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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Seong-Su Park Huseyin Tugcan Dinc Kwang-Hyun Lee Jee-Hwan Ryu Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

The present paper proposes a novel strategy to enhance admittance-type haptic interaction using bidirectional time-domain passivity control. While admittance-type haptic interaction is widely employed in human-robot collaboration, its ability to render low virtual inertia can be limited, leading to unstable interactions with rigid environments. The proposed strategy seeks to stabilize a lower range of virtual inertia while maintaining responsive behavior in free space. The Franka Emika Collaborative robot was utilized in various experiments to test the approach, and the results indicate that the bidirectional time-domain passivity controller can improve interaction performance relative to the conventional unidirectional time-domain passivity approach. This technique may aid in reducing operator fatigue during the manipulation of heavy, non-back drivable industrial robots while preserving a lightweight feel.

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A YOLOv7-Based Method for Detecting Buttons in Service Robots during Autonomous Elevator-Taking Tasks

A YOLOv7-Based Method for Detecting Buttons in Service Robot...

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International Conference on Advanced robotics and Intelligent Systems (ARIS)

作者： Ming-Hsin Chen Wei-Hsiang Huang Tzuu-Hseng S. Li aiRobots Lab. MS Degree Program on AI Robotics National Cheng Kung University Tainan Taiwan Dept. of Electrical Engineering aiRobots Lab. NCKU Tainan Taiwan

This paper presents a novel visual algorithm based on YOLOv7, designed to enhance the robustness of automated elevator-taking service robots in detecting elevator buttons. Traditional solutions for elevator interaction, such as image processing methods, feature selection, or wireless communication protocols, have inherent limitations related to communication security issues and additional equipment costs. In order to overcome these challenges, our method leverages a physical robotic arm and the YOLOv7 object detection neural network to improve the accuracy of elevator button detection and enable effective robot-elevator interaction. To identify elevator buttons, we employ single-stage object detection algorithms along with multiple cameras to capture comprehensive environmental information during experimental trials. By utilizing these techniques, our proposed method ensures the safety and reliability of the automated elevator-taking process for service robots. Experimental results demonstrate the effectiveness of our algorithm in accurately detecting elevator buttons across various testing scenarios. Overall, our method offers a more suitable solution for service robots to autonomously navigate elevators and perform their intended tasks.

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Microengineered platforms for characterizing the contractile function of in vitro cardiac models

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Microsystems & Nanoengineering 2022年第1期8卷 29-50页

作者： Wenkun Dou Manpreet Malhi Qili Zhao Li Wang Zongjie Huang Junhui Law Na Liu Craig A.Simmons Jason T.Maynes Yu Sun Department of Mechanical and Industrial Engineering University of TorontoTorontoON M5S 3G8Canada Program in Molecular Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Biochemistry University of TorontoTorontoON M5S 1A8Canada Institute of Robotics and Automatic Information System and the Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China School of Mechanical&Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China Institute of Biomedical Engineering University of TorontoTorontoON M5S 3G9Canada Translational Biology&Engineering Program Ted Rogers Centre for Heart ResearchTorontoON M5G 1M1Canada Department of Anesthesiology and Pain Medicine University of TorontoTorontoON M5S 1A8Canada Department of Anesthesia and Pain Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Computer Science University of TorontoTorontoON M5T 3A1Canada

Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.

关键词： cardiac contractile regularity

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A Machine Learning-Based Routing Technique for Off-chain Transactions in Payment Channel Networks 5

A Machine Learning-Based Routing Technique for Off-chain Tra...

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5th IEEE International Conference on Smart Internet of Things, SmartIoT 2021

作者： Kadry, Heba Gadallah, Yasser Robotics Control and Smart Systems Program American University in Cairo New Cairo11835 Egypt Department of Electronics and Communications Engineering American University in Cairo New Cairo11835 Egypt

ISBN: (纸本)9781665445115

Blockchain is a foundational technology that has the potential to create new prospects for our economic and social systems. However, the lack of scalability limits the capability to deliver a target throughput and latency compared to the traditional financial systems. Layer-two is a collective term for solutions designed to enhance scalability by handling transactions off the main chain. For example, bidirectional payment channels allow the execution of fast transactions between two parties, thus forming the so-called payment channel networks (PCNs). Therefore, efficient routing protocols are needed to find a payment path from the sender to the receiver, with the lowest transaction fees. These protocols have to consider, among other factors, the unexpected online/offline behavior of the constituent payment nodes and the payment channel imbalance. This study proposes a novel machine learning-based technique for off-chain transaction routing. It is a fully distributed approach to be used within PCNs. For this purpose, the effect of the offline nodes and channel imbalance on the payment channels network are modeled. The simulation results demonstrate that the proposed technique strikes a good balance when considering the success ratio, transaction fees, routing efficiency, transaction overhead, and transaction maintenance overhead compared to other techniques that have previously been proposed for the same purpose. © 2021 IEEE.

关键词： Scalability

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Comparative Study on Fall Detection using Machine Learning Approaches 19

Comparative Study on Fall Detection using Machine Learning A...

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19th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Tongskulroongruang, Thienrawit Wiphunawat, Paphatchaya Jutharee, Wisanu Kaewmahanin, Watchara Rassameecharoenchai, Teerameth Jennawasin, Tanagorn Kaewkamnerdpong, Boonserm King Mongkut's University of Technology Thonburi Department of Control Systems and Instrumentation Engineering Bangkok Thailand Institute of Field Robotics and Automation King Mongkut's University of Technology Thonburi Bangkok Thailand Naresuan University Faculty of Allied Health Sciences Department of Cardiothoracic Technology Phitsanulok Thailand King Mongkut's University of Technology Thonburi Biological Engineering Program Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

Falls are one of the most dangerous problems for the elderly. A reliable fall detection system can aid in reducing the harmful repercussions of an unintentional fall. The focus of this paper is on a dataset that includes signals from portable devices. We propose a simple feature selection approach to reduce the number of effective input attributes based on a voting strategy. The dataset is classified into three categories, i.e., pre-fall, fall and post-fall using various machine learning approaches. The computational time is significantly reduced for most of the classification algorithms, and the relative reduction reaches to 66.67% with decision tree algorithm. Classification accuracy can reach as high as 95.17% when using neural network, while the relative reductions on the classification accuracy due to the feature selection do not exceed 1.50%. © 2022 IEEE.

关键词： Decision trees

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Computational Analysis of Mechanical Interactions between a Soft Robotic Device and a Skin-Muscle Phantom for Mechanotherapy

Computational Analysis of Mechanical Interactions between a ...

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

作者： Victor Ticllacuri Renato Mio Biomedical Engineering Program PUCP-UPCH Lima Perú Laboratory of Biomechanics and Applied Robotics Pontificia Universidad Catolica del Peru Lima Perú Laboratory of Biomedical Engineering Universidad Peruana Cayetano Heredia Lima Perú Chair of Digital Health With a Focus on Data Science University of Bayreuth Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Studying the soft robot-tissue mechanical interaction in muscle stimulation devices poses a significant challenge due to the complex behavior of the materials involved. To advance this field, this paper models computationally three types of soft elastomeric actuators designed to perform deep cyclic compression stimuli on human soft tissues for muscle rehabilitation by mechanotherapy. The analysis focuses on the interaction between a phantom representing transversely isotropic muscle and homogeneous skin, with a soft robotic device comprised of a hyperelastic actuator and a rigid support. Results from deformation, stress-strain and surface pressure analysis demonstrate efficient actuation, suggesting deep and focused stimulation on the muscle, while actuators exhibit reliable safety factors and load distribution, implying longer operational life. This lightweight and compact soft robotic device is suitable for integration into a wearable suit for targeted muscle groups stimulation in the lower limbs. Furthermore, this computational approach represents a significant advance in the biomechanical study of soft robot-human tissues interaction, with potential for generalization in similar biomedical device applications. Keywords—Soft robotics, Mechanotherapy, Transversely Isotropic Muscle, Human-Robot interaction.

关键词： Analytical models Actuators Computational modeling Biological system modeling Biological tissues Phantoms Muscles Soft robotics Skin Load modeling

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Automatic Facial Asymmetry Analysis for Elderly Stroke Detection by using Cosine Similarity 19

Automatic Facial Asymmetry Analysis for Elderly Stroke Detec...

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19th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Kaewmahanin, Watchara Rassameecharoenchai, Teerameth Jutharee, Wisanu Tongskulroongruang, Thienrawit Wiphunawat, Paphatchaya Jennawasin, Tanagorn Kaewkamnerdpong, Boonserm Naresuan University Faculty of Allied Health Sciences Department of Cardiothoracic Technology Phitsanulok Thailand King Mongkut's University of Technology Thonburi Institute of Field Robotics and Automation Bangkok Thailand Mongkut's University of Technology Thonburi Department of Control Systems and Instrumentation Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Biological Engineering Program Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

For acute stroke patients, time to recognize the stroke symptom onset is crucial for the lifesaving treatment. The automatic detection of stroke signs has been increasingly developed for practical use. The better timely classification results could allow better detection of the symptoms and avoid morbidity and mortality. In this study, we proposed using cosine similarity between the left and right sides of the face as a part of features for classifying the stroke elderly patients from healthy elderly participants. We employed our method on images from the Toronto NeuroFace dataset. The results showed high classification accuracy (the average accuracy of 97.8998% from 6 classification methods). These results indicate that the cosine similarity features can be useful to detect facial asymmetry and pose very promising potential for elderly stroke detection. © 2022 IEEE.

关键词： Patient treatment

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Mathematical Modeling and Characterization of a Wearable Soft Robotic Device for Muscle Mechanotherapy

Mathematical Modeling and Characterization of a Wearable Sof...

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

作者： Victor Ticllacuri Renato Mio Biomedical Engineering Program PUCP-UPCH Lima Perú Laboratory of Biomechanics and Applied Robotics Pontificia Universidad Catolica del Peru Lima Perú Laboratory of Biomedical Engineering Universidad Peruana Cayetano Heredia Lima Perú Chair of Digital Health with a Focus on Data Science University of Bayreuth Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Treatments of disuse-induced muscle atrophy entail unmet clinical needs due to the lack of medical devices capable of mimicking physicians manual therapies. Therefore, in this paper we develop and model a wearable soft pneumatic elastomeric actuator to perform deep cyclic compression stimuli on human soft tissues for muscle rehabilitation by mechanotherapy. Static and dynamic characterization of the prototype demonstrate a 2.5 mm active deformation at 100 kPa with 600 mm 3 /s and a 5 Hz bandwidth. We estimate the transfer function of the experimentally acquired pressure, flow and deformation signals, processed by a Gaussian kernel-based approach. Our mathematical model accurately describes the actuator behavior and enables to extract its mechanical parameters. Then, through computational simulations, we illustrate its efficacy in emulating multiple complex bio-inspired movements. Our proposed methodology aims to improve the control efficiency in wearable soft robotics for muscle atrophy treatment.

关键词： Atrophy Actuators Deformation Computational modeling Biological system modeling Transfer functions Prototypes Muscles Soft robotics Mathematical models

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Recognition of Real-time Traffic Signals Using the Algorithm Fusion of On-board Graphic Chip-based Machine Learning and Deep Learning

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Journal of Institute of Control, robotics and Systems 2022年第2期28卷 88-94页

作者： Lee, Jaewook Baek, Seungbeom Choi, Eunji Moon, Heechang Industrial Convergence Interdepartmental Program Autonomous Vehicle·Intelligent Robotics Hongik University Korea Republic of Mechanical and System Design Engineering Hongik University Korea Republic of

The recognition of traffic light in unmanned autonomous driving is one of the key functions. In general, to achieve this goal, deep learning and image processing technology are mainly used in autonomous driving industry. Both technologies have high recognition rates alone, but misrecognition can occur because of interference of the environment. misrecognitions are considered important problem because it may lead to big accidents. In this paper, we suggest using machine learning based on LabVIEW and deep running grounded in Python to increase recognition rate at intersection. By using both technologies, we can minimize the impact of the environment on traffic light recognition and reduce misrecognition. © ICROS 2022.

关键词： Image processing

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Active Telepresence Assistance for Supervisory Control: A User Study with a Multi-Camera Tele-Nursing Robot

Active Telepresence Assistance for Supervisory Control: A Us...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Alexandra Valiton Hannah Baez Naomi Harrison Justine Roy Zhi Li Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA

Supervisory control of a humanoid robot in a manipulation task requires coordination of remote perception with robot action, which becomes more demanding with multiple moving cameras available for task supervision. We explore the use of autonomous camera control and selection to reduce operator workload and improve task performance in a supervisory control task. We design a novel approach to autonomous camera selection and control, and evaluate the approach in a user study which revealed that autonomous camera control does improve task performance and operator experience, but autonomous camera selection requires further investigation to benefit the operator’s confidence and maintain trust in the robot autonomy.

关键词： Telepresence Automation Robot kinematics Conferences Robot vision systems Humanoid robots Supervisory control

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