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

作者： Sunday Timothy Aboyeji Xin Wang Yan Chen Ijaz Ahmad Lin Li Zhenzhen Liu Chen Yao Yu Zhang Guoru Zhao Guanglin Li Shixiong Chen Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China Surgery Division Epilepsy Center Shenzhen Children's Hospital Shenzhen China department of Neurosurgery The National Key Clinic Specialty Shenzhen Key Laboratory of Neurosurgery First Affiliated Hospital of Shenzhen University Shenzhen Second People's Hospital Shenzhen China Translational Medicine Center Naval Medical University Shanghai School of Medicine The Chinese University of Hong Kong Shenzhen Guangdong China

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

Deep learning, particularly convolutional neural networks (CNNs), is increasingly used for epileptic seizure detection, leveraging their ability to directly extract features from EEG signals. Since CNNs excel with image data, various methods have been developed to convert signals into images, including the Gramian Angular Field (GAF) transformation algorithm. The algorithm is subtyped into the Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF). Each of these transformation techniques has been employed individually in earlier studies. However, each method focuses on unique properties of the EEG signal during image construction. Therefore, we proposed a feature fusion of the two transformation methods using the CNN model to learn richer representations. The experiment was conducted on publicly available Bonn datasets. Initially, the datasets are segmented into epochs of length 0.5s and step size 0.2s. Images are generated for each of the segments, resulting in 8600 images for each set of Bonn datasets. The generated images from each of the transformations are fed into parallel CNNs, and the extracted features are fused for improved performance. We conducted binary, ternary, and multiclass (5-class) classifications. The results show that feature fusion of GASF and GADF improves the classification performance with an average accuracy increase of 0.255%, 0.295%, and 0.660% for binary, ternary, and multiclass, respectively. This feature fusion of the GAF transformation approach shows promising potential for distinguishing various EEG patterns, underscoring its potential in seizure classification tasks.

关键词： Deep learning Image segmentation Correlation Accuracy Assistive technologies Feature extraction Brain modeling Electroencephalography Convolutional neural networks

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UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement

UMLE: Unsupervised Multi-discriminator Network for Low Light...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yangyang Qu Kai Chen Chao Liu Yongsheng Ou Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems (#2019B121205007) CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China

Low-light image enhancement is a complex and vital task including, recovering color and texture details from low-light images. For automated driving, low-light scenarios will have severe implications for vision-based applications. To address this problem, we propose a real-time unsupervised generative adversarial network (GAN) with multiple discriminators. It includes a multi-scale discriminator, a texture discriminator, and a color discriminator to evaluate images from different perspectives. Furthermore, considering the uneven illumination distribution of images and the different information contained in the channels, we adopte a feature fusion attention module to combine channel attention with pixel attention to extract image features. Experiments show that our method outperforms state-of-the-art methods in qualitative and quantitative evaluation and provides visible improvements in SLAM localization effects.

关键词： Simultaneous localization and mapping Image color analysis Unified modeling language Lighting Feature extraction Generative adversarial networks Visual effects

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Multimodal Surface Material Classification Based on Ensemble Learning with Optimized Features

Multimodal Surface Material Classification Based on Ensemble...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Xiang Liu Hancheng Wu Senlin Fang Zhengkun Yi Xinyu Wu CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen

ISBN: (数字)9781728162676

ISBN: (纸本)9781728162683

In this paper, we propose a novel method for multimodal material classification based on ensemble learning and optimized features. The proposed method consists of three key steps. Firstly, we extract a set of features for each modality. Compared to existing methods, the extracted features are relatively simple but more effective when they are incorporated into the classifiers. Then, the feature selection algorithms, including Multi-Cluster Feature Selection (MCFS) and Laplacian Score (LS) are employed to reduce the feature dimension due to the curse of dimensionality. Finally, an ensemble learning method is proposed to integrate the merits of different feature selection methods. The effectiveness of the proposed method is demonstrated on the LMT-108 surface material dataset which includes multiple modalities such as sound, acceleration, and image. The experimental results have shown that our approach performs better than the competing methods.

关键词： Support vector machines Learning systems Laplace equations Image color analysis Feature extraction Acceleration Data mining

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CEBRA Method: Decoding Brain Activity for Advanced Brain-Computer Interface Technology

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

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

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines Brain Accuracy Stroke (medical condition) Feature extraction Motors Brain-computer interfaces Neurorehabilitation Decoding Paralysis

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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A method of Monocular Visual Odometry Combining Feature points and Pixel Gradient for Dynamic Scene

A method of Monocular Visual Odometry Combining Feature poin...

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IEEE Conference on Vehicular Technology (VTC)

作者： Panwei Li Huiyun Li Beizhang Chen CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

In the outdoor dynamic scene, the current Monocular Visual odometry methods of simultaneous localization and mapping system (SLAM) have Low utilization of image information, and they are not enough to meet the problem of stable image matching tracking. An information fusion method combining feature points and direct method gray matching method is proposed, which not only has no serious feature loss when demanding in fast motion, but also reduces the dependence on gray-scale invariant hypothesis. The proposed method also has better stability in the scenes with insufficient texture information, and the method reduces the dependence on gray-scale invariant hypothesis. Experimental results show that our novel front-end visual odometry simplifies the feature points, enriches the front-end utilization information, improves the pixel information of the image, and improves the feature points loss and excessive dependence on the intensity. The average real-time frame rate is around 32Hz, meeting the requirements of real-time performance. In the large-scale scene of the KITTI visual odometry datasets, the jitter effect of the dynamic scene is weakened, and the root mean square error of the positioning is 1. 79m, The average root mean square error of the method is reduced to 22.77% of ORB-SLAM, 26.48% of Semi-Direct Monocular Visual Odometry(SVO),and 37.68% of Large-scale direct SLAM(LSD).

关键词： Cameras Feature extraction Visual odometry Simultaneous localization and mapping Visualization Distortion Matrix decomposition

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Prognostic prediction of electroacupuncture for low back pain based on surface electromyographic map and CNN-SE model

Prognostic prediction of electroacupuncture for low back pai...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Pengxu Sun Xuemei Cao Weiwei Peng Guang Ouyang Peng Fang Guanglin Li Deqi Huang Naifu Jiang School of Electrical Engineering Xinjiang University Urumqi People's Republic of China Department of Acupuncture and Moxibustion Shenzhen Traditional Chinese Medicine Hospital Shenzhen People's Republic of China School of Psychology Shenzhen University Shenzhen People's Republic of China Faculty of Education University of Hong Kong Hong Kong SAR People's Republic of China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences Shenzhen People's Republic of China

ISBN: (数字)9781837241910

Chronic low back pain (CLBP) is a common and refractory musculoskeletal disease, which seriously affects patients' work and life. Electroacupuncture (EA) has become a popular method for the treatment of CLBP due to its advantages of high safety and few side effects, but its effect is still controversial. One of the important reasons is that individual differences make EA therapy not effective for everyone. Through machine learning method, the therapeutic effect prediction model was constructed from the surface electromyography (sEMG) data shows potential to help screen patients suitable for specific treatments efficiency. Thus, this study aims to establish a prediction model for EA treatment of CLBP by using convolutional neural network (CNN) based on sEMG topographic map. Each map was categorized with a label according to the changes of pain indicators. The results show that the prediction accuracy of CNN-SE (Squeeze-and-Excitation) model proposed in this study is 75%, which proves the potential of CNN-SE model to predict the efficacy of EA. This study initially confirmed the effectiveness of the EA effect prediction model for CLBP based on multi-channel sEMG signal, which is also of great significance for further research on the pathogenesis and treatment optimization of CLBP.

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Biplane Transrectal Ultrasound Probe Calibration using Dual-arm Robotic System with Multi-DOF End-effectors

Biplane Transrectal Ultrasound Probe Calibration using Dual-...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Jing Xiong Qiangyun Li Faizan Ahmad Changfu Xu Hao Deng Zeyang Xia Medical Robotics and Minimally Invasive Surgical Devices Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Soft Robotics Research Center Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Soft Robotics Research Center Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology CAS Shenzhen China

Transrectal Ultrasound (TRUS)-guided dual-arm robotic needle insertion system has improved clinical diagnosis treatment for biopsy and brachytherapy. It provides a highly precise and flexible method for inserting surgical needles. In the existing system, both the probe and needle are fixed to the robot’s end flange, which prevents the rotation and translation of the TRUS probe and needle from enabling multi-angle scanning and flexible needle insertion. In our previous work, we developed a dual-arm robotic needle insertion system with multi-degree-of-freedom (DOF) end-effectors to address the aforementioned issues. The calibration accuracy of the system directly determines the system’s effectiveness. However, existing calibration schemes are challenging to complete the calibration of the dual-arm robotic needle insertion system with multi-DOF end-effectors. Therefore, this paper presents a biplane TRUS probe calibration using dual-arm robotic needle insertion system with multi-DOF end-effectors. In the proposed approach, the kinematic of the multi-DOF probe and needle are first modeled to obtain the calibration parameters. Then the multi-DOF needle and probe are calibrated as the biplane TRUS image, which can be used to track the different motion states of the probe and needle. Finally, the experimental results are obtained and validated on the TRUS-guided dual-arm robotic needle insertion system. The results showed that the calibration accuracy of the overall system is 0.80±0.23 mm, which meets the clinical requirements.

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Dynamically reversible and strong circular dichroism based on Babinet-invertible chiral metasurfaces

arXiv

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arXiv 2021年

作者： Luo, Xiaoqing Hu, Fangrong Li, Guangyuan CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin541004 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

We propose a Babinet-invertible chiral metasurface for achieving dynamically reversible and strong circular dichroism (CD). The proposed metasurface is composed of VO2-metal hybrid structure, and when VO2 transits between the dielectric state and the metallic state, the metasurface unit cell switches between complementary structures that are designed according to the Babinet principle. This leads to a large and reversible CD tuning range between ±0.5 at 0.97 THz, which is larger than the literature. We attribute the CD effect to extrinsic chirality of the proposed metasurface. We envision that the Babinet-invertible chiral metasurface proposed here will advance the engineering of active and tunable chiro-optical devices and promote their applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Dichroism

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Broadband switchable terahertz half-/quarter-wave plate based on VO2-metal hybrid metasurface with over/underdamped transition

arXiv

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arXiv 2021年

Dynamically switchable half-/quarter-wave plates have recently been the focus in the terahertz regime. Conventional design philosophy leads to multilayer metamaterials or narrowband metasurfaces. Here we propose a novel design philosophy and a VO2-metal hybrid metasurface for achieving broadband dynamically switchable half-/quarter-wave plate (HWP/QWP) based on the transition from the overdamped to the underdamped resonance. Results show that, by varying the VO2 conductivity by three orders of magnitude, the proposed metasurface's function can be switched between an HWP with polarization conversion ratio larger than 96% and a QWP with ellipticity close to −1 over the broad working band of 0.8-1.2 THz. We expect that the proposed design philosophy will advance the engineering of metasurfaces for dynamically switchable functionalities beyond the terahertz regime. © 2021, CC0.

关键词： Vanadium dioxide

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