overall results for the area under the curve (AUC) showed that the method reached a maximum accuracy of about 66%. Furthermore, the sensitivity and specificity results showed that an optimum discrimination threshold value for the similarity factor can be pursued that minimizes the incidence of false positives and false negatives simultaneously. © 2022 American Society of mechanical Engineers (ASME). All rights reserved.

关键词： Magnetic resonance

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Hierarchical Co3O4@ZnO binary oxide microstructures for high-performance photoelectrochemical glucose sensing

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Ceramics International 2025年

作者： Hilal, Muhammad Ali, Yasir Fayaz, Huma Cai, Zhicheng Kim, Hyojung Abdo, Hany S. Alnaser, Ibrahim A. Department of Semiconductor System Engineering Sejong University Seoul05006 Korea Republic of Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul04620 Korea Republic of Deanship of Scientific Research King Saud University Riyadh11421 Saudi Arabia

Binary metal oxides with hierarchical architectures have emerged as promising platforms for non-enzymatic glucose sensing due to their tunable optoelectronic properties and intrinsic redox activity. In this work, we report a sustainable, additive-free synthesis of a 3D Co3O4@ZnO heterostructure via a one-pot hydrothermal method followed by inert-gas calcination, effectively minimizing oxygen interference and preserving structural integrity. The composite integrates the redox-rich, narrow-bandgap Co3O4 with UV-active, high-mobility ZnO to form a type-II heterojunction that enables broad-spectrum light absorption, efficient charge separation, and suppressed carrier recombination. These features also enhance the electrochemical behavior of the material, yielding an ultra-low charge transfer resistance (Rct = 3.35 Ω) and facilitating rapid electron transport and ion diffusion at the electrode/electrolyte interface. As a result, the Co3O4@ZnO heterostructure supports high-performance dual-mode glucose sensing under both dark and illuminated conditions. In the absence of light, the sensor exhibits a sensitivity of 114 μA cm−2 mM−1, a wide linear range (0.2–4 mM), and a fast response time (21 s). Upon illumination, a photoinduced internal electric field at the heterojunction interface significantly boosts performance, achieving a photoelectrochemical sensitivity of 231 μA cm−2 mM−1 with a rapid response of 11 s. Photocurrent studies further reveal response and recovery times of 5 s and 4 s, respectively. Importantly, the sensor also demonstrates reliable glucose detection in real urine samples, confirming its suitability for real-time physiological monitoring. These results highlight the Co3O4@ZnO heterostructure as an efficient and eco-friendly platform for stable, reliable, and high-performance dual-mode glucose sensing. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Redox reactions

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Learning to Refine Input Constrained Control Barrier Functions via Uncertainty-Aware Online Parameter Adaptation

arXiv

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arXiv 2024年

作者： Kim, Taekyung Kee, Robin Inho Panagou, Dimitra Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Robotics Department of Aerospace Engineering University of Michigan Ann ArborMI48109 United States

Control Barrier Functions (CBFs) have become powerful tools for ensuring safety in nonlinear systems. However, finding valid CBFs that guarantee persistent safety and feasibility remains an open challenge, especially in systems with input constraints. Traditional approaches often rely on manually tuning the parameters of the class K functions of the CBF conditions a priori. The performance of CBFbased controllers is highly sensitive to these fixed parameters, potentially leading to overly conservative behavior or safety violations. To overcome these issues, this paper introduces a learning-based optimal control framework for online adaptation of Input Constrained CBF (ICCBF) parameters in discrete-time nonlinear systems. Our method employs a probabilistic ensemble neural network to predict the performance and risk metrics, as defined in this work, for candidate parameters, accounting for both epistemic and aleatoric uncertainties. We propose a two-step verification process using Jensen-Rényi Divergence and distributionally-robust Conditional Value at Risk to identify valid parameters. This enables dynamic refinement of ICCBF parameters based on current state and nearby environments, optimizing performance while ensuring safety within the verified parameter set. Experimental results demonstrate that our method outperforms both fixed-parameter and existing adaptive methods in robot navigation scenarios across safety and performance metrics. [Project Page]1 [Code] [Video] © 2024, CC BY.

关键词： Discrete time control systems

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The planning strategy of robotics technology for nuclear decommissioning in Taiwan

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Nuclear engineering and Technology 2024年第1期56卷 64-69页

作者： Tu, Chung Yi Chen, Kuen Tsann Ting, Kuen Yang Sheng, Chin Doctoral Program in Data Science and Industrial Analytics National Chung Hsing University Taichung County 402 Taiwan Department of Applied Mathematics National Chung Hsing University Taichung County 402 Taiwan Department of Mechanical Engineering Lunghwa Univesity of Science and Technology Taoyuan County 33306 Taiwan

According to the market research report, the nuclear decommissioning services market is currently experiencing considerable growth, with a projected Compound Annual Growth Rate (CAGR) of nearly 13% during the 2020–2024 forecast period. This expansion is primarily fueled by the advancement of Industry 4.0, in conjunction with the emergence of cutting-edge technologies such as the Internet of Things, big data, artificial intelligence, and 5G. Even though the fact that robots have already been utilized in the nuclear industry, their adoption has been hindered by conservative regulations. However, the nuclear decommissioning market presents an opportunity for the advancement of robotics technology. The British have already invested heavily in encouraging the use of intelligent robots for nuclear decommissioning, and other countries, such as Taiwan, should follow suit. Taiwan's flourishing robotics development industry in manufacturing, logistics, and other domains can be leveraged to introduce advanced robotics in the decommissioning of its nuclear power plants. By doing so, Taiwan can establish itself as a competitive player in the nuclear decommissioning services market for the next two decades. © 2023 Korean Nuclear Society

关键词： Intelligent robotics Nuclear decommissioning Planning strategy

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PyDigitizer – A Python module for digitizing 2D curves

PyDigitizer – A Python module for digitizing 2D curves

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2022 International Conference on Applied Sciences, ICAS 2022

作者： Nedelcu, D. Latinovic, T. Sikman, L. "Babes-Bolyai" University of Cluj-Napoca Faculty of Engineering Department of Engineering Sciences Traian Vuia square no.1-4 Resita320085 Romania University of Banja Luka Faculty of Mechanical Engineering Department of Mechatronic and Robotics Stepe Stepanovica 71 Banja Luka78000 Bosnia and Herzegovina University of Banja Luka Faculty of Technology Stepe Stepanovića 73 Banja Luka78000 Bosnia and Herzegovina

The paper presents a PyDigitizer application designed to extract numerical coordinates of existing curves from image format. The application is created in the Python programming language and offers the possibility of manual extraction of curve coordinates, intersection of curves with user-specified abscissa or ordinate, generation of polynomial regression equations of curves. All generated information (coordinates, intersection points, regression curves) can be exported to Microsoft Excel. Also, point coordinates of Excel curves can be loaded into PyDigitizer to obtain polynomial regression curves or intersections. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： High level languages

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