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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Su, Tong Song, Chengqun Cheng, Jun CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong Hong Kong Wuhan Research Institute of Post and Telecomunication Wuhan China

ISBN: (纸本)9789811966125

Fine-grained visual classification focus on accurately identifying the subordinate categories from a base class. One key of this task is to find discriminative local parts. Convolutional neural network-based methods using attention mechanism can enhance the representation of local regions and improve the classification accuracy. Recently, vision transformer has shown great potential in traditional image classification with its self-attention mechanism, which can grab important regions. However, one main difficulty in fine-grained visual classification is the subtle inter-class variance. Different classes may share the same or similar features and these features may be identified by attention. This discriminative information is redundant for related classes and may damage the performance of network. To this end, we apply information bottleneck to vision transformer training to relieve the interference of redundant information. The experiments demonstrate the effectiveness of this simple method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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High-resolution and large-size stretchable electrodes based on patterned silver nanowires composites

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Nano Research 2022年第5期15卷 4590-4598页

作者： Yong Lin Qingsong Li Chen Ding Jiayi Wang Wei Yuan Zhiyuan Liu Wenming Su Zheng Cui Printable Electronics Research Centre Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences(CAS)Shenzhen 518055China School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefei 230026China

Electrodes based on composites of silver nanowires(AgNWs)and elastic polymers have been widely studied and applied in various stretchable electronic ***,due to the high aspect ratio of nanowires,the patterning of AgNW-based composite electrodes remains a huge challenge,especially for high-resolution complex circuit wiring on large-size elastic *** this paper,we propose a method for preparing large-size stretchable circuit boards with high-resolution electrodes by the combination of screen printing and vacuum filtration of AgNWs/polydimethylsiloxane(PDMS)*** as-prepared stretchable electrodes have smooth edges with patterning resolution up to-50 μ*** conductivity of the composite electrode can be precisely controlled by varying deposition densities of AgNWs and have reached to 1.07 x 10^(4) S/cm when the deposition density was 2.0 mg/cm^(2).In addition,the uniformity of conductivity and the resistance-strain characteristics of composite electrodes were systematically evaluated with different AgNWs deposition *** composite electrodes have been successfully employed to construct a large-size programmable display system and an 18-channel surface electromyography(EMG)recording,showing great potentials for some strain-insensitive stretchable circuits in wearable and health-related electronic applications.

关键词： AgNWs patterning silver nanowires AgNWs/PDMS composite stretchable circuit board high-density electrode array

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Toward Improving the Performance of Hand Movement Intention Recognition for Upper Limb Amputees Using Flexible Ultrasound Transducers 17

Toward Improving the Performance of Hand Movement Intention ...

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17th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2024

作者： Peng, Xinhao Oyemakinde, Tolulope Tofunmi Wang, Mengtao Zhao, Hang Tian, Lan Zheng, Yue Guo, Yao Ma, Teng Liu, Zhiyuan Li, Guanglin Li, Xiangxin Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen People's Hospital China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9798350355154

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. © 2024 IEEE.

关键词： Ultrasonic transducers

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A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG signals

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-12页

作者： Ahmad, Ijaz Wang, Xin Javeed, Danish Kumar, Prabhat Samuel, Oluwarotimi Williams Chen, Shixiong CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques. IEEE

关键词： Feature extraction

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Instant Motion Recognition of Lower Limb Using Surface Electromyography Signal Based on Convolutional Neural Network

Instant Motion Recognition of Lower Limb Using Surface Elect...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Li, Xiaohui Yu, Xiaoyue Zhou, Hao Jin, Dingxun Li, Guanglin Wang, Lin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems China University of Chinese Academy of Sciences China Shenzhen Lower Limb Rehabilitation Intelligent Aids Engineering Research Center China Shenzhen Technology University China Southern University of Science and Technology China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781665481090

The field of rehabilitation robotics has emerged as a prominent area of research in the medical community in recent years, offering innovative and promising solutions for patients with physical disabilities. The existing lower limb rehabilitation robots have some shortcomings in human-machine compatibility and motion synergy due to the limitation of the real-time human motion intention recognition. In this paper, an instant motion recognition of the lower limb was proposed using surface electromyography (sEMG) for significantly shortening the time delay between the robot motion and the actual human motion. The first 200ms data of the complete sEMG signal for a single motion cycle was employed in the recognition algorithm. The proposed recognition algorithm demonstrated superior accuracy compared to traditional machine learning methods. The accuracy of 9-class recognition was 94.32%, while 12-class recognition achieved that of 89.32%. A data augmentation method based on magnitude warping followed by shifting was employed to enhance recognition accuracy by 11.37% in 9-class recognition and 19.23% in 12-class recognition when comparing with no augmentation. The optimal results for motion recognition were observed when the first 200ms data of the complete sEMG signal for a single motion cycle with a duration of 1.0s were utilized, as evidenced by comparative results. The proposed method offers a novel approach to enhancing human-machine compatibility and comfort in the application of lower limb rehabilitation robots in the future. © 2024 IEEE.

关键词： Convolutional neural networks

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Mastering Cooperative Driving Strategy in Complex Scenarios using Multi-Agent Reinforcement Learning

Mastering Cooperative Driving Strategy in Complex Scenarios ...

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

作者： Liang, Qingyi Jiang, Zhengmin Yin, Jianwen Xu, Kun Pan, Zhongming Dang, Shaobo Liu, Jia Southern University of Science and Technology Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350327182

With the advent of machine learning, several autonomous driving tasks have become easier to accomplish. Nonetheless, the proliferation of autonomous vehicles in urban traffic scenarios has precipitated other challenges, such as cooperative driving. Multi-Agent Reinforcement Learning (MARL) approaches have emerged as a promising solution, as they can successfully address the challenge by simulating the interactive relationship among autonomous vehicles (AVs). In this paper, we leverage Social Value Orientation to depict the behavioral tendencies of AVs, thereby enhancing the performance of MARL approaches. We also incorporate different scale features in the policy network to strengthen its representation ability. Moreover, effective reward functions are designed based on traffic efficiency, comfort, safety, and strategy. Finally, we validate our approach in an open-source autonomous driving simulator. Simulation results indicate that our proposed approach outperforms IPPO and MAPPO algorithms in terms of success rate, route completion rate, crash rate, and other metrics. © 2023 IEEE.

关键词： Autonomous vehicles

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A Novel Carbon Paste Electrode for Convenient and Efficient Auditory Brainstem Response Acquisition: A Pilot Study 1

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15th International Conference on Sensing Technology, ICST 2022

作者： Wang, Xin Tian, Qiong Xu, Yangjie Zhu, Mingxing Wang, Yingying He, Yuchao Chen, Shixiong Liu, Zhiyuan Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen518055 China

ISBN: (数字)9783031298714

ISBN: (纸本)9783031298707

Auditory Brainstem Response (ABR) is the commonly used tool in Otology for clinical hearing loss and disease diagnoses. Wet electrodes, served as the gold standard, are widely used in clinical settings to acquire ABR signals for superior performance. However, it is a complex and time-consuming process to fix the electrodes by applying bandages or adhesive tapes. Moreover, wet electrodes might cause allergy and uncomfortable for patients, especially for children. Therefore, a novel anhydrous carbon paste electrode (CPE) with adhesion and biocompatibility was proposed to solve the aforementioned problems. The impedance of unfixed CPE and Conductive Paste-based electrode was recorded, and the ABR waveforms obtained by both two methods were systematically compared to further investigate and verify the feasibility of the proposed unfixed CPE. The results showed that the proposed CPE could achieve high-quality ABR recording under unfixed condition, which was comparable with the gold standard method. Besides, the analyses of the latency and amplitude of the ABRs showed that unfixed CPE could acquire higher amplitudes which would help with wave recognition, while the latency was similar. This proved that the unfixed CPE could obtain ABR with the same information compared with that of Paste-based ABR, since the physicians always made the diagnosis by analyzing the latency. Furtherly, we compared the correlation coefficient of the ABRs under different averaging times, and the results showed that unfixed CPE was comparable, even better than the conventional method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electrodes

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Latency Localization of Auditory Brainstem Response by Different Deep Learning Methods

Latency Localization of Auditory Brainstem Response by Diffe...

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

作者： Ji, Junyu Wang, Xin Jia, Desheng Zhao, Chunrui He, Yuchao Ahmad, Ijaz Zhu, Mingxing Pan, Hongguang Chen, Shixiong Li, Guanglin University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Children's Hospital Department of Otolaryngology Shenzhen518033 China Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Harbin Institute of Technology School of Electronics and Information Engineering Shenzhen518055 China

ISBN: (纸本)9798350327182

Auditory brainstem response (ABR) is an evoked potential generated by the auditory brainstem nuclei in response to auditory stimuli. Hearing loss or impairment in the auditory pathway can be objectively reflected by extracting the latencies of the peaks of characteristic waves (especially wave V) in the clinic. The commonly used clinical method of peak localization is the manual labeling approach by subjective visualization. However, this method is time-consuming and highly dependent on the experience of the clinician. In this study, three different types of neural networks were proposed to realize the automatic intelligent recognition of the wave V position. Firstly, during the data preprocessing, the ABR data of humans was clipped and normalized. Then three types of neural networks, namely DNN, CNN, and RNN, were proposed for the latency localization of wave V. The performances of these three networks were further evaluated under different sound pressure levels and different error scales. The results showed that DNN was the best network for localizing the wave V position. The highest overall accuracy rate was 91.70% and the error scale was 0.1ms. This study showed that the proposed automatic method with neural networks can greatly reduce the workload of medical personnel in finding the ABR latencies and can assist doctors in further diagnosis. © 2023 IEEE.

关键词： Deep learning

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Heterogeneous Information Fusion-Based Distributional Reinforcement Learning for Autonomous Driving 27

Heterogeneous Information Fusion-Based Distributional Reinfo...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Yin, Jianwen Jiang, Zhengmin Liang, Qingyi Peng, Lei Zhu, Fenghua Liu, Jia Li, Huiyun Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Southern University of Science and Technology Shenzhen518055 China Institute of Automation Chinese Academy of Sciences 100190 China

ISBN: (纸本)9798331505929

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. © 2024 IEEE.

关键词： Contrastive Learning

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Spasticity assessment with muscle coactivation of elbow flexors during passive stretch in Post-stroke Hemiplegia

Spasticity assessment with muscle coactivation of elbow flex...

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

作者： Hui Wang Yanjuan Geng Peng Fang Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China

the assessment of muscle properties is an essential prerequisite in the treatment of post-stroke muscle spasticity. Previous studies have shown that muscle coactivation, which reflects the simultaneous activation of agonist and antagonist muscle groups, is associated with muscle spasticity during voluntary contraction. However, current spasticity assessment approaches do not often consider muscle coactivation for passive contraction measured with surface electromyography (sEMG). The purpose here is to evaluate the validity and reliability of muscle co-activation based on sEMG for assessing spasticity of post-stroke patients. This study was conducted on 39 chronic hemiplegia post-stroke patients with varying degrees of elbow flexor spasticity. The severity of spasticity was assessed with Modified Ashworth Scale (MAS). The patients produced elbow flexion passively on affected arm. Two-channel surface sEMG recordings were acquired simultaneously for the biceps and triceps muscles. The effectiveness and reliability of the EMG-based spasticity assessment method were evaluated using Spearman's correlation analysis and intra class correlation coefficients (ICCs). The results showed that there was a statistically significant positive relationship between the level of activity and the coactivation index (R=0.710, P=0.003), while the ICCs for intra trial measures ranged between 0.928 and 0.976. Muscle coactivation is a promising tool for continuously quantifying muscle spasticity in post-stroke patients, suggesting that the EMG-based muscle coactivation index could be useful for assessing motor function.

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