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Latin American Electron Devices Conference (LAEDC)

作者： A.X. González-Cely C.F. Blanco-Díaz Díaz Camilo A.R. T Bastos-Filho Telecommunications Laboratory Graduate Program in Electrical Engineering Federal University of Espírito Santo Av. Fernando Ferrari 514-Goiabeiras Vitoria Brazil Robotics and Assistive Technology Laboratory Graduate Program in Electrical Engineering Federal University of Espírito Santo Av. Fernando Ferrari 514-Goiabeiras Vitoria Brazil

The design of electrical wheelchair controllers remains a challenge for the scientific community. In recent years, Polymer Optical Fiber (POF)-based sensors have been used for biomedical applications, however, the application of these types of sensors for the control of assistive devices has been little explored. For this reason, this work proposes the design and validation of a fuzzy logic controller that uses information from an array of POF-based sensors located in a pillow, which is used for wheelchair control in four directions (right, left, forward and stop) by using neck movements. The results allow concluding that the system is fast, accurate, and reliable. This work presents a contribution related to the design of assistive devices based on optical fiber sensors and focuses on people with upper- and lower-limb mobility restrictions. Future studies will address the evaluation of the system for people with disabilities.

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Roborueda: Python-Based GUI to Control a Wheelchair and Monitor User Posture

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SSRN 2023年

作者： Gonzalez-Cely, Aura Ximena Blanco-Diaz, Cristian Felipe Diaz, Camilo A.R. Bastos-Filho, Teodiano Freire Robotics and Assistive Technology Laboratory Graduate Program in Electrical Engineering Federal University of Espirito Santo Av. Fernando Ferrari 514 Espirito Santo Vitoria29075-910 Brazil Telecommunications Laboratory Graduate Program in Electrical Engineering Federal University of Espirito Santo Av. Fernando Ferrari 514 Espirito Santo Vitoria29075-910 Brazil

The increasing development of Optical Fiber Sensors (OFS) and inertial sensors applied into the health area. The use of neck and/or head motion for people with physical impairments in their limbs allows achieving independence and improve their mobility. This paper addresses a gap based on the development of Human-Machine Interfaces by introducing Roborueda, a robust Graphical User Interface (GUI) designed to facilitate interaction of electric wheelchair users, with a primary focus on enhancing mobility and preventing future illnesses. The main objective of the system is to monitor the body posture and control the wheelchair using neck and head motion, through the GUI. The interface incorporates machine learning and fuzzy logic techniques and a robust communication system using the Python programming language. The GUI was developed using PyQt, and data exchange between Python libraries was facilitated through dataframes and multithreading. Furthermore, the classification model was loaded, while Wi-Fi communication employ sockets and User Datagram Protocol. The user-friendly GUI offers a range of functionalities, including the ability to adjust controllers, and gather system information, among others. The implementation of this interface marks a significant step towards integrating OFS, which is a contribution to the advancement of assistive technologies. To ensure the effectiveness and practicality of Roborueda, future work will focus on validating the system through extensive testing with wheelchair users, thereby obtaining valuable feedback and making further improvements. © 2023, The Authors. All rights reserved.

关键词： Fuzzy logic

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Stress Classification Using a Low-Cost Optical Fiber Physiological Sensor: A Preliminary Study

Stress Classification Using a Low-Cost Optical Fiber Physiol...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： María Gaitán-Padilla Marcela Múnera Carlos A. Cifuentes Maxwell E. Monteiro Maria J. Pontes Camilo A. R. Diaz Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Bristol Robotics Laboratory University of the West of England Bristol UK Instituto Federal do Espirito Santo (IFES) Serra Brazil

The classification and measurement of stress through physiological sensors have been studied because of their high impact on mental health and well-being. Low-cost optical fiber wearable sensors, can monitor signals and be adaptive systems. Therefore, the present study sought to evaluate a lowcost optical fiber physiological sensor developed for stress classification while monitoring physiological signals. Five volunteers participated in a stress-induced test, where pulse and respiratory measurements were correctly performed with the optical fiber sensor during both stress and relaxation. The data obtained were used to find the best machine-learning classification algorithm for stress detection: a Bagged Decision Tree Classifier with an accuracy of 88%, and a precision of 91%. Thus, the proposed sensor can effectively monitor pulse and respiratory signals for stress classification. Based on these results, a study with more volunteers will be performed.

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Comparative Study Between Unscented Kalman Filter and Multi-Layer Perceptron Applied in an Electric Vehicle Simulation with Pack Parameters Generated from the Database

Comparative Study Between Unscented Kalman Filter and Multi-...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Kawe Monteiro de Souza José Rodolfo Galvão Jorge Augusto Pessatto Mondadori Fernanda Cristina Corrêa Paulo Broniera Junior Maria Bernadete de Morais França Dept. Postgraduate Degree in Electrical Engineering State University of Londrina (UEL) Londrina Brazil Graduate Program in Electrical Engineering (PPGEE) Federal University of Technology (UTFPR) Ponta Grossa Brazil Laboratory LAPSEE-PIM Senai Institute of Inf. and Communication Technology (ISTIC) Londrina Brazil

In electric vehicles (EVs), monitoring battery cells ensures safety and longevity. The Battery Management System (BMS) is responsible for this task, using variables such as State of Charge (SoC). This paper presents a comparative study between the Unscented Kalman Filter (UFK) and Multilayer Perceptron Neural Network (PMC) methods applied to a pack using a simulation of a vehicle under the FTP-75 cycle with reference SoC using Coulomb Counting (CC). The Neural Network performed better with an RMSE value of 0.418 compared to 0.0593 for UFK.

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Fiber-Optic and IMU Sensors for Muscle Fatigue Detection in Work Settings

Fiber-Optic and IMU Sensors for Muscle Fatigue Detection in ...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Sophia Otálora Marcelo Eduardo Vieira Segatto Maxwell E. Monteiro Carlos A. Cifuentes Camilo A.R. Diaz Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Campus Serra Instituto Federal do Espirito Santo (IFES) Serra Brazil Bristol Robotics Laboratory University of the West of England Bristol UK

Detection of muscle fatigue is essential in industrial environments to prevent work-related disorders and improve worker safety. Also, measuring elbow position is essential in human motion analysis. This paper proposes a novel approach for detecting muscle fatigue using an Optical Fiber (OF) sensor and Inertial Measurement Unit sensors (IMU). Ten healthy subjects performed a repetitive lifting activity using the IMU and OF sensor until reaching muscle fatigue. The study evaluates four machine learning algorithms, including Ensemble Classifiers (EC), Support Vector Machine (SVM), k Nearest Neighbour (kNN), and Neural Networks (NN). Results of detecting muscle fatigue using the OF and IMU sensors obtained an accuracy of 98.4%. This setup can be easily used by workers and be relevant to identify workers' fatigue during long working shifts.

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Electronic and Structural Insights into Boron Nitride Counterparts of Net-(Y,W)

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SSRN 2024年

作者： Lima, Kleuton Antunes Lopes dos Santos, Emanuel José Alexandrino de Mendonça, Fábio Lúcio Lopes da Silva, Daniel Alves Giozza, William Ferreira Ribeiro, Luiz Antonio Institute of Physics University of Brasília Brasília70910-900 Brazil Computational Materials Laboratory LCCMat Institute of Physics University of Brasília Brasília70910-900 Brazil Department of Electrical Engineering Faculty of Technology University of Brasília Brasília Brazil Professional Postgraduate Program in Electrical Engineering - PPEE University of Brasília Brasília Brazil

Computational materials play a crucial role in comprehending various material classes and their properties, offering valuable insights for predicting novel structures and complementing experimental approaches. Net-(C,Y,W) has emerged as stable two-dimensional carbon allotropes of 4-6-8 carbon rings within this framework. This study uses density functional theory calculations and ab initio molecular dynamics simulations to explore the structural, electronic, optical, and mechanical properties of their boron nitride counterparts, named NetBN-W and NetBN-Y, with NetBN-C already investigated. The electronic analysis reveals an insulating characteristic for NetBN-W and a potential wide band gap semiconducting nature for NetBN-Y, with values of 5.12 eV and 3.71 eV, respectively. Optical properties examination indicates UV activity for these materials, accompanied by optical anisotropy between the x and y directions. Evaluation of mechanical properties also uncovers significant anisotropy in both materials, with Young's moduli reaching 417.58 GPa for NetBN-W and 433.31 GPa for NetBN-Y when stretched in the y-direction of the basal plane. The nearly zero Young's modulus for NetBN-Y in the x-direction signifies its substantial instability under tension due to nitrogen bonds parallel to the strain direction. These findings offer valuable insights into the fundamental properties of NetBN-W and NetBN-Y and their potential applications. © 2024, The Authors. All rights reserved.

关键词： Density functional theory

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Power Consumption Optimization of GPU Server with Offline Reinforcement Learning

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IEEE Access 2025年 13卷 85826-85837页

作者： Chung, Heechan Im, Yeeun Park, Jongchan Lee, Taeho Kim, TaeYoung Kim, Hyungjun Lee, Dongwhan KT Research and Development Center AI Security Research Team Future Network Laboratory Network Group Seoul Korea Republic of KT Research and Development Center AI Perceived Quality Research Team Future Network Laboratory Network Group Seoul Korea Republic of Korea Advanced Institute of Science and Technology Robotics Program Daejeon Korea Republic of Korea Advanced Institute of Science and Technology School of Electrical Engineering Daejeon Korea Republic of

The rapid growth of artificial intelligence (AI) has led to increased reliance on power-intensive Graphics Processing Units (GPUs), which are essential for training and deploying large-scale models. However, the escalating energy demands of AI workloads pose sustainability challenges, necessitating efficient power management strategies to reduce carbon footprints. Optimizing GPU server power consumption is complex due to the interdependence of various components. Conventional methods often involve trade-offs: increasing fan speed enhances cooling but raises overall power usage, whereas lowering GPU clock frequencies conserves energy at the cost of longer computation times. To address these challenges, we propose a data-driven optimization framework based on offline reinforcement learning (RL). Our approach collects operational data from a custom-designed workload that simulates varying server loads, capturing key metrics such as power consumption, temperature, and core frequency. The reward function balances power efficiency with performance. The reinforcement learning agent learns from pre-collected server logs, enabling intelligent real-time GPU clock control decisions without costly live experiments. Additionally, periodic fan speed adjustments and pre-training of the Q-network further enhance overall efficiency. Experimental results demonstrate that our method reduces power consumption by 3.62% while improving computation time by 1.51% for synthetic workloads. For LLaMA-2 fine-tuning, power consumption decreases by 6.40% with only a minor 1.27% increase in computation time, demonstrating its practical effectiveness. Our framework was validated on the latest NVIDIA L40S GPU, demonstrating its compatibility with cutting-edge hardware. © 2013 IEEE.

关键词： Energy utilization

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Exploring Multimodal Gait Rehabilitation and Assistance through an Adaptable Robotic Platform

Exploring Multimodal Gait Rehabilitation and Assistance thro...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sophia Otálora Sergio D. Sierra M. Felipe Ballén-Moreno Marcela Múnera Carlos A. Cifuentes Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Bristol Robotics Laboratory University of the West of England Bristol UK Robotics & Multibody Mechanics (R&MM) Reasearch Group Vrije Universiteit Brussel Brussels Belgium Flanders Make Brussels Belgium Biomedical Engineering Program Colombian School of Engineering Julio Garavito Bogotá D.C. Colombia The School of Engineering Science and Technology Universidad del Rosario Bogotá D.C. Colombia

Lower-limb exoskeletons and smart walkers are robotic devices to assist patients in regaining their autonomy after a stroke. The integration of these devices enables gait rehabilitation and functional compensation, promoting natural over-ground walking. This article presents the Adaptable Robotic Platform for Gait Rehabilitation and Assistance (AGoRA V2 platform), which integrates the new AGoRA V2 Smart Walker and the AGoRA V2 unilateral lower-limb exoskeleton. It was evaluated with 14 healthy subjects using physiological and kinematic variables and a perception assessment. The study entailed four conditions: Without exoskeleton (WOE), With Exoskeleton (WE&T), With Walker (WW), and With Platform (WP). Results indicate a reduction in the muscle activity of the Rectus Femoris (18%) and Vastus Lateralis (15%), comparing WE&T and WP, as well as walking without any device (WOE) and using any robotic device (WE&T, WW, WP). Results suggest the importance of combining the exoskeleton with the robotic walker and the assistance of each device independently. Moreover, using the complete platform induces slower gait patterns than the walker, as the mean impulse force and linear velocity decrease by 42% and 44%, respectively. These results demonstrate that the platform contributes to safety, and improvements in gait parameters and muscular activity, indicating the system's potential to act as a modular device according to users' conditions and therapeutic goals.

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Development and Functional Evaluation of The PrHand V3 Soft-robotics Prosthetic Hand

Development and Functional Evaluation of The PrHand V3 Soft-...

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

作者： Orion Ramos Laura De Arco Marcela Múnera Jorge Robledo Mehran Moazen Helge Wurdemann Carlos A. Cifuentes School of Engineering Science and Technology Universidad del Rosario Bogota Colombia Graduate Program of Electrical Engineering Federal University of Espírito Santo Vitoria Brazil Bristol Robotics Laboratory University of the West of England Bristol UK Colombian School of Engineering Bogotá D.C. Colombia Prótesis Avanzadas SAS Medellin Colombia Department of Mechanical Engineering University College London London UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The affordability and functionality of hand prosthetics in developing countries are still very limited. This work aims to present and evaluate the new version of the PrHand affordable robotic prosthesis (PrHand V3), built with soft robotics and compliant mechanisms. PrHand V3 implements a new frictionless tendon unification system, the degree of freedom of thumb opposition was removed, and the finger flexion was improved to the previous version, PrHand V2. The study contributes by evaluating these mechanical changes and conducting the first functional assessment of PrHand V3 with an amputee user. The Anthropomorphic Hand Assessment Protocol (AHAP) dexterity test was the first evaluation in this work; it evaluated how the prosthesis performs eight different grips. PrHand V3 was compared with a PrHand V2 and a commercial robotic prosthesis A3D from Prótesis Avanzadas SAS. PrHand V3’s score on the AHAP test was 80%. This result is higher than the 69% obtained by the PrHand V2 and the 79% obtained by A3D. The Activities Measure for Upper Limb Amputees (AM-ULA) test was the second evaluation in this work; An A3D amputee user performed 23 Activities of Daily Living with PrHand V3 and an A3D. PrHand V3 obtained an average of 2.86/4 and A3D obtained an average of 2.96/4 without significant differences between the two tests. The soft actuation of PrHand V3 as an affordable prosthesis performs similarly to a commercial robotic prosthesis with the advantage of being more flexible to assist a trans-radial hand amputee.

关键词： Hands Protocols Manufacturing processes Thumb Grasping Soft robotics Synthetic aperture sonar Prosthetic hand Prosthetics Tendons

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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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