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Exploring Eye-tracking based Biomarkers to Assess Cognitive Abilities in Autistic Children: A Feasibility Study

IEEE Journal of Biomedical and Health Informatics 2025年第6期PP卷 PP页

作者： Zhang, Hanlin Hu, Chunchun Wang, Zhiyong Zhou, Bingrui Wang, Xinming Nie, Wei Ye, Qinyi Lin, Ruihan Xu, Xiu Liu, Honghai Children's Hospital of Fudan University Department of Child Health Care Shanghai201102 China Harbin Institute of Technology State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Shenzhen518055 China Peng Cheng Laboratory Shenzhen518000 China

Cognitive assessment can reveal a person's cognitive processing and behavioral patterns, making it an indispensable component of autism intervention and prognosis. Existing machine-assisted cognitive assessment methods primarily focus on children's performance outcomes, overlooking distinctive behavioral models, particularly characteristics of eye movement behavior, which have been demonstrated as the most direct indicators of cognitive abilities. In this study, we explore eye-tracking biomarkers for assisting cognitive assessment through a series of meticulously designed multi-level human-computer interaction protocols, encompassing three cognitive abilities: pairing and categorization, emotion recognition, and social interaction. A platform embedded with an eye-tracking module has been developed to reliably collect and analyze eye movement data, even in the presence of unrestricted large head movements in children. Experimental results indicate that there are significant group differences between autism and typically developing children in the eye-tracking features of total fixation duration, response latency, time to first fixation, mean fixation duration, and visit count in the absence of significant intergroup differences in the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) and Wechsler Intelligence Scale for Children (WISC) assessment results. In addition, certain eye movement features in each group are correlated with WPPSI/WISC scale scores, enabling clinical cognitive assessments within each group based on these eye movement features. This study suggests that using eye-tracking features as biomarkers to assist detailed cognitive assessments holds significant potential for the intervention and prognosis of autism. © 2013 IEEE.

关键词： Diseases

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Reliability Design of Manipulator Joint for China Space Station 2

Reliability Design of Manipulator Joint for China Space Stat...

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2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2022

作者： Li, Delun Zhu, Chao Zhang, Yun Bai, Mei Huang, Zhipeng Xie, Zongwu Liu, Teng Fu, Hao State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering CAST Beijing100094 China Beijing Satellite Manufacturing Co. Ltd. Beijing100094 China Beijing Institute of Space Environment Engineering Beijing100094 China

ISBN: (纸本)9781510657717

The TianHe manipulator is responsible for major tasks such as China Space Station construction, operation, and supporting astronaut's extravehicular operations. The joint is the direct execution part of the manipulator to realize flexible movement, and it is the key component to ensure the core functions and performance of the China Space Station manipulator, The composition of the joint is complex, and the experience of its reliability design is very poor. So the research of reliability design method for the joint is of great significance. Starting from the joint reliability design method, this paper uses various methods such as redundant design, margin design, and environmental adaptability design to complete the joint reliability design comprehensively. The ideas and methods for reliability design for the joint of the China Space Station manipulator introduced in this paper have important reference value for the reliability design for the space complicated electromechanical products. © 2022 SPIE. All rights reserved.

关键词： Space stations

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Feature Space Augmentation and Old Class Space Preservation for Class Incremental Learning

Feature Space Augmentation and Old Class Space Preservation ...

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第42届中国控制会议

作者： Ming Zeng Siying Li Jiazhe Liu Yuanhao Li Institute of Robotics and Automation Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University

The ability of continuously learning new knowledge without forgetting old ones is crucial to adapt to an ever-changing world. This scenario becomes more challenging when the previous data are not available. Current class incremental learning(CIL) approaches tend to incorporate new classes with backward compatibility, i.e., maintaining discriminability of old ones. By contrast, we focus on the extensibility and compatibility for future new classes in the early stage. Our proposed method achieves this by augmenting feature space. In detail, a large number of pseudo-new classes are generated via real example mixture and then train the initial spatially augmented model using pseudo-new classes and the base classes. Besides, considering the fact that it is impossible to maintain backward compatibility with only one prototype for each old class, the updated model also needs to preserve old class space in incremental stages. We employ a certain perturbation to the old class prototypes to effectively avoid old class space from being over-squeezed by the samples of new classes. The experiments on CIFAR-100 and ImageNet-Subset(100 classes) demonstrate that our method substantially reduces the overlap of old and new classes, outperforming state-of-the-art various baselines.

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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

Improved Capon Estimator for High-Resolution DOA Estimation ...

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作者： Zuo, Weiliang Xin, Jingmin Liu, Changnong Zheng, Nanning Sano, Akira National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an710049 China Data Center China Construction Bank Beijing100033 China Keio University Department of System Design Engineering Yokohama223-8522 Japan

Despite some efforts and attempts have been made to improve the direction-of-arrival (DOA) estimation performance of the standard Capon beamformer (SCB) in array processing, rigorous statistical performance analyses of these modified Capon estimators are still lacking. This paper studies an improved Capon estimator (ICE) for estimating the DOAs of multiple uncorrelated narrowband signals, where the higher-order inverse (sample) array covariance matrix is used in the Capon-like cost function. By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC (multiple signal classification), it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE, the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio (SNR). Furthermore the statistical performance of the ICE is analyzed, and the large-sample mean-squared-error (MSE) expression of the estimated DOA is derived. Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples, where the Cramer-Rao lower bound (CRB) is used to evaluate the validity of the derived asymptotic MSE expression. © 2014 Chinese Association of Automation.

关键词： Multiple signal classification

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Obstacle Identification Based on Deep Learning for Inspection Robot of FAST Wire Rope

Obstacle Identification Based on Deep Learning for Inspectio...

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Automation, robotics and Computer engineering (ICARCE), International Conference on

作者： Chengxu Yang Xiangyu Sun Gangfeng Liu School of Mechatronics Engineering Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

In this paper, for the requirements of image recognition and localization of the obstacles existing on the moving path in the inspection task of FAST cable inspection robot, the deep learning method is used to realize the recognition of multi-kind and multi-scale obstacles. The obstacles on FAST wire rope have not been identified before. By enhancing the data set and improving the network model, the recognition accuracy and speed are both improved, which meets the efficiency and accuracy requirements of obstacle detection on the wire rope, and provides help for the subsequent robot pace planning and overhaul tasks.

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Active Camera View Planning Based on Deep Reinforcement Learning in Indoor Exploration

Active Camera View Planning Based on Deep Reinforcement Lear...

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第42届中国控制会议

作者： Hao-Yu Wang Qing-Hao Meng Xu-Yang Dai Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and SystemSchool of Electrical and Information Engineering Tianjin University

In the last decades, significant progress has been made in the field of active vSLAM（visual simultaneous localization and mapping）. However, the majority of active vSLAM methods suffer from the problem of coupling the camera's viewpoint to the robot's motion, which limits the initiative during the exploration of unknown environments. To address this issue, this study proposes a camera view planning method based on deep reinforcement learning（DRL）, which makes the camera rotate independently. First, feature encoding is performed on the reconstructed navigation map using a multi-layer convolutional neural network（CNN）. Second, the DRL-based camera view planning method is trained through the proximal policy optimization（PPO）algorithm. Last, a significant number of tests is performed on a public dataset of indoor environments. The simulation results show that the proposed camera view planning method improves the exploration coverage by an average of 4%（33.86 m for traditional vs. 35.21 m for the suggested method）. Meanwhile, the kind of semantic information newly observed in each step increases by 21%（2.9 for traditional vs. 3.5 for the proposed method） on average, indicating the improvement in the efficiency of information acquisition.

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Buckling-Inspired Triboelectric Sensor for Multifunctional Sensing of Soft robotics and Wearable Devices

SSRN

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SSRN 2024年

作者： Xu, Jinsui Pan, Xueting Xu, Boyi Yue, Honghao Lu, Yifan Ying, Kai Wang, Zheping Yang, Fei State Key Laboratory of Robotics and System School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China Wu Xianming School of Intelligent Engineering South China University of Technology Guangzhou510641 China

Amidst the rapid development of intelligent robotics and wearable health monitoring devices, there is an urgent demand for flexible sensor systems with high sensitivity and precise feedback. In this paper, a multifunctional triboelectric sensor inspired by the phenomenon of buckling is introduced, which integrates kirigami structures with triboelectric nanogenerator principles to accurately detect one-dimensional (1D) and two-dimensional (2D) deformations. The sensor employs a contact-separating structure, with its friction layers consisting of 2D laser-cut kirigami electrodes, which deform into three-dimensional (3D) structures through tensile or compressive buckling. The tensile buckling sensor (BS) developed is capable of sensing the bending angle of soft grippers, allowing the detection of the size of grasped objects with a high recognition rate of 93.75%. Additionally, in its 1D compressive buckling form, the sensor can be used to recognize various human motion patterns effectively, while the 2D compressive version can be used to measure the expansion of curved surfaces with a sensitivity of 1.3307 V/mm. By combining the unique properties of buckling structures, the innovative triboelectric multifunctional sensor proposed offers a new solution for high-precision motion feedback control with potential application in soft robotics and health monitoring. © 2024, The Authors. All rights reserved.

关键词： Buckling

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Motion Capture and Gait Analysis of Ants with Leg Injuries

Motion Capture and Gait Analysis of Ants with Leg Injuries

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第42届中国控制会议

作者： Ming Zeng Yuanhao Li Chang Meng Shutong Zhong Zhijing Wang Feng Zhao Institute of Robotics and Automation Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University

Many insects in nature, such as ants, recover quickly after sustaining leg injuries. If this capability could be imparted to multi-legged robots, it would drastically enhance their ability to resist destruction and adapt to the environment. In this paper, the Camponotus Japonicus is used as the research object, and its locomotive patterns are studied under different leg-loss *** facilitate this, a high-speed acquisition system, with a dual-view(bottom view and side view) setup, is designed to analyze the ant's linear gaits. Subsequently, the pivotal gait characteristics of the injured ants are extracted by keyframe annotation technology, including the gait phase diagram, duty factor, angle before support, angle before swing, and leg swing range. Lastly,according to the obtained gait characteristics, the general and specific laws of gait adjustment in the absence of one or two legs are systematically analyzed, which provides the basis for parametric settings and strategy optimization of robotic injured gait self-adjustment.

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