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The non-contact method for biomechanical motion analysis of Icelandic horses

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The non-contact method for biomechanical motion analysis of ...

作者： Kohut, Piotr Giergiel, Mariusz Bujarska, Marta Augustyn, Romana Dlugosz, Boguslawa Pieszka, Magdalena Luszczyński, Jaroslaw Department of Robotics and Mechatronics Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Cracow Poland Department of Horse Breeding Animal Science Institute Agricultural University Cracow Poland

A natural fluid gait of the Icelandic horses is so called tölt. It is a gait without suspension which is characterized by a symmetrical four-beat with lateral sequence of footfalls. In this paper biomechanics of the tolt gait of the Icelandic horses is investigated. To analyze parameters of the tolt an architecture of a vision system is develop and described. Results obtained during the experimental part of the study are presented and discussed. © 2019, Springer Nature Switzerland AG.

关键词： Motion analysis

Clustering of the Blendshape facial model

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arXiv 2021年

作者： Racković, Stevo Soares, Cláudia Jakovetić, Dušan Desnica, Zoranka Ljubobratović, Relja Institute for Systems and Robotics Instituto Superior Técnico Lisbon Portugal Computer Science Department NOVA School of Science and Technology Lisbon Portugal Department of Mathematics Faculty of Sciences UNS Novi Sad Serbia 3Lateral Epic Games Company

Digital human animation relies on high-quality 3D models of the human face—rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common rigging models is the blendshape model. We present a novel approach for learning the inverse rig parameters at increased accuracy and decreased computational cost at the same time. It is based on a two fold clustering of the blendshape face model. Our method focuses exclusively on the underlying space of deformation and produces clusters in both the mesh space and the controller space—something that was not investigated in previous literature. This segmentation finds intuitive and meaningful connections between groups of vertices on the face and deformation controls, and further these segments can be observed independently. A separate model for solving the inverse rig problem is then learnt for each segment. Our method is completely unsupervised and highly parallelizable. © 2021, CC BY-NC-SA.

关键词： Deformation

Selection of Methods for Intuitive, Haptic Control of the Underwater Vehicle’s Manipulator 20th

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Selection of Methods for Intuitive, Haptic Control of the Un...

20th Polish Control Conference, PCC 2020

作者： Grzejszczak, Tomasz Babiarz, Artur Bieda, Robert Jaskot, Krzysztof Kozyra, Andrzej Ściegienka, Piotr Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland SR Robotics sp. z o.o. ul. Karoliny 4 Katowice40-186 Poland

ISBN: (纸本)9783030509354

This paper is the early report of available market and scientific solutions allowing intuitive control. Manipulator control is presented in form of the Human Machine Interaction loop that describes both machine possibilities of sensing the human control and human possibilities of sensing the machine state. The survey is presented in form of the description and discussion of the advantages, disadvantages and usability of the available solutions. The aim of the research is to chose the proper path of development of the new way of intuitive control. © 2020, Springer Nature Switzerland AG.

关键词： Manipulators

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

A Comparative Study of Human Activity Recognition: Motion, Tactile, and multi-modal Approaches

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arXiv 2025年

作者： Belcamino, Valerio Le, Nhat Minh Dinh Luu, Quan Khanh Carfì, Alessandro Ho, Van Anh Mastrogiovanni, Fulvio TheEngineRoom Department of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy Faculty of Mechanical Engineering The University of Danang University of Science and Technology Danang 54 Nguyen Luong Bang Da Nang550000 Viet Nam Soft Haptics Lab School of Materials Science Japan Advanced Institute of Science and Technology Nomi923-1292 Japan

Human activity recognition (HAR) is essential for effective Human-Robot Collaboration (HRC), enabling robots to interpret and respond to human actions. This study evaluates the ability of a vision-based tactile sensor to classify 15 activities, comparing its performance to an IMU-based data glove. Additionally, we propose a multi-modal framework combining tactile and motion data to leverage their complementary strengths. We examined three approaches: motion-based classification (MBC) using IMU data, tactile-based classification (TBC) with single or dual video streams, and multi-modal classification (MMC) integrating both. Offline validation on segmented datasets assessed each configuration’s accuracy under controlled conditions, while online validation on continuous action sequences tested online performance. Results showed the multi-modal approach consistently outperformed single-modality methods, highlighting the potential of integrating tactile and motion sensing to enhance HAR systems for collaborative robotics. © 2025, CC BY.

关键词： Collaborative robots

Finite-Time Model-Based Event-Triggered Control of LTI Systems

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Finite-Time Model-Based Event-Triggered Control of LTI Syste...

IEEE Conference on Decision and Control

作者： Zhu Xuan-Zhi Pedro Casau Carlos Silvestre Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Science and Technology University of Macau Taipa Macau

ISBN: (纸本)9781665436601

In this paper, we design a model-based event-triggered controller for networked control of a linear time-invariant (LTI) system using a finite-time observer. Under the framework of hybrid dynamical systems, we show that, if the plant dynamics are detectable and stabilizable, then: 1) the zero error set is globally asymptotically stable and globally finite-time stable for the closed-loop system; 2) the closed-loop system is robust to small state perturbations; 3) the state of the plant converges to a neighborhood of the origin that can be made arbitrarily small; 4) the number of transmissions through the network is finite. We illustrate these results through numerical simulations.

关键词： Linear systems Pathology Perturbation methods Conferences Observers Numerical simulation Numerical models

A Neural Network Approach in WSN Real-Time Monitoring System to Measure Indoor Air Quality

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A Neural Network Approach in WSN Real-Time Monitoring System...

Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Thadeu Brito José Lima Elias Biondo Alberto Nakano Ana I. Pereira Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança (IPB) Campus de Santa Apolónia Bragança Portugal Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança (IPB) Campus de Santa Apolónia Bragança Portugal INESC TEC-INESC Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Federal University of Technology Paraná Toledo Brazil

Indoor Air Quality (IAQ) pertains to the air quality within a specific space and is directly linked to the well-being and comfort of its occupants. In line with this objective, this research presents a real-time system dedicated to monitoring and predicting IAQ, encompassing both thermal comfort and gas concentration. The system initiates with a data acquisition, wherein a set of sensors captures environmental parameters and transmits this data for storage in a database. The measured parameters are analyzed by a neural network algorithm that predicts anomalies based on historical data. The neural network model generated predictions from 75.9% to 98.1% (depending on the parameter) of precision during regular situations. After that, a test with smoke in the same place was done to validate the model, and the results showed it could detect anomalies. Finally, prediction data are stored in a new database and displayed on a dashboard for monitoring in real-time measured and prediction data.

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Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions

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arXiv 2025年

作者： Yao, Tianliang Lu, Bo Kowarschik, Markus Yuan, Yixuan Zhao, Hubin Ourselin, Sebastien Althoefer, Kaspar Ge, Junbo Qi, Peng Department of Control Science and Engineering College of Electronic and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electronic Engineering Faculty of Engineering The Chinese University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electrical Engineering Soochow University Suzhou215131 China Bavaria Forchheim91301 Germany CREATe Division of Surgery & Interventional Science UCL LondonHA7 4LP United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London LondonSE1 7EH United Kingdom Centre for Advanced Robotics @ Queen Mary School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Cardiology Zhongshan Hospital Fudan University Shanghai Institute of Cardiovascular Diseases Shanghai200032 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Endovascular procedures have revolutionized the treatment of vascular diseases thanks to minimally invasive solutions that significantly reduce patient recovery time and enhance clinical outcomes. However, the precision and dexterity required during these procedures poses considerable challenges for interventionists. Robotic systems have emerged offering transformative solutions, addressing issues such as operator fatigue, radiation exposure, and the inherent limitations of human precision. The integration of Embodied Intelligence (EI) into these systems signifies a paradigm shift, enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, advanced computer vision, medical image analysis, and machine learning techniques, are at the forefront of this evolution. These methods augment procedural intelligence by facilitating real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further refine navigation strategies and replicate experts’ techniques. This review systematically examines the integration of EI principles into robotic technologies, in relation to endovascular procedures. We discuss recent advancements in intelligent perception and data-driven control, and their practical applications in robot-assisted endovascular procedures. By critically evaluating current limitations and emerging opportunities, this review establishes a framework for future developments, emphasizing the potential for greater autonomy and improved clinical outcomes. Emerging trends and specific areas of research, such as federated learning for medical data sharing, explainable AI for clinical decision support, and advanced human-robot collaboration paradigms, are also explored, offering insights into the future direction of this rapidly evolving field. © 2025, CC BY.

关键词： Medical robotics

A New Computer-Aided Solution for the Automatic Detection of Metal Stent Struts in Follow-up Evaluation in OCT Images

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A New Computer-Aided Solution for the Automatic Detection of...

Computers in Cardiology (CinC)

作者： Zofia Schneider Elżbieta Pociask Klaudia Proniewska Radek Kolecki Department of Biocybernetics and Biomedical Engineering BioMedical Imaging Science Club AGH University of Science and Technology Kraków Poland Center for Digital Medicine and Robotics Jagiellonian University Medical College Kraków Poland Department of Bioinformatics and Telemedicine Faculty of Medicine Jagiellonian University Medical College Kraków Poland University Hospital in Kraków Poland Jagiellonian University Medical College Kraków Poland

Stenting is commonly used in the treatment of coronary artery disease. To optimize the results of the procedure, it is crucial to evaluate the stent immediately after its implantation, and then to later monitor how it becomes covered by the neointima. One modality used for this is intravascular optical coherence tomography (IVOCT). While the identification and assessment of stent struts in IVOCT images is routinely done in clinical practice, manual analysis remains laborious and time-consuming. To address this, automated algorithms for stent segmentation have recently been developed. However, these are mainly used for stents without thick tissue coverage. This study proposes a new computer-aided method to automate the detection of both covered and uncovered metal stent struts in OCT images. In general, the algorithm involves segmenting potential stent strut shadows, analyzing the distribution of pixel intensities in detected areas, and then classifying objects. The algorithm has been validated on 606 cross-sections chosen randomly from 34 cases containing pullbacks: at baseline and at 3-36-month follow-up. The presented algorithm achieves sensitivity of 88% and precision of 90% including in-stent restenosis cases.

关键词： Image segmentation Sensitivity Optical coherence tomography Metals Morphology Manuals Classification algorithms