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Emergency Clinical Procedure Detection via Wearable Sensors

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2022年第1期66卷 2239 - 2243页

作者： Prakash Baskaran Jamison Heard Julie A. Adams Collaborative Robotics and Intelligent Systems Institute Oregon State University Corvallis OR Department of Electrical and Microelectronic Engineering Rochester Institute of Technology NY

Communicating a patient’s state accurately during transfer from emergency medical technicians to hospital personnel is crucial for optimal care. Prior work demonstrated automated algorithms that combined wearable sensors with video data from cameras to detect clinical procedures and improve this information transfer. However, incorporating video requires task-or environment-specific installation mechanisms, raises privacy concerns, and is susceptible to occlusion and image noise. The presented approach detects clinical procedures using wearable sensors (i.e., inertial and electrophysiological) only and the procedures’ subtasks to mitigate the sensors’ signal variability to provide clinical procedure detection.

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Self-Healing First-Order Distributed Optimization with Packet Loss

arXiv

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arXiv 2023年

作者： Donato Ridgley, Israel L. Freeman, Randy A. Lynch, Kevin M. Northwestern University EvanstonIL60208 United States Department of Electrical & Computer Engineering Department of Mechanical Engineering Northwestern Institute on Complex Systems United States Center for Robotics and Biosystems United States

We describe SH-SVL, a parameterized family of first-order distributed optimization algorithms that enable a network of agents to collaboratively calculate a decision variable that minimizes the sum of cost functions at each agent. These algorithms are self-healing in that their convergence to the correct optimizer can be guaranteed even if they are initialized randomly, agents join or leave the network, or local cost functions change. We also present simulation evidence that our algorithms are self-healing in the case of dropped communication packets. Our algorithms are the first single-Laplacian methods for distributed convex optimization to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods. © 2023, CC BY-SA.

关键词： Cost functions

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Renderable Street View Map-Based Localization: Leveraging 3D Gaussian Splatting for Street-Level Positioning

Renderable Street View Map-Based Localization: Leveraging 3D...

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

作者： Howoong Jun Hyeonwoo Yu Songhwai Oh Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of Department of Intelligent Robotics & Mechanical Engineering Sungkyunkwan University (SKKU) Suwon-si Korea Republic of Department of Electrical and Computer Engineering (ECE) & Interdisciplinary Program in Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a new method that first utilizes 3D Gaussian splatting in street-level localization problem. Robust localization with street-level real-world images such as street view is a major issue for autonomous vehicle, augmented reality (AR) navigation, and outdoor mobile robots. The objective is to determine the position and orientation of a query image that matches a street view database composed of RGB images. However, given the limited information available in the street view images, accurately determining the location solely based on this data presents a significant challenge. To address this challenge, we propose a novel method called renderable street view map-based localization (RSM-Loc). This approach enhances the localization process by augmenting 2D street view images into a renderable 3D map using 3D Gaussian splatting, to resolve street-level localization problems. Upon receiving a query RGB image without geometry information, the proposed method renders 2D images from a pre-made renderable map and compares image pose similarities between the rendered images and the query image. Through iterations of this process, the proposed method eventually estimates the pose of the given query image. The experimental results demonstrate that RSM-Loc outperforms the baselines with neural-field-based localization. Additionally, we conduct deep analysis on the proposed method to show that our method can serve as a new concept for the street-level localization problem.

关键词： Location awareness Visualization Matched filters Three-dimensional displays Image resolution Navigation Noise measurement Mobile robots Intelligent robots Image reconstruction

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A More Accurate FEA Model for Machines with Segmented Stators Manufactured using Oriented Steel

A More Accurate FEA Model for Machines with Segmented Stator...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Anmol Aggarwal Bhuvan Khoshoo John Agapiou Shanelle N. Foster General Motors Pontiac MI USA Department of Electrical & Computer Engineering Michigan State University East Lansing MI USA Retired Manufacturing Systems Research Laboratory General Motors Warren MI USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Using oriented steel for segmented stator construction in AC electric machine can improve the efficiency of machine by decreasing core losses compared to a machine designed with non-oriented steel. Therefore, analysis of the oriented steel stator in AC electric machines in finite analysis is of great interest. Several attempts were made to accurately simulate oriented steel in segmented stators; however, the insight into the flux distribution and consequently the perceived properties of the oriented steel is at-large missing. In this work, a physics-based approach to model oriented steel for radial flux AC machines is developed to accurately capture the magnetic properties by considering the direction of flux. The determined magnetic properties are used to model an equivalent isotropic model in FEA. The accuracy of developed FEA model is validated experimentally.

关键词： Stator cores Accuracy Correlation AC machines Core loss Energy conversion Finite element analysis Steel Magnetic properties

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Cyber security risk in pervasive environments of unobtrusive cECG-monitoring system during driving

Cyber security risk in pervasive environments of unobtrusive...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Milos Stanojevic Gordana Ostojic Tamara Skoric Natasa Popovic Srdjan Lale Nikola Kukric Department of Industrial Engineering and Management Faculty of Technical Sciences Center for Identification Technologies Novi Sad Serbia Department of Power Electronic and Telecommunication Engineering Faculty of Technical Sciences Novi Sad Serbia Department of Automation and Robotics Faculty of Electrical Engineering University of East Sarajevo East Sarajevo Bosnia and Herzegovina Department of Electronics and Electonic Systems Faculty of Electrical Engineering University of East Sarajevo East Sarajevo Bosnia and Herzegovina

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

The development of pervasive healthcare systems in the automotive industry is enabled by unobtrusive acquisition systems for recording vital parameters of drivers and developed ML algorithms for driver health services. The paper proposes a pervasive system based on the acquisition of the cECG signal recorded through clothing during driving, the transmission of the cECG to the local device for processing cECG and extraction of cECG parameters, and the extracted parameters transmission via vehicle-to-infrastructure (V2I) communication channel to the cloud. The potential of cyber security risk in implementing the proposed system is identified.

关键词： Industries Vehicle-to-infrastructure Clothing Medical services Communication channels Recording Computer crime Vehicles Automotive engineering

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Fast yet predictable braking manoeuvers for real-time robot control

arXiv

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arXiv 2023年

作者： Hamad, Mazin Gutierrez-Moreno, Jesus Kussaba, Hugo T.M. Mansfeld, Nico Abdolshah, Saeed Swikir, Abdalla Burgard, Wolfram Haddadin, Sami Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Franka Emika GmbH Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University Libya Department of Engineering University of Technology Nuremberg Germany Germany

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings. © 2023, CC BY-NC-ND.

关键词： Braking

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Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible Coupling Suction Disc Design for Adhesion Enhancement 16th

Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Tan, Wenjun Qin, Hengshen Zhang, Chuang Wang, Ruiqian Zhang, Yiwei Yang, Lianchao Chen, Qin Wang, Feifei Xi, Ning Liu, Lianqing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chengdu Institute of Biology Chinese Academy of Sciences Sichuan Chengdu610042 China Department of Electrical and Electronic Engineering University of Hong Kong 999077 Hong Kong Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (纸本)9789819964888

Underwater high-performance biological adhesion systems have attracted increasing research interest to inspire engineered adhesion systems. Switchable underwater adhesion systems typically employ suction adhesion. However, achieving low pre-pressure negative pressure adhesion is still a challenge. An inspiring nature mode is provided by the rock-climbing fish, which needs little preload and can resist a pull-off force of 1000 times its own body weight. The pectoral and abdominal fins of rock-climbing fish have evolved to almost a flat suction cup. Through micro CT, we found that a unique skeletal network is embedded in the suction cup. This unique structure increases the stiffness of the suction cup and enhances its adhesion performance. Through numerical simulation, it was revealed that this rigid, flexible coupling structure enhances the ability of the suction cup to resist lateral pressure and increases the energy dissipation of the edge detachment of the suction cup. This work can shed light on the design of novel underwater adhesion systems and inspire underwater adhesion crawling robots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Adhesion

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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic systems over a Lossy Communication Channel

引用

Journal of systems Science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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Bilateral-Head Region-Based Convolutional Neural Networks: A Unified Approach for Incremental Few-Shot Object Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4376-4390页

作者： Li, Yiting Zhu, Haiyue Tian, Sichao Ma, Jun Xiang, Cheng Vadakkepat, Prahlad National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research 138634 Singapore Peking Union Medical College Institute of Medicinal Plant Development Chinese Academy of Medical Sciences Beijing100193 China Robotics and Autonomous Systems Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong SAR Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen Futian518045 China

Practical object detection systems are highly desired to be open-ended for learning on frequently evolved datasets. Moreover, learning with little supervision further adds flexibility for real-world applications such as autonomous driving and robotics, where large-scale datasets could be prohibitive or expensive to obtain. However, continual adaption with small training examples often results in catastrophic forgetting and dramatic overfitting. To address such issues, a compositional learning system is proposed to enable effective incremental object detection from nonstationary and few-shot data streams. First of all, a novel bilateral-head framework is proposed to decouple the representation learning of base (pretrained) and novel (few-shot) classes into separate embedding spaces, which takes care of novel concept integration and base knowledge retention simultaneously. Moreover, to enhance learning stability, a robust parameter updating rule, i.e., recall and progress mechanism, is carried out to constrain the optimization trajectory of sequential model adaption. Beyond that, to enforce intertask class discrimination with little memory burden, we present a between-class regularization method that expands the decision space of few-shot classes for constructing unbiased feature representation. Final, we deeply investigate the incomplete annotation issue considering the realistic scenario of incremental few-shot object detection (iFSOD) and propose a semisupervised object labeling mechanism to accurately recover the missing annotations for previously encountered classes, which further enhances the robustness of the target detector to counteract catastrophic forgetting. Extensive experiments conducted on both Pascal visual object classes dataset (VOC) and microsoft common objects in context dataset (MS-COCO) datasets demonstrate the effectiveness of our method. © 2020 IEEE.

关键词： Feature extraction

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Cyber security risk in pervasive environments of unobtrusive cECG-monitoring system during driving 24

Cyber security risk in pervasive environments of unobtrusive...

引用

24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Stanojevic, Milos Ostojic, Gordana Skoric, Tamara Popovic, Natasa Lale, Srdjan Kukric, Nikola Center for Identification Technologies Faculty of Technical Sciences Department of Industrial Engineering and Management Novi Sad Serbia Faculty of Technical Sciences Department of Power Electronic and Telecommunication Engineering Novi Sad Serbia University of East Sarajevo Faculty of Electrical Engineering Department of Automation and Robotics East Sarajevo Bosnia and Herzegovina University of East Sarajevo Faculty of Electrical Engineering Department of Electronics and Electonic Systems East Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798331515799

关键词： Cyber attacks

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