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European Conference on Mobile Robots (ECMR)

作者： Sedat Dogru Carlos A. Silva Lino Marques Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Smooth social navigation requires not only detection of the people around the robot, but also accurate localization of the people, a process difficult to achieve with a single sensing modality. Hence, literature has focused on various fusion approaches, such as RGB-D, ROI based lidar vision fusion, or Artificial Neural Network (ANN) based lidar vision fusion. However, monocular photogrammetry has always been ignored in the literature. In this work, we propose a fusion approach based on monocular positon estimation and lidar, and show the effectivenes of the approach both on a public dataset and a purpose built dataset with different cameras.

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Exploring Player's Body as a Game Controller for Fitness Activity Promotion

Exploring Player's Body as a Game Controller for Fitness Act...

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Experiment@ International Conference (exp.at)

作者： Diogo Simões Paulo Menezes Electrical and Computer Engineering Department University of Coimbra Institute of Systems and Robotics Coimbra Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

In recent years, there has been a tendency for people to resort to a more sedentary lifestyle, which has only been aggravated by the COVID-19 pandemic and the evermore common remote work policies. This lack of physical activity brings negative health outcomes and can generally be difficult to overcome due to how challenging it can be for a person to find the motivation to adopt a more physically active routine. To address this issue, we propose the use of serious games as an engaging, fun, and highly motivational tool that can aid people in their quest for a healthier lifestyle. We focus on three problem areas: motor control improvement and rehabilitation, post-stroke rehabilitation, and physical activity for general health. There exist multiple solutions for these problems which often require specific equipment that may not be available to the common user. Therefore, we propose a different approach requiring solely a laptop equipped with a camera, thus making it more practical and accessible to the wider public.

关键词： COVID-19 Motor drives Portable computers Pandemics Games Cameras Remote working

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MuSe4ASD - An Interactive Music Station for Children with ASD

MuSe4ASD - An Interactive Music Station for Children with AS...

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Experiment@ International Conference (exp.at)

作者： Beatriz Lagos Paulo Menezes Electrical and Computer Engineering Department University of Coimbra Institute of Systems and Robotics Coimbra Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

This article explores the topic involving children with autism, referencing what this condition is about, as well as the hypotheses for approaching the problem based on the literature reviewed. The proposed approach involves the creation of an adequate space with an immersive interactive system based on musical stimuli, and physical exercise to improve their communication, memory, visual and motor capabilities, and eventually potentiate the development of artistic skills. The development of this interactive system includes the development of an avatar that will be animated using Motion Capture technology, so it can interact with the child and accompany him/her while doing the exercises with fluid movements.

关键词： Visualization Autism Fluids Interactive systems Avatars Music Variable speed drives

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Impact of UAV Propellers on Gas Plume Tracking

Impact of UAV Propellers on Gas Plume Tracking

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European Conference on Mobile Robots (ECMR)

作者： Rui Baptista Hugo Magalhães Lino Marques Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Unmanned Aerial Vehicles (UAVs) are highly manoeuvrable platforms that are becoming popular to map and localise the source of chemical substances in the environment. However, their propellers disturb the environment and may seriously impact the measurements and the expected results in such applications. Some works have already addressed this problem, but only for specific cases and proposing different solutions about the best way to mitigate this problem. This work studies the impact of propellers for multiple sensor positions and 3 UAVs of different sizes, in an Odour Source Localisation (OSL) problem under field conditions and compares the results with a non-disturbing setup. The experiments show a significant impact on the measured signals from asymmetrical frames and from the motion of the agent, with the best results achieved from sensors acquiring data on intake air regions.

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Nonlinear Model Predictive Control for Optimal Dose Administration in Radiotherapy 15th

Nonlinear Model Predictive Control for Optimal Dose Administ...

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15th Advanced Doctoral Conference on Computing, electrical and Industrial systems, DoCEIS 2024

作者： Araújo, João C. G. Guerreiro, Bruno J. Oliveira, Luis B. da Silva, Filipe Ferreira CTS-UNINOVA and LASI Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica2829-516 Portugal Institute for Systems and Robotics LARSyS Lisbon1049-001 Portugal CEFITEC Department of Physics NOVA School of Science and Technology Caparica2829-516 Portugal

ISBN: (纸本)9783031638503

This work discusses the nonlinear modeling of healthy and cancer cell population growth under the influence of radiotherapy, and proposes predictive control techniques to determine the optimal dose for tumor control, while sparing surrounding healthy tissue. Radiation effects are modelled by a linear-quadratic formalism, coupled with a novel population-specific delayed dose response. Numerical simulations of the open- and closed-loop system are conducted, and robustness against model uncertainty is studied. Results illustrate the controller’s performance when dealing with imperfect information and unexpected system behavior, and how it can be tuned to produce the desired treatment outcome. However, optimal operation is conditioned by a limited prediction horizon, as well as strict adherence to treatment schedules and dose restrictions. © IFIP International Federation for Information Processing 2024.

关键词： Nonlinear systems

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Multispectral Image Segmentation in Agriculture: Evaluating Deep Learning Models with Train-Test Split and Cross-Validation Strategies

Multispectral Image Segmentation in Agriculture: Evaluating ...

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Iberian robotics Conference (ROBOT)

作者： Wilgo Cardoso Tiago Barros Gil Gonçalves Cristiano Premebida Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Dept. of Mathematics Institute for Systems Engineering and Computers at Coimbra University of Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

In agricultural robotics, the integration of multi-spectral image processing and deep learning (DL) has become the state-of-the-art (SOTA) in crop monitoring, yield estimation, and efficient land management. This work addresses the impact of different DL-segmentation models and evaluation protocols on multispectral imagery datasets collected by a UAV over vineyards. In terms of evaluation protocols, we have considered train-test split, standard k-fold cross-validation, and group k-fold cross-validation. While the first two assume that the training and test data are drawn from the same underlying distribution, the group k-fold cross-validation protocol assumes that each fold represents distinct distributions. Most works either adopt train-test split or k-fold cross-validation under the assumption that both the training and test sets are drawn from the same distribution. However, this assumption is rarely met in real-world applications. Therefore, the objective of this study is to evaluate and compare different evaluation protocols within the context of a real-world agricultural task, highlighting their limitations and weaknesses. Two SOTA DL-based segmentation models, SegNet and DeepLabV3, are employed to perform semantic segmentation on datasets of three Vineyards. The models have been trained and tested considering single-modality representations. In addition to the RGB modality, models trained on NDVI, GNDVI and early fusion are also evaluated. The performance of the models are evaluated using the IoU metric across different dataset configurations. The results indicate that the early fusion representation achieves the highest performance across the various splitting protocols, compared to the single-input representations. The results also show that the train-test and random k-fold splitting approaches report similar results. However, when employing group k-fold the performance drops consistently across both models and the modalities. This indicates that the models

关键词： Deep learning Training Precision agriculture Measurement Protocols Data models Yield estimation Robots Monitoring Standards

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An Augmented Reality Application for Upper-Limb Rehabilitation Supported by a Robotic Arm 6

An Augmented Reality Application for Upper-Limb Rehabilitati...

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6th Experiment at International Conference, exp.at 2023

作者： Rodrigues, Jose Carlos Restivo, Maria Teresa Menezes, Paulo University of Porto LAETA-INEGI Faculty of Engineering Porto Portugal University of Porto LAETA-INEGI Porto Portugal A3ES Lisbon Portugal University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra Portugal

ISBN: (纸本)9798350314403

This work looks at the potential use of a robotic arm and augmented reality application, with a head-mounted display, in an upper-limb rehabilitation context. A sample application using a robotic simulator and a cross-sectional study using a real robotic arm are shown. Results from the usability evaluation are positive and encourage further development, but some identified problems must be corrected before further evaluations. © 2023 IEEE.

关键词： Robotic arms

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation

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npj Flexible Electronics 2024年第1期8卷 856-868页

作者： Jinwoo Lee Kangkyu Kwon Ira Soltis Jared Matthews Yoon Jae Lee Hojoong Kim Lissette Romero Nathan Zavanelli Youngjin Kwon Shinjae Kwon Jimin Lee Yewon Na Sung Hoon Lee Ki Jun Yu Minoru Shinohara Frank L.Hammond Woon-Hong Yeo Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoul 04620Republic of Korea IEN Center for Wearable Intelligent Systems and Healthcare Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Industrial Design Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering Yonsei UniversitySeoul 03722Republic of Korea School of Biological Sciences Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Materials Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper ***,we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength *** embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities,which are simultaneously computed to determine the user’s intended *** deep learning predicts four upper-limb joint motions with an average accuracy of 96.2%at a 500–550 ms response rate,suggesting that the exoskeleton operates just by human *** addition,an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87mm at *** intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

关键词： skeleton learning utilize

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A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification

arXiv

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arXiv 2023年

作者： Pereira, Ricardo Barros, Tiago Garrote, Luís Lopes, Ana Nunes, Urbano J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Polytechnic Institute of Tomar Portugal

This work proposes a novel approach that uses a semantic segmentation mask to obtain a 2D spatial layout of the segmentation-categories across the scene, designated by segmentation-based semantic features (SSFs). These features represent, per segmentation-category, the pixel count, as well as the 2D average position and respective standard deviation values. Moreover, a two-branch network, GS2F2App, that exploits CNN-based global features extracted from RGB images and the segmentation-based features extracted from the proposed SSFs, is also proposed. GS2F2App was evaluated in two indoor scene benchmark datasets: the SUN RGB-D and the NYU Depth V2, achieving state-of-the-art results on both datasets. © 2023, CC BY.

关键词： Semantic Segmentation

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