检索结果-内蒙古大学图书馆

2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Liu, Haibin Xie, Xurong Ruzi, Rukiye Wang, Lan Yan, Nan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Science Beijing10049 China Chinese University of Hong Kong Hong Kong999077 Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen51800 China

ISBN: (纸本)9781665436786

Automatic music generation has become a more exciting research topic. However, the existing music generation methods tend to utilize music data inherently and hardly consider music generation from the perspective of music composition theory. Therefore, how to use music theory to guide the automatic generation of music is drawing increasing attention to this area. In this work, we aim to extract music rules from given corpora and then apply them to generate new music of a similar style. We divided the melody into different scales (music segments with different numbers of notes, not set of notes ordered by pitch) based on the music structures and then employed Latent Dirichlet Allocation (LDA) topic model to learn the structural constraints of the provided musical form. The multi-scale fusion of musical features through reinforcement learning (RL) enables the model to consider the music generation from the global scope. Our experimental results show that our model is superior to the baseline model according to objective and subjective rating. The music generated from our model has better consistency in terms of music style, which indicates that the extracted structural features and the multi-scale modeling are promising for music generation of a certain style or topic. © 2021 IEEE.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

Toward Improving the Performance of Hand Movement Intention Recognition for Upper Limb Amputees Using Flexible Ultrasound Transducers

Toward Improving the Performance of Hand Movement Intention ...

引用

Rehabilitation Engineering and Assistive Technology (i-CREATe), International Convention on

作者： Xinhao Peng Tolulope Tofunmi Oyemakinde Mengtao Wang Hang Zhao Lan Tian Yue Zheng Yao Guo Teng Ma Zhiyuan Liu Guanglin Li Xiangxin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen People's Hospital Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

In recent years, with the development of ultrasonic sensing technology, ultrasound is considered as a promising alternative to surface electromyography for the recognition of human hand movements. However, the existing ultrasound transducer is bulky and poor wearability, and the performance of ultrasound based motion recognition for amputees was not comprehensively investigated yet. So, in this study, we used a wearable, flexible and lightweight B-mode ultrasound probe to detect the morphological change of muscles of 24 fine hand movements for four subjects. In order to develop an effective B-mode ultrasound based motion recognition method, the performances of five kinds of image features and three kinds of classification algorithms were systematically investigated. And the results showed that the combination of HOG+SVM had the highest average recognition accuracy of 97.84% and the combination of Sobel+SVM was the most robust to individual difference. The work of this study may provide help for the application of muscle ultrasound image based motion recognition.

关键词： Support vector machines Ultrasonic imaging Image recognition Transducers Accuracy Surface morphology Muscles Sensors Probes Prosthetics

来源：评论

学校读者我要写书评

暂无评论

A high-density stretchable electrode array for stable sEMG monitoring on dorsal hand

A high-density stretchable electrode array for stable sEMG m...

引用

2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Yang, Ruofan Luo, Dong Sun, Jing Zhao, Hang Li, Qingsong Tian, Qiong Tong, Wei Qi, Dianpeng Li, Guanglin Liu, Zhiyuan University of Chinese Academy of Sciences School of Artificial Intelligence China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China The Chinese University of Hong Kong Hong Kong Hong Kong Chinese Academy of Sciences Center for Opto-Electronics Engineering & Technology Shenzhen Institutes of Advanced Technology China Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Science and Technology of China Department of Nano Science and Technology Institute Suzhou China Harbin Institute of Technology Miit Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical Engineering Harbin150001 China Harbin Institute of Technology Natl. and Loc. Jt. Eng. Lab. for Synthesis Transformation and Separation of Extreme Environ. Nutrients Harbin150001 China

ISBN: (纸本)9798350327182

Hand electromyogram (EMG) signals, instrumental in tasks like movement recognition, rehabilitation monitoring, disease diagnosis, and human-computer collaboration, are typically obtained via high-density EMG electrodes on the dorsal hand. Conventional polyimide (PI)-based flexible high-density electrodes, despite their popularity, suffer from high modulus and poor stretchability. These factors lead to conformability issues, particularly on complex human body surfaces such as the dorsal hand skin, causing motion artifacts, signal degradation, and in severe cases, signal loss due to electrode *** paper presents a thermoplastic polyurethane (TPU)-based high-density stretchable sEMG electrode for reliable EMG signal capture on complex surfaces. Performance comparisons between the proposed stretchable electrode (SE) and the traditional Pl-based flexible sEMG electrode (FE), focusing on time-domain waveforms and signal-to-noise ratio (SNR) under identical fixation conditions, indicate that SE maintains stable EMG signal acquisition across various hand postures. In contrast, the flexible electrode demonstrates signal weakness or loss under large deformations. Further, SNR analysis highlights SE's superior performance during high-intensity movements causing substantial skin deformation. Thus, the proposed SE emerges as a more convenient solution for stable and reliable hand EMG signal monitoring. © 2023 IEEE.

关键词： Electrodes

来源：评论

学校读者我要写书评

暂无评论

An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients’ Motor Execution from Multi-Channel EEG-Recordings

An Efficient Deep Transfer Learning Network for Characteriza...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Oluwarotimi Williams Samuel Mojisola Grace Asogbon Frank Kulwa Alistair A. McEwan Sunday Timothy Aboyeji Rami Khushaba Peng Fang Guanglin Li School of Computing and Data Science Research Centre University of Derby Derby United Kingdom Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS Shenzhen China Australian Centre for Field Robotics Sydney University Chippendale NSW Australia

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Rehabilitation Robots (IRR) that can effectively engage post-stroke patients (PSP) in intuitive motor training for full function recovery. Most existing rehabilitation robots incorporate functionalities that are passive in nature, constraining PSP to predetermined trajectories that often deviate from patients’ limb movement intentions, consequently hindering recovery. To resolve this issue, a robust deep-transfer learning driven network (DTLN) is developed to adequately characterize PSP’s motion intention signatures from neural oscillations towards achieving intuitive and active training. Thus, we investigated and proposed the utilization of mu-frequency spectrum (muFS) based CWT approach for Scalograms construction, which serves as inputs to the DTLN model that characterizes multiple classes of PSP’s motor execution signatures from multi-channel electroencephalography (EEG) recordings. Then, we evaluated the proposed method using EEG data from six PSP and compared the decoding results to those of related approaches under similar experimental settings. The proposed method resulted in a significant increment of 10.84 % - 13.19% decoding accuracy across stroke patients and better convergence in comparison to other methods. Additionally, the method exhibited distinct task separability for individual motor execution signature across patients. In conclusion, our method offers a consistently accurate decoding of motor tasks that could enable intuitively active robotic training in PSPs with impaired motor function.

关键词： Training Accuracy Transfer learning Stroke (medical condition) Motors Assistive robots Electroencephalography Decoding Trajectory Robots

来源：评论

学校读者我要写书评

暂无评论

A Spatial Feature Extraction Method for Enhancing Upper Limb Motion Intent Prediction in EMG-PR System

A Spatial Feature Extraction Method for Enhancing Upper Limb...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Boxing Peng Haoshi Zhang Xiangxin Li Yue Zheng Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

High-Density Surface Electromyography (HD-sEMG) enriches motion intention pattern recognition systems by providing more spatial information. Multichannel linear descriptors (MLD) could provide a comprehensive description of the overall state characteristics within the muscle regions. In this study, an MLD-based spatial feature extraction method was proposed to capture differences and correlations in various muscle regions during movement, ultimately enhancing the system’s classification accuracy. The performance of the feature extraction method was compared with traditional time domain feature extraction method under various classifiers and different movement types. The results show that employing the proposed method with the spatial features improves the classification error rates of combined movements from 11.14% to 7.28%, and better adaptability for all classifiers utilized in this study, which shows the effect of utilization of spatial information in different muscle regions.

关键词： Correlation Accuracy Error analysis Muscles Feature extraction Electromyography Data mining Time-domain analysis Engineering in medicine and biology

来源：评论

学校读者我要写书评

暂无评论

6DoF-ICTC: A Deep Image Feature Extraction Network Combining Transformers and CNNs for 6DoF Pose Estimation 12

6DoF-ICTC: A Deep Image Feature Extraction Network Combining...

引用

12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Zhang, Liming Zhou, Xin Zhu, Baixian Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In this work, we propose a new image feature extraction network for 6D pose estimation tasks. The majority of existing 6D pose estimation networks are based on the RGBD input, which is the fusion of images and point clouds. They are mainly working on keypoint extraction, point cloud feature extraction, and other tasks, however, image features, on the other hand, mainly rely on CNN networks that are currently advanced. With the development of Vision Transformer in the field of computer vision, the results on ImageNet demonstrate its benefit of extracting global features, and we see new directions that the Transformers can bring to the area of image feature extraction. ICTC is a Transformer paired with a CNN network that fuses the retrieved global features into each pixel and then does local feature extraction. This provides greater robustness to 6D pose estimation In complex situations such as shifting lighting conditions and object occlusion. Our method achieves 96% accuracy using the average ADD metric on the LineMOD dataset, demonstrating our method's effectiveness. © 2022 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Strenuous exercise-tolerance stretchable dry electrodes for continuous multi-channel electrophysiological monitoring

引用

npj Flexible Electronics 2022年第1期6卷 745-753页

作者： Ruijie Xie Qingsong Li Lijun Teng Zhengshuai Cao Fei Han Qiong Tian Jing Sun Yang Zhao Mei Yu Dianpeng Qi Peizhi Guo Guanglin Li Fengwei Huo Zhiyuan Liu CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences(CAS)Shenzhen 518055China The Institute of Flexible Electronics(IFE Future Technologies)Xiamen UniversityXiamen361005 FujianChina Key Laboratory of Flexible Electronics(KLOFE) School of Flexible Electronics(Future Technologies)Nanjing Tech University30 South Puzhu RoadNanjing 211816China Institute of Materials for Energy and Environment State Key Laboratory of Bio-fibers and Eco-textilesCollege of Materials Science and EngineeringQingdao UniversityQingdao 266071China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin 150001China

Electrophysiological monitoring under strenuous exercise by using stretchable dry electrodes is vital for healthcare monitoring,prosthetic control,human−machine interfaces and other biomedical ***,the existing dry electrodes are not applicable to the strenuous exercise situation that always involves both fast moving and profuse ***,we present a nano-thick porous stretchable dry electrode system with high stretchability and water *** system attaches conformably to the skin and stretches with it under Van der Waals forces even at sweating conditions,allowing the detection of electromyogram when moving with an acceleration of 10 g at a sweating rate of 2.8 mg cm^(−2) min^(−1).It is also capable of acquiring electrocardiogram and electroencephalogram *** strategy proposed would enable the biomedical studies and related applications with the requirement of stably recording electrophysiological signals under strenuous exercise scenarios.

关键词： acceleration recording enable

来源：评论

学校读者我要写书评

暂无评论

Effect of Camera Position on Knee Angle Extracted by Single RGB Camera Markerless Method of human Posture

Effect of Camera Position on Knee Angle Extracted by Single ...

引用

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Rui Hu Yanan Diao Juyi Sheng Yue Sun Yunkun Ning GenYuan Liu Yingchi Wang Guanglin Li Guoru Zhao CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Research Center for Neural Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China

Medical rehabilitation robots have shown strong potential for rehabilitation assessment and monitoring of the elderly. Accurate estimation of human posture based on video analysis can help the clinical application of medical rehabilitation robots. However, the current human pose estimation still needs to improve its accuracy; it is missing 3D posture information and has an unclear occlusion effect on accuracy. This paper proposes a markerless human pose measurement method based on a monocular RGB camera to find the knee joint angle using a 2D and 3D pose estimator. Eleven young people were recruited to participate in the experiments to study the effects of different camera positions on the joint angle extraction results. The results showed a camera angle of 45° was more effective in measuring sitting posture, with a standard deviation of 5.97 for the left leg. A camera angle of 90° was more effective in measuring gait, with a standard deviation of 9.34 for the left leg. In summary, the proposed method can fully use 3D posture information and consider the influence of camera angle on accuracy to improve posture estimation accuracy. The execution of this study is beneficial to promote medical rehabilitation robots to clinical applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Information Fusion-Based Distributional Reinforcement Learning for Autonomous Driving

Heterogeneous Information Fusion-Based Distributional Reinfo...

引用

International Conference on Intelligent Transportation

作者： Jianwen Yin Zhengmin Jiang Qingyi Liang Lei Peng Fenghua Zhu Jia Liu Huiyun Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Southern University of Science and Technology Shenzhen China Institute of Automation Chinese academy of sciences China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Autonomous driving has made significant advancements in recent years, with reinforcement learning emerging as a promising approach for developing high-level driving policies in urban traffic scenarios. Nevertheless, one challenge encountered when utilizing reinforcement learning is the issue of suboptimal and unstable driving policy due to the Q-value overestimation. To address this challenge, we propose a novel distributional reinforcement learning method that incorporates implicit quantiles into the actor-critic framework, thereby enabling a more accurate estimation of Q-values. Another issue is the inefficiency of sample learning. To enhance the representation learning of urban traffic scenarios and improve sample efficiency, we introduce a temporal-wise attention-based model that effectively aggregates heterogeneous types of state information. Through extensive experiments, our approach demonstrates superior performance when compared to the baselines on the NoCrash and CoRL benchmarks. The results show that our proposed method not only learns improved policies but also surpasses the baselines in dense traffic scenarios, as well as obtains comparative performance in other traffic scenarios.

关键词： Representation learning Accuracy Navigation Aggregates Decision making Estimation Benchmark testing Deep reinforcement learning Stability analysis Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Localizing epileptic focus and assessing electrical stimulus effects on epilepsy in rats using stretchable micro electrocorticogram electrodes

引用

Science China Materials 2022年第8期65卷 2244-2251页

作者： Qi Zhang Yang Zhao Lin Wang Mingyi Wei Mei Yu Jianping Huang Guanglin Li Renheng Wang Shiwei Du Zhiyuan Liu College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Neural Engineering CenterShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Neurosurgery Department Shenzhen University General HospitalShenzhen 518055China

Epilepsy is a chronic nervous disease with increasing incidence worldwide,while the accurate localization of epileptic focus and the corresponding treatment are still challenging due to the lack of effective tools to monitor and modulate the related brain neurological *** this work,stretchable micro electrocorticogram(mECoG)electrodes are developed and used to investigate penicillininduced epilepsy in *** electrodes possess excellent stretchability,conformality,anti-interference ability and sufficient resolution to successfully monitor electroencephalogram(EEG)signals,which is superior to traditional rigid polyimide-based *** epileptic spike waves are detected and analyzed to study the epileptic focus and electrical stimulus effects during epileptic *** is found that the spike waves occur first in the visual cortex which is likely to be the epileptic *** spike wave frequency quickly increases to 1.07 Hz where it reaches a plateau and remains *** is no dominant brain hemisphere that would show early warning of epileptic *** stimuli for various times are applied after administering *** is found that 15 min of electrical stimulus has the best restraining effect on epileptic *** mECoG electrodes developed in this study show potentials for applications in stretchable biomedical devices.

关键词： stretchable electrode stretchable electronics electrocorticogram epilepsy electrical stimulus

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：