检索结果-内蒙古大学图书馆

Adaptive network interactions in cognition after stimulation-induced inferior parietal lobe inhibition

Brain Stimulation 2023年第1期16卷 324-325页

作者： Williams, Kathleen Numssen, Ole Bzdok, Danilo Hartwigsen, Gesa Max Planck Institute for Human Cognitive and Brain Sciences Lise Meitner Research Group Cognition and Plasticity Germany Mila QC Canada Montreal Neurological Institute McConnell Brain Imaging Centre Canada Department of Biomedical Engineering Faculty of Medicine and School of Computer Science McGill University Montréal QC Canada

来源：评论

学校读者我要写书评

暂无评论

Exploiting aliasing for manga restoration

arXiv

引用

arXiv 2021年

作者： Xie, Minshan Xia, Menghan Wong, Tien-Tsin Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology SIAT CAS

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screentone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative twostage method to restore quality bitonal manga from degraded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SENet) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Restoration

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Different Radar Placements for Food Intake Monitoring Using Deep Learning

Evaluation of Different Radar Placements for Food Intake Mon...

引用

IEEE National Conference on Radar

作者： Chunzhuo Wang T. Sunil Kumar Walter De Raedt Guido Camps Hans Hallez Bart Vanrumste The e-Media Research Lab and the ESAT-STADIUS Division KU Leuven Leuven Belgium The Life Science Department IMEC Heverlee Belgium Department of Agrotechnology and Food Sciences The Division of Human Nutrition and Health Wageningen University and Research and the OnePlanet Research Center Wageningen Netherlands Department of Computer Science The M-Group DistriNet KU Leuven Sint-Michiels Belgium

Automated food intake monitoring has drawn significant attention due to its potential applications in the healthcare domain. Plenty of research, including wrist-worn imu-based and camera-based approaches, have emerged to detect food intake activity passively and objectively. Recently, researchers explored radar for food intake monitoring because of its contactless and privacy-preserving characteristics. In this study, we deploy the Frequency Modulated Continuous Wave (FMCW) radar in three different positions to investigate the performance of each position in automated eating gesture detection. The three positions are front, side, and overhead. Fifteen participants are recruited to have three meals (45 meals, 641 min in total), while the radar is deployed in different positions in each meal. A 3D Temporal Convolutional Network (3D-TCN) is used to process the range-doppler cube (RD Cube) of each dataset. The Leave-One-Subject-Out (LOSO) validation method shows that putting radar in the front position obtains the best performance with a segmental F1-score of 0.786 and 0.825 for eating and drinking gestures, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intake Gesture Detection With IMU Sensor in Free-Living Environments: The Effects of Measuring Two-Hand Intake and Down-Sampling

Intake Gesture Detection With IMU Sensor in Free-Living Envi...

引用

International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Chunzhuo Wang Jiaze Kong Yutong Cai T. Sunil Kumar Walter De Raedt Guido Camps Hans Hallez Bart Vanrumste The e-Media Research Lab and the ESAT-STADIUS Division KU Leuven Leuven Belgium The Life Science Department IMEC Heverlee Belgium Department of Agrotechnology and Food Sciences The Division of Human Nutrition and Health Wageningen University and Research and the OnePlanet Research Center Wageningen The Netherlands Department of Computer Science The M-Group DistriNet KU Leuven Sint-Michiels Belgium

Food intake monitoring plays an important role in personal dietary systems. Numerous approaches have been proposed to automatically detect eating gestures using various sensors and machine learning. However, existing eating gesture detection approaches mainly focus on meal sessions. Such a task is still challenging in free-living environments due to longer monitoring duration and more non-feeding activities. This paper proposes a wearable Inertial Measurement Unit (IMU) based method to detect eating and drinking gestures in free-living environments. Two important factors that impede intake gesture detection in free-living environments are addressed: 1) how to handle IMU data from two hands, and 2) what is the impact of downsampling sensor data on performance. To integrate two-hand data, we propose a solution that combines hand mirroring and temporal concatenation techniques. The multi-stage temporal convolutional network (MS-TCN) is applied to effectively recognise intake gestures. A dataset contains 12 subjects with 67.5 h data is collected for validation. Moreover, IMU data with different sampling frequencies are processed to test performance. Validated by Leave-One-Subject-Out (LOSO) method, our approach (with 16 Hz sampling frequency) achieves a segmental F1-score of 0.826 and 0.893 for recognizing eating and drinking gestures, respectively. Results show that the proposed solution outperforms existing two-hand data combination approaches. Moreover, in our case, a higher sampling frequency does not always mean better performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A human-human Physical interaction System for Training Handwriting

A Human-Human Physical Interaction System for Training Handw...

引用

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

作者： S. Buscaglione A. Noccaro C. Provenzale C. Bonsignori E. Ivanova L. Sparaci F. Taffoni E. Burdet D. Formica NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit Università Campus Bio-Medico di Roma Italy Neurorobotics Lab Newcastle University Newcastle Upon Tyne UK Advanced Robotics and Human-Centred Technologies-CREO Laboratory Università Campus Bio-Medico di Roma Italy Department of Bioengineering Imperial College of Science Technology and Medicine UK School of Electric Engineering and Computer Science Queen Mary University of London UK Institute of Cognitive Sciences and Technologies (ISTC) National Research Council of Italy (CNR) Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Handwriting training in childhood is known to influence academic success, foster self-esteem, and contribute to overall development. Previous studies underscored the positive impact of haptic feedback on handwriting learning using one-way communication systems that provided the trainee with haptic feedback based on pre-recorded trainer's movements. In this study, we propose a system involving bidirectional physical coupling (haptic communication) between a trainee and a trainer to exploit the advantages of haptic communication for motor task improvement, proved in tracking tasks. The system, composed of an interactive display and a coupling spring, was tested in a handwriting task performed by a dyad of healthy adults with different cursive handwriting backgrounds. The participants' performance demonstrated the feasibility of using this system for handwriting, opening the way for further studies to assess its benefits in handwriting improvement and learning.

关键词： Training Couplings Tracking Communication systems Biomechatronics Motors Haptic interfaces Springs Robots

来源：评论

学校读者我要写书评

暂无评论

CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

来源：评论

学校读者我要写书评

暂无评论

Sensorimotor Devices: Coupling Sensing and Actuation to Augment Bodily Experience

Sensorimotor Devices: Coupling Sensing and Actuation to Augm...

引用

2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Strohmeier, Paul Vidal, Laia Turmo Vega, Gabriela Reed, Courtney N. Mazursky, Alex AliAbbasi, Easa Tajadura-Jiménez, Ana Steimle, Jürgen Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany KTH Royal Institute of Technology Stockholm Sweden Sensorimotor Interaction Group Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Institute for Digital Technologies Loughborough University London London United Kingdom University of Chicago Chicago IL United States Max Planck Institute for Informatics Saarbrücken Germany Department of Computer Science and Engineering Universidad Carlos III de Madrid Leganés Madrid Spain Saarland University Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9798400713958

An emerging space in interface research is wearable devices that closely couple their sensing and actuation abilities. A well-known example is MetaLimbs [39], where sensed movements of the foot are directly mapped to the actuation of supernumerary robotic limbs. These systems are different from wearables focused on sensing, such as fitness trackers, or wearables focused on actuation, such as VR headsets. They are characterized by tight coupling between the user’s action and the resulting digital feedback from the device, in time, space, and mode. The properties of this coupling are critical for the user’s experience, including the user’s sense of agency, body ownership, and experience of the surrounding world. Understanding such systems is an open challenge, which requires knowledge not only of computer science and HCI, but also Psychology, Physiology, Design, Engineering, Cognitive Neuroscience, and Control Theory. This workshop aims to foster discussion between these diverse disciplines and to identify links and synergies in their work, ultimately developing a common understanding of future research directions for systems that intrinsically couple sensing and action. © 2025 Copyright held by the owner/author(s).

关键词： feedback systems motion-coupled feedback sensorimotor interaction wearables

来源：评论

学校读者我要写书评

暂无评论

Investigation on data adaptation techniques for neural named entity recognition

arXiv

引用

arXiv 2021年

作者： Tokarchuk, Evgeniia Thulke, David Wang, Weiyue Dugast, Christian Ney, Hermann Informatics Institute University of Amsterdam Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University

Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of samples, it is important to obtain additional labeled data in an efficient and reliable manner. A common practice is to utilize large monolingual unlabeled corpora. Another popular technique is to create synthetic data from the original labeled data (data augmentation). In this work, we investigate the impact of these two methods on the performance of three different named entity recognition tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Federated semi-supervised medical image classification via inter-client relation matching

arXiv

引用

arXiv 2021年

作者： Liu, Quande Yang, Hongzheng Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Beihang University China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training setting, most hospitals in realistic usually cannot afford the intricate data labeling due to absence of budget or expertise. This paper studies a practical yet challenging FL problem, named Federated Semi-supervised Learning (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals). We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme. The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, thereby mitigating the deficiency of task knowledge at unlabeled clients and promoting discriminative information from unlabeled samples. We validate our method on two large-scale medical image classification datasets. The effectiveness of our method has been demonstrated with the clear improvements over state-of-the-arts as well as the thorough ablation analysis on both tasks4 © 2021, CC BY-NC-ND.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

Comparative Methodological Approaches for Analyzing Margin of Stability During Normal Walking

Comparative Methodological Approaches for Analyzing Margin o...

引用

2024 International Symposium on 3D Analysis of human Movement, 3DAHM 2024

作者： Sganga, Magali Villagra, Federico Akanyeti, Otar Ravera, Emiliano Pablo Universidad Maimónides Universidad Nacional de La Matanza Laboratorio de Investigación del Movimiento Humano Departamento de Ciencias de la Salud Buenos Aires Argentina Penglais Campus Aberystwyth University Department of Life Sciences Carwyn James Building Wales AberystwythSY233DF United Kingdom Aberystwyth University Department of Computer Science Wales AberystwythSY23 3DB United Kingdom National University of Entre Rios Group of Analysis Modeling Processing and Clinician Implementation of Biomechanical Signals and Systems IBB CONICET-UNER Human Movement Research Laboratory Oro Verde Argentina

ISBN: (纸本)9798331521738

A comprehensive evaluation of pathological gait patterns is essential for understanding the underlying pathology and its progression to deliver personalized treatments. One quantitative indicator for assessing fall risks during walking is margin of stability (MoS), a measure of instantaneous mechanical stability derived from the dynamic relationship between the extrapolated center of mass (CoM) and the center of pressure. However, extrapolating CoM during movement is not straightforward, and there is an undesired variability induced by the selected measurement protocol, which often depends on the available technology. Up until now, the extent of this variability has not been investigated systematically. This study compares four different CoM estimation methods. Based on the Conventional Gait Model, the first two methods estimate CoM using virtual sacrum (1) and pelvis centroid (2). The third method estimates CoM from ground reaction force (3), and the last method uses the accelerometer and gyroscope data from a smartphone mounted on the lower back. 21 healthy subjects (10 female, 11 male, age: 31.7 ± 9.0 years;BMI: 24.7 ± 3.5 kg m-2) walked on an instrumented treadmill at a fixed speed at 1.3 m s -1 for 3 minutes. Descriptive statistical analysis was performed, including temporal and normalized gait cycle analysis, focusing on the minimum and maximum MoS, and its ranges during walking. SPMld with paired t-tests was employed for the analysis. Our results suggest that CoM estimation varies significantly depending on the selected measurement protocol and technology which should be considered while reporting and interpreting MoS results. © 2024 IEEE.

关键词： Extrapolation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：