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BLOCK MEDCARE: ADVANCING HEALTHCARE THROUGH BLOCKCHAIN INTEGRATION

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arXiv 2024年

作者： Simonoski, Oliver Bogatinoska, Dijana Capeska Faculty of Communication Networks and Security University of Information Science and Technology St. Paul the Apostle Ohrid6000 Macedonia Faculty of Applied IT Machine Intelligence and Robotics University of Information Science and Technology St. Paul the Apostle Ohrid6000 Macedonia

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as

关键词： Electronic health record

Block MedCare: Advancing healthcare through blockchain integration with AI and IoT

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arXiv 2024年

This research explores the integration of blockchain technology in healthcare, focusing on enhancing the security and efficiency of Electronic Health Record (EHR) management. We propose a novel Ethereum-based system that empowers patients with secure control over their medical data. Our approach addresses key challenges in healthcare blockchain implementation, including scalability, privacy, and regulatory compliance. The system incorporates digital signatures, Role-Based Access Control, and a multi-layered architecture to ensure secure, controlled access. We developed a decentralized application (dApp) with user-friendly interfaces for patients, doctors, and administrators, demonstrating the practical application of our solution. A survey among healthcare professionals and IT experts revealed strong interest in blockchain adoption, while also highlighting concerns about integration costs. The study explores future enhancements, including integration with IoT devices and AI-driven analytics, contributing to the evolution of secure, efficient, and interoperable healthcare systems that leverage cutting-edge technologies for improved patient care. © 2024, CC BY.

关键词： Electronic health record

Safe Execution of Learned Orientation Skills with Conic Control Barrier Functions

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Safe Execution of Learned Orientation Skills with Conic Cont...

IEEE International Conference on robotics and Automation (ICRA)

作者： Zheng Shen Matteo Saveriano Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany Department of Industrial Engineering (DII) University of Trento Trento Italy Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Bilbao Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the field of Learning from Demonstration (LfD), Dynamical Systems (DSs) have gained significant attention due to their ability to generate real-time motions and reach predefined targets. However, the conventional convergence-centric behavior exhibited by DSs may fall short in safety-critical tasks, specifically, those requiring precise replication of demonstrated trajectories or strict adherence to constrained regions even in the presence of perturbations or human intervention. Moreover, existing DS research often assumes demonstrations solely in Euclidean space, overlooking the crucial aspect of orientation in various applications. To alleviate these shortcomings, we present an innovative approach geared toward ensuring the safe execution of learned orientation skills within constrained regions surrounding a reference trajectory. This involves learning a stable DS on SO(3), extracting time-varying conic constraints from the variability observed in expert demonstrations, and bounding the evolution of the DS with Conic Control Barrier Function (CCBF) to fulfill the constraints. We validated our approach through extensive evaluation in simulation and showcased its effectiveness for a cutting skill in the context of assisted teleoperation.

关键词： Learning systems Perturbation methods Real-time systems Trajectory Behavioral sciences Usability Task analysis

Intelligent Vision-Assisted Obstacle Avoidance and Unknown Seasonal Products Navigation in Farmer's Market for the Visually Impaired

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SSRN

SSRN 2023年

作者： Liu, Mei Chen, Yunhua Djoulde, Aristide He, Mengfan Rao, Jinjun Giernacki, Wojciech Wang, Zhiming Chen, Jinbo Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

Abstract: Shopping at a farmer's market is a considerable daily challenge for the visually impaired. General indoor obstacle avoidance and navigation algorithms are not fully applicable to the crowded and dynamic shopping environment of farmer's market. To overcome these challenges and provide precise assistance to visually impaired, this paper proposes an intelligent vision-assisted (IVA) algorithm for navigating narrow and crowded farmer's market aisles. The algorithm employs a pedestrian detection camera based on deep learning as the hardware to intelligently detect the type and number of pedestrians in front, and guides a navigation robot to adopt corresponding obstacle avoidance strategies in time according to the detection results. Different from general obstacle avoidance methods, IVA doesn't have issues of guiding robot to make frequent turns and change routes when avoiding obstacles in a crowded and narrow environment, making it more suitable for the visually impaired. Additionally, this paper proposes an unknown seasonal product navigation (USPN) algorithm for robots to automatically search for navigation information that has changed. When used in conjunction with a camera driven by deep learning, USPN successfully guided a navigation robot to identify the names and coordinates of newly listed products. Compared with general navigation algorithms, USPN can guide robots to update product information timely and intelligently. Experimental tests conducted in a farmer's market demonstrated that IVA is more intelligent and efficient than the dynamic window approach (DWA) in obstacle avoidance performance, and it is more suitable for guiding the visually impaired;furthermore, USPN successfully guides the robot to automatically acquire products information that changes with the seasons, and the acquired product categories and locations are precise. Particularly, IVA and USPN can be used as references when designing guide robots for use in public spaces like super

关键词： Navigation

Preliminary Validation of an Immersive Virtual Reality Framework for Prosthetic Embodiment Quantification

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Preliminary Validation of an Immersive Virtual Reality Frame...

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

作者： Johanna Happold Diogo Morais Patricia Capsi-Morales Cristina Piazza School of Computation Information and Technology Technical Univer-sity of Munich (TUM) Munich Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Munich Germany Faculty of Science University of Lisbon Lisbon Portugal

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

This work introduces an immersive virtual reality framework designed for the assessment of both functionality and sense of embodiment of myoelectric upper limb prostheses. The framework features separate training and testing environments in which participants are asked to grasp and release objects with different dimensions controlling a virtual hand with eight sEMG sensors. In the testing session, we introduced a new metric for objective quantification of embodiment based on the shift in users' attention. We evaluated the proposed methodology with ten able-bodied participants and one par-ticipant with limb loss, collecting pre- and post-training data within multiple sessions. Results suggest a similar trend of the proposed virtual reality assessment method and standard questionnaire-based measures of embodiment for able-bodied participants. Moreover, we found a positive correlation between functionality and subjective embodiment highlighting the need for appropriate functional training. Future investigations will include longitudinal studies exploring the integration of our virtual reality framework in real-world rehabilitation settings.

关键词： Training Correlation Virtual reality Sensor phenomena and characterization Particle measurements Market research Standards Robots Testing Prosthetics

Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping

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arXiv 2025年

作者： Wang, Dong Haag, Hannes Herraez, Daniel Casado May, Stefan Stachniss, Cyrill Nüchter, Andreas Informatics XVII – Robotics Julius-Maximilians-Universität Würzburg Germany Faculty of Electrical Engineering Precision Engineering Information Technology Nuremberg Institute of Technology Georg Simon Ohm Germany CARIAD SE University of Bonn Germany Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Simultaneous localization and mapping (SLAM) is a critical capability for autonomous systems. Traditional SLAM approaches, which often rely on visual or LiDAR sensors, face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a novel Doppler-aided radar-inertial and LiDAR-inertial SLAM framework that leverages the complementary strengths of 4D radar, FMCW LiDAR, and inertial measurement units. Our system integrates Doppler velocity measurements and spatial data into a tightly-coupled front-end and graph optimization back-end to provide enhanced ego velocity estimation, accurate odometry, and robust mapping. We also introduce a Doppler-based scan-matching technique to improve front-end odometry in dynamic environments. In addition, our framework incorporates an innovative online extrinsic calibration mechanism, utilizing Doppler velocity and loop closure to dynamically maintain sensor alignment. Extensive evaluations on both public and proprietary datasets show that our system significantly outperforms state-of-the-art radar-SLAM and LiDAR-SLAM frameworks in terms of accuracy and robustness. To encourage further research, the code of our Doppler-SLAM and our dataset are available at: https://***/Wayne-DWA/Doppler-SLAM. © 2025, CC BY-NC-ND.

关键词： Mapping

CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

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CITR: A Coordinate-Invariant Task Representation for Robotic...

IEEE International Conference on robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： Learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

Passive Realizations of Series Elastic Actuation: Effects of Plant and Controller Dynamics on Haptic Rendering Performance

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arXiv 2022年

作者： Kenanoglu, Celal Umut Patoglu, Volkan Munich Institute of Robotics and Machine Intelligence Technical University of Munich 80992 Germany Faculty of Engineering and Natural Sciences Sabanci University Istanbul34956 Turkey

We introduce minimal passive physical equivalents of series (damped) elastic actuation (S(D)EA) under closed-loop control to determine the effect of different plant parameters and controller gains on the closed-loop performance of the system and to help establish an intuitive understanding of the passivity bounds. Furthermore, we explicitly derive the feasibility conditions for these passive physical equivalents and compare them to the necessary and sufficient conditions for the passivity of S(D)EA under velocity sourced impedance control (VSIC) to establish their relationship. Through the passive physical equivalents, we rigorously compare the effect of different plant dynamics (e.g., SEA and SDEA) on the system performance. We demonstrate that passive physical equivalents make the effect of controller gains explicit and establish a natural means for effective impedance analysis. We also show that passive physical equivalents promote co-design thinking by enforcing simultaneous and unbiased consideration of (possibly negative) controller gains and plant parameters. We demonstrate the usefulness of negative controller gains when coupled to properly designed plant dynamics. Finally, we provide experimental validations of our theoretical results and characterizations of the haptic rendering performance of S(D)EA under VSIC. © 2022, CC BY-NC-ND.

关键词： Controllers

Functional Mode Switching for Safe and Efficient Human-Robot Interaction

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Functional Mode Switching for Safe and Efficient Human-Robot...

IEEE-RAS International Conference on Humanoid Robots

作者： Petr Svarny Mazin Hamad Alexander Kurdas Matej Hoffmann Saeed Abdolshah Sami Haddadin Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Engineering Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence School of Computation Information and Technology Technische Universität München (TUM)

ISBN: (纸本)9798350309805

Various approaches can ascertain the safety and efficiency of physical human-robot collaboration. This paper presents the concept of robot functional mode switching to efficiently ensure human safety during collaborative tasks based on biomechanical pain and injury data and task information. Besides the robot's reflected inertial properties summarizing its impact dynamics, our concept also integrates safe and smooth velocity shaping that respects human partner motion, interaction type, and task knowledge. We further discuss different approaches to safely shape the robot velocity without sacrificing the overall task execution time and motion smoothness. The experimental results showed that our proposed approaches could decrease jerk level during functional mode switching and limit the impact of safety measures on productivity, especially when guided with additional task knowledge.

关键词： Robot motion Service robots Human-robot interaction Humanoid robots Switches Interference Safety