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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wenfeng Song Xinyu Zhang Shuai Li Yang Gao Aimin Hao Xia Hau Chenglizhao Chen Ning Li Hong Qin Beijing Information Science and Technology University Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Research Unit of Virtual Human and Virtual Surgery (2019RU004) Chinese Academy of Medical Sciences College of Computer Science and Technology China University of Petroleum (East China) Department of Computer Science Stony Brook University (SUNY at Stony Brook) New York USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

To date, the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research. The underlying challenge demands both a discriminating interpretation of language and a comprehen-sive physics-centric model supporting real-world dynamics. To ameliorate, this paper advocates HOIAnimator, a novel and interactive diffusion model with perception ability and also ingeniously crafted to revolutionize the animation of complex interactions from linguistic narratives. The effectiveness of our model is anchored in two ground-breaking innovations: (1) Our Perceptive Diffusion Models (PDM) brings together two types of models: one focused on hu-man movements and the other on objects. This combination allows for animations where humans and objects move in concert with each other, making the overall motion more realistic. Additionally, we propose a Perceptive Message Passing (PMP) mechanism to enhance the communication bridging the two models, ensuring that the animations are smooth and unified; (2) We devise an interaction Contact Field (ICF), a sophisticated model that implicitly captures the essence of HOls. Beyond mere predictive contact points, the ICF assesses the proximity of human and object to their respective environment, informed by a probabilistic distribution of interactions learned throughout the denoising phase. Our comprehensive evaluation showcases HOlani-mator's superior ability to produce dynamic, context-aware animations that surpass existing benchmarks in text-driven animation synthesis.

关键词： computer vision Technological innovation Message passing Computational modeling Noise reduction Linguistics Animation

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Natural human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans 10

Implementation of a Hip Fracture Classification Network Base...

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10th International Conference on computer and Communications, ICCC 2024

作者： Zhao, Kui Yang, Meng Zhang, Ti Hu, Die Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China University of Chinese Academy of Science Shenyang China Peking University Third Hospital Department of Orthopedics Beijing China

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition. © 2024 IEEE.

关键词： Classification ConvNeXt Hip Fracture Segmentation V-Net

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Network Properties Analysis in Stroke Patients Based on the resting-state EEG 23

Network Properties Analysis in Stroke Patients Based on the ...

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Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering

作者： Zhongpeng Wang Jinxiang Nan Yijie Zhou Long Chen Shuang Liu Minpeng Xu Qi Li Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University China and Haihe Laboratory of Brain-computer Interaction and Human-machine Integration China Academy of Medical Engineering and Translational Medicine Tianjin University China Academy of Medical Engineering and Translational Medicine Tianjin University China and Department of Rehabilitation Tianjin Hospital China

ISBN: (纸本)9798400708343

In recent years, the researches on brain networks in stroke patients based on EEG signals have attracted attention. Few of them have investigated the specific patterns of FCs across metrics. In this paper, we focus on the lesion properties of common brain regions under different network metrics. Phase synchronization indexes (PSI) between EEG channels were calculated to construct graph theory networks in the Alpha and Beta bands for stroke patients and healthy controls. Network metrics degree, clustering coefficient, and local efficiency were utilized to characterize brain network differences between groups. The results revealed that in Alpha band, patients were characterized by a lower clustering coefficient and exhibited lower global efficiency and lower degree in the Beta band. Regionally, the motor cortex and parietal cortex were identified as common impaired regions across all network metrics, performing weaker capacity for information transfer and integration, which may explain their motor disability. The findings of our study will play an important role in motor evaluation after stroke.

关键词： Electroencephalography (EEG) graph theory phase synchronization index (PSI) stroke

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Insight into the liver dysfunction in COVID-19 patients: Molecular mechanisms and possible therapeutic strategies

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World Journal of Gastroenterology 2023年第14期29卷 2064-2077页

作者： Naina Khullar Jasvinder Singh Bhatti Satwinder Singh Bhawana Thukral P Hemachandra Reddy Gurjit Kaur Bhatti Department of Zoology Mata Gujri CollegeFatehgarh Sahib 140407PunjabIndia Laboratory of Translational Medicine and Nanotherapeutics Department of Human Genetics and Molecular MedicineSchool of Health SciencesCentral University of PunjabBathinda 151401PunjabIndia Department of Computer Science and Technology Central University of PunjabBathinda 151401PunjabIndia Department of Nutrition and Dietetics University Institute of Applied Health SciencesChandigarh UniversityMohali 140413PunjabIndia Department of Internal Medicine Texas Tech University Health Sciences CenterLubbockTX 79430United States Department of Medical Lab Technology University Institute of Applied Health SciencesChandigarh UniversityMohali 140413PunjabIndia

As of June 2022,more than 530 million people worldwide have become ill with coronavirus disease 2019(COVID-19).Although COVID-19 is most commonly associated with respiratory distress(severe acute respiratory syndrome),metaanalysis have indicated that liver dysfunction also occurs in patients with severe *** studies revealed distinctive patterning in the receptors on the hepatic cells that helps in viral invasion through the expression of angiotensinconverting enzyme *** has also been reported that in some patients with COVID-19,therapeutic strategies,including repurposed drugs(mitifovir,lopinavir/ritonavir,tocilizumab,etc.)triggered liver injury and cholestatic *** proven indicators support cytokine storm-induced hepatic *** there are 1.5 billion patients with chronic liver disease worldwide,it becomes imperative to critically evaluate the molecular mechanisms concerning hepatotropism of COVID-19 and identify new potential *** review also designated a comprehensive outlook of comorbidities and the impact of lifestyle and genetics in managing patients with COVID-19.

关键词： COVID-19 Liver damage Pharmacotherapy Cytokine storm Molecular mechanisms

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Immersive Virtual Reality Improves Psychophysiological Responses to Agent-Guided Breathing Training

SSRN

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SSRN 2025年

作者： Corrò, Christian Chittaro, Luca Human-Computer Interaction Lab Department of Mathematics Computer Science and Physics University of Udine Via delle Scienze 206 Udine33100 Italy

Slow deep breathing (SDB) techniques offer physical and mental health benefits that help mitigate stress, anxiety, and pain. However, economic and logistical barriers limit access to traditional breathing training programs. While self-help resources like videos and apps are available, they lack the personalized guidance and feedback provided by human trainers and tend to have high dropout rates. The increasing availability of consumer-grade virtual reality (VR) headsets presents new opportunities for immersive breathing training experiences, potentially enhancing engagement and adherence. However, it remains unclear whether breathing training can benefit from immersive VR (iVR). This paper addresses this question by comparing agent-guided breathing training in iVR (headset-based) vs. non-immersive VR (based on a computer monitor). We introduce and evaluate a virtual agent that provides users with real-time, personalized feedback on their respiratory frequency and depth. We evaluate both self-reported measures (including engagement, attention, presence, and perception of the virtual agent) and objective physiological signals, comprising respiration, electrodermal activity, cardiac activity, and brain prefrontal cortex activity (acquired through functional near-infrared spectroscopy, fNIRS). The results of the user study on 36 participants highlighted several benefits of iVR over non-immersive VR. Results showed that iVR increased users’ attention, engagement, sense of presence, social presence, and perceived anthropomorphism of the virtual agent. Physiologically, iVR improved depth of breathing at the correct SDB frequency, reduced cognitive load, decreased sympathetic arousal and increased parasympathetic activity. The paper describes and discusses these findings, highlighting the important role that iVR can play in enhancing the effectiveness of breathing training. © 2025, The Authors. All rights reserved.

关键词： Audition

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kulwa, Frank Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Oyemakinde, Tolulope Tofunmi Obe, Olumide Olayinka Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China University of Derby School of Computing and Engineering DerbyDE22 3AW United Kingdom Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9781665493840

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario ( © 2023 IEEE.

关键词： Electrodes

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A Comparison of Three Types of computer-Based Procedures: An Experiment Using the Rancor Microworld Simulator

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Proceedings of the human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 2552 - 2557页

作者： Anna Hall Ronald L. Boring Thomas A. Ulrich Roger Lew Michelle Velazquez Jinding Xing Tim Whiting Georgios Michail Makrakis Human Factors and Reliability Department Idaho National Laboratory Idaho Falls Idaho USA Virtual Technology and Design Program University of Idaho Moscow Idaho USA Psychology Department Idaho State University Pocatello Idaho USA Civil and Environmental Engineering Carnegie Mellon University Pittsburgh Pennsylvania USA Computer Science Department Brigham Young University Provo Utah USA Computer Science Department University of Idaho Idaho Falls Idaho USA

The nuclear power industry has historically used paper-based procedures, but a shift towards computer-based procedures (CBPs) has the potential to reduce human errors, alleviate mental workload, and improve work performance. Twenty-seven participants were randomly assigned to one of three CBP types using the Rancor Microworld Simulator, and each performed two different types of operational scenarios (startup and loss of feedwater). The three CBP types varied in levels of digitalization. It was hypothesized that there would be less favorable impressions of the most basic procedures, and that these would demonstrate lower usability than types with greater digitalization. Overall, our predictions were partially supported with some interesting caveats, specifically with some performance benefits for CBPs that provided indicators but not embedded controls. We discuss our findings in terms of optimal levels of digitalization/automation within nuclear operations and suggest pathways for future directions.

关键词： human-system integration Interface evaluation Usability System design and analysis

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On Improving Boundary Quality of Instance Segmentation in Cluttered and Chaotic Scenarios

On Improving Boundary Quality of Instance Segmentation in Cl...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Biqi Yang Xiaojie Gao Xianzhi Li Yun-Hui Liu Chi-Wing Fu Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology

Instance segmentation is a long-standing task for supporting robotic bin picking. However, objects of diverse classes can be closely packed with occlusions in cluttered and chaotic scenes, hence, even recent methods could have difficulty in locating clear and precise boundaries to distinguish nearby objects. In this work, we aim to improve the boundary quality of the instance masks for robust and precise instance segmentation in these challenging scenarios. Technical-wise, we first formulate an IoU-based Boundary-aware Mask head (IBM head) for predicting the instance-level mask, boundary, and their corresponding IoU scores. With this core module, we then follow the coarse-to-fine strategy and design our pipeline with two stages: an 1IoUNet to learn localization-based objectness cue and a hierarchical mask refiner to produce sharper and cleaner boundaries. We deploy the IBM head throughout the framework. Extensive experimental results on three grasping benchmarks manifest that our method attains the best instance segmentation performance, compared with the state-of-the-art approaches. Practically, we conduct real-world picking tests to show that with the objectness and boundary IoU scores as guidance, we are able to filter invalid (occluded) instances and select high-fidelity (exposed) instances for grasping.

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans

Implementation of a Hip Fracture Classification Network Base...

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International Conference on computer and Communications (ICCC)

作者： Kui Zhao Meng Yang Ti Zhang Die Hu Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Science Shenyang China Department of Orthopedics Peking University Third Hospital Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

关键词： Deep learning Image segmentation Adaptation models Osteoporosis Accuracy Computed tomography Transfer learning Predictive models Older adults Hip

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