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

作者： Zhang, Zhewen Cao, Wujing Chen, Chunjie Yu, Hongliu Wu, Xinyu Meng, Qiaoling Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology

ISBN: (纸本)9781665436786

In this article, a rehabilitation exoskeleton assisted by lateral walking is designed to help patients with lower limb movement disorders to solve the problem of lateral walking assistance, so as to achieve the effect of rehabilitation training. Based on the characteristics of lateral movement of human body, the exoskeleton designed in this paper uses linear push rod motor and thigh binding to assist hip abduction and adduction respectively. Based on the research of human transverse movement gait, a reasonable linkage mechanism was designed and kinematics simulation was carried out. The dynamics model of human lower limbs was established and calculated with MATLAB simulation software. the moment of hip joint lateral movement is obtained. Finally, the structural parameters of the driving mechanism were analyzed by dynamic simulation, and the simulation results were compared with the target motion function to verify the rationality of the design. © 2021 IEEE.

关键词： MATLAB

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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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Asynchronous steady-state visual evoked potential brain-computer interface application: True and false positive rate comparison between with and without eye-tracking switch paradigms

Asynchronous steady-state visual evoked potential brain-comp...

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

作者： Xie, Jun Zhang, Huanqing Liu, Yi Fang, Peng Yu, Hongwei He, Liushi Li, Guanglin Yuan, Song Xu, Guanghua Li, Min School of Mechanical Engineering Xi'An Jiaotong University Xi'an710049 China Beijing Institute of Astronautical Systems Engineering Beijing100076 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Shenzhen518055 China

ISBN: (纸本)9781665436786

Due to the bypass of muscle activity or peripheral nerve control, brain-computer interface (BCI) technique has advantages in different fields such as medical field for the rehabilitation of paralyzed patients. Steady-state visual evoked potential (SSVEP) has been widely adopted in BCI applications. SSVEP based BCIs have the advantages of high information transfer rate, less or no need of training, and strong anti-interference, which could be used in the more natural asynchronous BCI application with control of the users rather than the operant system of the synchronous mode. In order to solve the problem of high false positive rate (FPR) in common asynchronous SSVEP BCI applications, this paper proposed an eye-tracking switch based BCI paradigm to reduce the FPR and to improve the performance of the asynchronous BCI system. In the proposed paradigm, the fixation point position instead of EEG signal is used to determine whether the system is in idle state. Experimental results showed that when eye-tracking switch was applied in the asynchronous SSVEP BCI, the FPR was reduced to less than 10% and the recognition accuracy (i.e., the true positive rate, TPR) can also be improved to a certain extent, which proved the applicability of the eye-tracking switch in asynchronous BCI applications. © 2021 IEEE.

关键词： Brain computer interface

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Control Method of Constant Force Grinding based on Active and Passive Control*

Control Method of Constant Force Grinding based on Active an...

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IEEE International Conference on Robotics and Biomimetics

作者： Kunlun Li Yu Sun Meng Li Xiaozhi Qi Yucheng he Yu Wang Xiaojun Yang Harbin Institute of Technology Shenzhen CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences

In recent years, bone cutting tool-ultrasound osteotome has been used in the treatment of spinal stenosis. However, the contact force has a great influence on its cutting performance. The cutting speed will be reduced, if the connecting force is too small or too excessive. At present, the clamping devices at the end of the surgical auxiliary robot are mostly rigid connections, which will lead to the instability of the contact force between the surgical instruments and the vertebrae and the inability to achieve efficient and safe grinding. In this paper, a flexible force stabilizer for clamping ultrasonic bone knives is designed based on active-passive combined control mode. A flexible element is added in the ending grasp device to eliminate the sudden change of the contact force. An adaptive fuzzy controller is designed outside the loop to adjust the ideal contact force between the osteotome and the lamina according to the difference with the actual contact force for real-time adjustment. Through comparison experiments, the validity and feasibility of the active-passive combined hybrid control method for the contact force control of the end of the bone knife was verified.

关键词：

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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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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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A multi-task learning convolutional neural network for object pose estimation

A multi-task learning convolutional neural network for objec...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Wang, Yurui Jin, Shaokun Ou, Yongsheng Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Beijing China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

Estimating 6D poses of objects from RGB images is very crucial for robots to interact with the surrounding environment and to cooperate with humans. It is a challenging problem due to the various shapes of objects, the occlusions among objects, as well as the complexity of the scene. In this study, we present a new multi-task convolutional neural network for 6D object pose estimation, which also learns the object region in the image to further improve the estimation result. A weighted loss computed from the training samples is adopted to guarantee that the components of the estimated pose are equally accurate. Additionally, we sythesize new virtual data based on the existing training data so as to overcome the possible adverse situation caused by insufficiency of training data. Experiments on the YCB-video dataset are undertaken to validate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Semantic Segmentation

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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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A Novel Gesture Recognition Technique based on sEMG Armband and Leap Motion Controller

A Novel Gesture Recognition Technique based on sEMG Armband ...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Huanqing Xie, Jun Xiao, Yi Yang, Yuzhe Ren, Zhiyuan Zhang, Ruiwen Dong, Yuanzhe Tang, Xi Li, Guanglin Fang, Peng School of Mechanical Engineering Xi'An Jiaotong University Xi'an710049 China China Astronauts Research and Training Center National Key Laboratory of Human Factors Engineering Beijing100094 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

With the rapid development of human-computer interaction technology, it has become very common to use various sensors for gesture recognition to control machines or to complete specified tasks. This study proposes a fusion method that uses Leap Motion for gesture recognition and gForcePro+ to evaluate actual force of hands. A three-dimensional (3-D) virtual gesture recognition platform was designed in Unity. Leap Motion was used to control gestures in the virtual environment, and surface electromyography (sEMG) data was used to distinguish different forces of the same gesture. The success of grasping objects was judged by detecting and analyzing the sEMG signals and Leap Motion signals, combined with the size of the grasping objects. Experimental results showed that Leap Motion and gForcePro+ can be used together to control hand motion. In addition, the proposed system can be used to distinguish the same gesture with different force sizes, which can reflect different forces when the hand grasp objects with the same action. It was proved that the fusion technique of gesture recognition system based on Leap Motion and sEMG is effective for online gesture recognition. © 2022 IEEE.

关键词： Gesture recognition

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