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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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Design and validation of a novel tracheal intubation robot*

Design and validation of a novel tracheal intubation robot*

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IEEE International Conference on robotics and Biomimetics

作者： Jingjing Luo Yu Tian Hongbo Wang Jiawei Wang Luyan Zhou Jianhao Tang Xing Zhu Lili Feng Yuan Han Wenxian Li Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China Jihua Laboratory Foshan China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China Eye & ENT Hospital of Fudan University Shanghai China Shanghai Clinical Research Center for Aging and Medicine Shanghai Shanghai Intelligent Robot Engineering Technology Research Center Shanghai China

Tracheal intubation is a crucial measure to ensure smooth respiration during general anesthesia surgeries. Currently, manual intubation is commonly employed, with its success heavily reliant on the proficiency of the physicians performing the procedure. In this paper, a 3 degree-of-freedom (DOF) master-slave operated tracheal intubation robotic system is proposed, wherein a flexible endoscope serves as the end effector. The structural components of the master-slave mechanism are described in detail, and the kinematic model of the flexible endoscope is established. Four participants were recruited to conduct experimental research on master-slave operation intubation. In the experiment, the shortest operation time recorded was 8.88 seconds, while the longest is 72.04 seconds, they are less than the clinically required safety threshold of 2 minutes. This validates the feasibility of the developed prototype in achieving robot-assisted intubation.

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Origami Actuator with Tunable Limiting Layer for Reconfigurable Soft robotic Grasping

Origami Actuator with Tunable Limiting Layer for Reconfigura...

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

作者： Yang Yang Kejin Zhu Yuan Xie Shaoyang Yan Juan Yi Pei Jiang Yunquan Li Yazhan Zhang Yingtian Li School of Automation Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China State Key Laboratory of Mechanical Transmission University of Chongqing Chongqing China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents a soft actuator inspired by origami and a tunable strain limiting layer, which is proposed for reconfigurable soft robotic grasping. Main structure of the actuator is based on Miura origami which generates extension under pressurized air while a limiting layer with tunable length enables the actuator with different motion patterns. By driving the limiting layer through a servo motor, the range of motion and trajectory of the actuator can be pre-programed and the gripper’s grasping range will be affected accordingly. This paper discusses the design, fabrication, analysis and experimental verification of the actuator. Then grasping performance of the gripper under objects of different shapes, sizes, and weights is experimentally evaluated. The reconfigurable soft gripper can be applied as an end-effector to accomplish adaptive grasping tasks with various targets.

关键词： Actuators Limiting Grasping Soft robotics robot sensing systems Trajectory Grippers Motion control Tuning Strain

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

Automated Detection of Epileptic Spikes and Seizures Incorpo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei230088 China Sanbo Brain Hospital Capital Medical University Beijing Key Laboratory of Epilepsy Department of Neurosurgery Beijing China Peking University McGovern Institute for Brain Research Beijing China Peking University Center for Mri Research Academy for Advanced Interdisciplinary Studies Beijing China Peking University Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Beijing China Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China

ISBN: (纸本)9798350386226

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal's intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor's spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. © 2024 IEEE.

关键词： Magnetoencephalography

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A Remotely Driven Finger Rehabilitation robot for Passive and Active Rehabilitation 12

A Remotely Driven Finger Rehabilitation Robot for Passive an...

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12th International Conference on CYBER technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Wang, Hongbo Du, Jiazheng Zhang, Yongshun Tian, Yu Zhang, Dagan Ning, Yuansheng Niu, Jianye Hebei Provincial Key Laboratory of Parallel Robot and Mechatronic System Yanshan University Qinhuangdao066004 China Academy for Engineering and Technolgy Fudan University Shanghai200433 China Shanghai Clinical Research Center for Aging and Medicine Shanghai200040 China Yanshan University Key Laboratory of Advanced Forging and Stamping Technology Science of Ministry of Education Qinhuangdao066004 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In this paper, a finger rehabilitation robot with flexion/extension function is developed. This device consists of a four-finger mechanism and a thumb mechanism. The four-finger mechanism is modular designed, and consists of the metacarpophalangeal joint mechanism and the proximal interphalangeal joint mechanism. The thumb mechanism has an active degree of freedom (DoF). The hardware system and software system of the finger rehabilitation robot were built, and preliminary experiments were carried out, including the passive and active movement of the single joint and the multi-finger movement. The results show the range of motion of this robot can meet the requirements of the rehabilitation, and it is able to carry out active rehabilitation training by means of the human-machine contact force, and it has the ability to realize occupational therapy training for grasping objects in different shapes. © 2022 IEEE.

关键词： Degrees of freedom (mechanics)

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Adapting to the “Open World”: The Utility of Hybrid Hierarchical Reinforcement Learning and Symbolic Planning

Adapting to the “Open World”: The Utility of Hybrid Hierar...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Pierrick Lorang Helmut Horvath Tobias Kietreiber Patrik Zips Clemens Heitzinger Matthias Scheutz Human-Robot Interaction Laboratory Tufts University Medford MA USA AIT Austrian Institute of Technology GmbH Center for Vision Automation & Control Vienna Austria Institute of Information Systems Engineering Faculty of Informatics TU Wien TU Wien Vienna Austria

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Open-world robotic tasks such as autonomous driving pose significant challenges to robot control due to unknown and unpredictable events that disrupt task performance. Neural network-based reinforcement learning (RL) techniques (like DQN, PPO, SAC, etc.) struggle to adapt in large domains and suffer from catastrophic forgetting. Hybrid planning and RL approaches have shown some promise in handling environmental changes but lack efficiency in accommodation speed. To address this limitation, we propose an enhanced hybrid system with a nested hierarchical action abstraction that can utilize previously acquired skills to effectively tackle unexpected novelties. We show that it can adapt faster and generalize better compared to state-of-the-art RL and hybrid approaches, significantly improving robustness when multiple environmental changes occur at the same time.

关键词： Training robot control Reinforcement learning Robustness Planning Task analysis Autonomous vehicles

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Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread Functions

arXiv

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arXiv 2024年

作者： Qian, Xiaolong Jiang, Qi Gao, Yao Gao, Shaohua Yi, Zhonghua Sun, Lei Wei, Kai Li, Haifeng Yang, Kailun Wang, Kaiwei Bai, Jian State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics Hunan University Changsha410012 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

Controllable Depth-of-Field (DoF) imaging commonly produces amazing visual effects based on heavy and expensive high-end lenses. However, confronted with the increasing demand for mobile scenarios, it is desirable to achieve a lightweight solution with Minimalist Optical Systems (MOS). This work centers around two major limitations of MOS, i.e., the severe optical aberrations and uncontrollable DoF, for achieving single-lens controllable DoF imaging via computational methods. A Depth-aware Controllable DoF Imaging (DCDI) framework is proposed equipped with All-in-Focus (AiF) aberration correction and monocular depth estimation, where the recovered image and corresponding depth map are utilized to produce imaging results under diverse DoFs of any high-end lens via patch-wise convolution. To address the depth-varying optical degradation, we introduce a Depth-aware Degradation-adaptive Training (DA2T) scheme. At the dataset level, a Depth-aware Aberration MOS (DAMOS) dataset is established based on the simulation of Point Spread Functions (PSFs) under different object distances. Additionally, we design two plug-and-play depth-aware mechanisms to embed depth information into the aberration image recovery for better tackling depth-aware degradation. Furthermore, we propose a storage-efficient Omni-Lens-Field model to represent the 4D PSF library of various lenses. With the predicted depth map, recovered image, and depth-aware PSF map inferred by Omni-Lens-Field, single-lens controllable DoF imaging is achieved. To the best of our knowledge, we are the first to explore the single-lens controllable DoF imaging solution. Comprehensive experimental results demonstrate that the proposed framework enhances the recovery performance, and attains impressive single-lens controllable DoF imaging results, providing a seminal baseline for this field. The source code and the established dataset will be publicly available at https://***/XiaolongQian/DCDI. Copyright © 2024, The A

关键词： Optical transfer function

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Towards Precise 3D Human Pose Estimation with Multi-Perspective Spatial-Temporal Relational Transformers

arXiv

引用

arXiv 2024年

作者： Jiao, Jianbin Cheng, Xina Chen, Weijie Yin, Xiaoting Shi, Hao Yang, Kailun School of Artificial Intelligence Xidian University China State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

3D human pose estimation captures the human joint points in three-dimensional space while keeping the depth information and physical structure. That is essential for applications that require precise pose information, such as human-computer interaction, scene understanding, and rehabilitation training. Due to the challenges in data collection, mainstream datasets of 3D human pose estimation are primarily composed of multi-view video data collected in laboratory environments, which contains rich spatial-temporal correlation information besides the image frame content. Given the remarkable self-attention mechanism of transformers, capable of capturing the spatial-temporal correlation from multi-view video datasets, we propose a multi-stage framework for 3D sequence-to-sequence (seq2seq) human pose detection. Firstly, the spatial module represents the human pose feature by intra-image content, while the frame-image relation module extracts temporal relationships and 3D spatial positional relationship features between the multi-perspective images. Secondly, the self-attention mechanism is adopted to eliminate the interference from non-human body parts and reduce computing resources. Our method is evaluated on Human3.6M, a popular 3D human pose detection dataset. Experimental results demonstrate that our approach achieves state-of-the-art performance on this dataset. The source code will be available at https://***/WUJINHUAN/3D-human-pose. Copyright © 2024, The Authors. All rights reserved.

关键词： Human computer interaction

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Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images

arXiv

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arXiv 2025年

作者： Wu, Ruixin Li, Zihan Wang, Jin Xu, Xiangyu Yu, Huan Zheng, Zhi Huang, Kaixiang Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China State Key Laboratory of Robotics and Systems Department of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often struggle to leverage the effectiveness of diffusion models in super-resolution, largely due to the unnatural range-azimuth heatmap (RAH) or bird’s eye view (BEV) representation. To overcome this limitation, we propose a novel method that pioneers the application of fusing range images with image diffusion models, achieving accurate and dense mmWave radar point clouds that are similar to LiDAR. Benefitting from the projection that aligns with human observation, the range image representation of mmWave radar is close to natural images, allowing the knowledge from pre-trained image diffusion models to be effectively transferred, significantly improving the overall performance. Extensive evaluations on both public datasets and self-constructed datasets demonstrate that our approach provides substantial improvements, establishing a new state-of-the-art performance in generating truly three-dimensional LiDAR-like point clouds via mmWave radar. Copyright © 2025, The Authors. All rights reserved.

关键词： Radar imaging

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