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IEEE International Conference on Robotics and Biomimetics

作者： Wenze Shang Ruisen Huang Wei-Hsin Liao Xinyu Wu Fei Gao Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

sEMG signal is widely used and explored in control strategies of powered assistive human-robot interaction systems due to its non-invasive nature and ability to estimate motion intention well. However, prolonged use of the sEMG-based systems leads to muscle fatigue, contributing to unstable sEMG signals and regarding the performance of sEMG-based control strategies. In this study, three subjects, including two healthy subjects and one stroke subject, participated in our experiment, and the time domain, frequency domain, and complex features extracted from their $sEMG$ signals were utilized to evaluate muscle fatigue. The results indicate that $sEMG$ from the stroke side of the stroke subject contains more non-linear and chaotic parts, and the statistical differences between different trials are more significant, indicating that stroke has a more significant impact on the stroke side of the stroke subject. This study suggests that RMS in the stroke side of stroke subjects is less than that in others, whereas FuzzyEn in the stroke side of stroke subjects is greater than that in others, indicating that stroke leads to different motion patterns and more susceptibility to muscle fatigue.

关键词： Legged locomotion Limbs Frequency-domain analysis Biomimetics human-robot interaction Muscles Fatigue Feature extraction Time-domain analysis Robots

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TactCapsNet: Tactile capsule network for object hardness recognition

TactCapsNet: Tactile capsule network for object hardness rec...

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

作者： Fang, Senlin Mi, Tingting Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Wu, Hancheng Yi, Zhengkun Wu, Xinyu Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781665436786

Hardness is one of the most essential tactile clues for robots to recognize objects. However, methods for robots to recognize hardness are limited. In this paper, based on the Capsule Network (CapsNet), we propose a novel tactile capsule network (TactCapsNet) for object hardness recognition. Specifically, we collect a tactile dataset on the silicone samples with three different shapes, and the silicone samples of each shape have thirteen hardness levels ranging from 0A (Shore A scale) to 60A at 5A intervals. Furthermore, we construct the tactile image as the input of the CapsNet to make full use of the spatio-temporal information of the tactile hardness dataset. The experimental results prove that the proposed approach achieves higher accuracy and quadratic weighted kappa (QWK) than support vector machine (SVM), long short-term memory (LSTM), convolutional neural network (CNN), and CapsNet. © 2021 IEEE.

关键词： Hardness

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Terrain environment classification and recognition for soft lower-limb exosuit

Terrain environment classification and recognition for soft ...

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

作者： Ni, Jiangpeng Wang, Zhuo Li, Feng Su, Zhilong Cao, Wujing Chen, Chunjie Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Advanced Technology Chinese Academy of Sci-ences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Reliable environmental context prediction is critical for wearable robots (exoskeletons or prostheses) to assist terrain-adaptive locomotion. Inspired by the mechanism of human perception of the environment, the vision sensor can be introduced into the soft lower limb exosuit robot so that future environmental information can be used to improve the control. In this paper, a stable terrain classification and recognition system (TCRS) is designed for lower-limb soft exosuit robots. When the exosuit robot perceives the environment, it uses the residual network to train the terrain classifier to classify the terrain. During walking, the wearable soft exosuit robot collects the indoor environment, preprocesses, and marks the sampled images separately. The experimental results show that the classification accuracy is more than 90%, and the highest is 97.5%. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications. © 2021 IEEE.

关键词： Landforms

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Gesture Recognition Based on Attention and Dilated Causal Convolution

Gesture Recognition Based on Attention and Dilated Causal Co...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Zhou, Xin Liu, Jiaqing Huai, Yaping Ye, Jiancong 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 Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In recent years, based on surface electromyography (sEMG), great progress has been made in gesture recognition tasks, which is significant to the study of computer interaction and prosthesis control. Prior to this, many researches have focused on feature selection and time series modeling of data. However, it is difficult and complicated to manually perform feature extraction and feature selection on data. Besides, most of the time series modeling is based on recurrent neural networks with memory cell, that is, Long Short-term Memory (LSTM). Even though LSTM has memory gate, but cannot completely records historical information. In this paper, to address the above problems, we propose a Temporal Convolutional Network based on Attention mechanism (ATCN), in which Attention is used to characterize temporal features, and dilated causal convolution to capture more historical information. ATCN achieves 95.26% accuracy with this method on the public dataset NinaPro DB2, which exceeds the state-of-the-art accuracy of 5%. The experiments show that this approach is feasible and effective for gesture recognition tasks. Our contributions are as follows: Firstly, Attention mechanism is introduced into the field of prosthetic control. Then, ATCN is proposed for gesture recognition. Finally, ATCN achieves better results than LSTM on multiple public datasets. © 2022 IEEE.

关键词： Long short-term memory

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

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

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

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

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.

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The Changes of Cardiovascular Neurotransmitter Levels under Low-Intensity Focused Ultrasound Stimulation of the Vagus Nerve

The Changes of Cardiovascular Neurotransmitter Levels under ...

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

作者： Ning Ji Yuanheng Li Jingjing Wei Lei Huang Wan-Hua Lin Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Medicine and Biological Information Engineering Northeastern University Shenyang China Department of Human Anatomy Zhuhai Campus of Zunyi Medical University Zhuhai People’s Republic of China Department of Cardiovascular Surgery Peking University Shenzhen Hospital Shenzhen China

Background: Our previous study has shown that stimulation of the vagus nerve with low-intensity focused ultrasound could modulate blood pressure (BP), but the underlying mechanisms remain unclear. This study investigated the changes of cardiovascular neurotransmitter levels to indirectly evaluate the responses of the autonomic nervous system and renin-angiotensin system under low-intensity focused ultrasound stimulation (FUS) of the vagus nerve. Methods: Cardiovascular neurotransmitter levels of epinephrine (EPI), norepinephrine (NE), and angiotensin II (ANGII) were measured and compared before and after the FUS in seven spontaneously hypertensive rats; and were also measured and compared between a target stimulation group (FUS, n = 6) and non-target stimulation group (Control, n = 5) after stimulation to exclude the influence of potential confounding factors. Results: The t-test results showed that the levels of EPI, NE, and ANGII were significantly decreased (P < 0.05) after stimulation compared to before stimulation. Additionally, the levels of NE and EPI were significantly lower (P < 0.05) in the FUS group than in the Control group after stimulation, indicating that the activities of the sympathetic nervous system and renin-angiotensin system of the vagus nerve might be inhibited by FUS of the vagus nerve. Conclusion: These findings reveal the mechanism of BP lowing in response to FUS of the vagus *** Relevance—This study revealed the mechanism of BP lowering in response to focused ultrasound stimulation of the vagus nerve through analyzing the changes of cardiovascular neurotransmitter levels.

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A motion planning method based on HRL for autonomous exoskeleton

A motion planning method based on HRL for autonomous exoskel...

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

作者： Dong, Yao He, Yong Su, Zhilong Ni, Jiangpeng Feng, Wei Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

With the increase of the world's population and the serious aging phenomenon, more and more people with mobility impairments need technology to assist them in their daily lives. Autonomous Exoskeleton are one of the important technologies. The existing gait generation algorithm are most based on simplified mathematical model calculations, which have the problem of poor accuracy and unfriendliness to the wearer. This research aims to propose a path planning and gait generation policies (HPPO) based on hierarchical reinforcement learning (HRL), so that the wearer can safely avoid obstacles and maintain dynamic balance when using the autonomous exoskeleton controlled by this policy to walk. The high-level controller (HL) is responsible for path planning, it can quickly plan a reasonable exoskeleton footholds in the indoor environment. The low-level controller (LL) can imitate a given reference trajectory, learn a walking policy suitable for the exoskeleton, and it can follow the planned footholds, so as to achieve the purpose of avoiding obstacles for the wearer. The algorithm is verified in simulation environment. © 2021 IEEE.

关键词： Motion planning

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Polarization-independent resonant lattice Kerker effect in phase-change metasurface

arXiv

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arXiv 2022年

作者： Xiong, Lei Luo, Xiaoqing Ding, Hongwei Lu, Yuanfu Li, Guangyuan School of Information Science and Engineering Yunnan University Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

Resonant lattice Kerker effect in periodic resonators is one of the most interesting generalizations of the Kerker effect that relates to various fascinating functionalities such as scattering management and Huygens metasurfaces. However, so far this effect has been shown to be sensitive to the incident polarization, restricting its applications. Here, we report, for the first time, polarization-independent resonant lattice Kerker effect in metasurfaces composed of periodic Ge2Se2Te5(GST) disks. For such a metasurface of square lattice, the spectrally overlap of the electric dipole and magnetic dipole surface lattice resonances can be realized by choosing an appropriate GST crystalline fraction regardless of the incident polarization. The operation wavelength and the required GST crystalline fraction can be conveniently tuned over large ranges since these parameters scale linearly with the disk size and the lattice period, greatly facilitating the design. Making use of the obtained resonant lattice Kerker effect, we realize a reconfigurable and polarization-independent lattice Huygens' metasurface with a dynamic phase modulation of close to 2π and high transmittance. This work will advance the engineering of the resonant lattice Kerker effect and promote its applications in phase modulation and wavefront control. Copyright © 2022, The Authors. All rights reserved.

关键词： Tellurium compounds

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Adaptive Stepwise Feature Selection Approach for EEG-Based Epileptic Seizure Classification 46

Adaptive Stepwise Feature Selection Approach for EEG-Based E...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Aboyeji, Sunday Timothy Zhou, Wenfang Tao, Yuan Zhu, Mingxing Samuel, Oluwarotimi Williams Ahmad, Ijaz Zhao, Guoru Yi, Ji Tong, Michael Chi Fai Wang, Xin Guo, Yi Chen, Shixiong Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China Luohu District People's Hospital Department of Otolaryngology Shenzhen China Peking University Shenzhen Hospital Department of Otorhinolaryngology Shenzhen China Harbin Institute of Technology Shenzhen School of Electronics and Information Engineering Shenzhen China University of Derby School of Computing and Engineering Derby United Kingdom Johns Hopkins University School of Medicine Department of Ophthalmology Baltimore United States The Chinese University of Hong Kong Department of Otorhinolaryngology Head and Neck Surgery Hong Kong Shenzhen People's Hospital Department of Neurology Shenzhen China The Chinese University of Hong Kong Shenzhen School of Medicine Shenzhen China

ISBN: (纸本)9798350371499

Recent advancements in feature selection (FS) optimization algorithms have influenced the field of epileptic seizure classification. However, integrating these optimization algorithms into machine learning (ML) models often creates time complexity, limiting their clinical deployment. To address this issue, we propose an innovative adaptive stepwise FS method tailored for epileptic seizure detection (ESD). First, a discrete wavelet transform (DWT) was applied to the preprocessed signal to get three levels of the db4 wavelet family within the frequency range pertinent to epileptic seizure classification. Linear and nonlinear features are then extracted from each level of the DWT. The selected features are initially ranked using the minimum relevance, maximum redundancy (mRMR) FS technique. After that, a stepwise FS approach was applied to the ranked features to optimize the performance of Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector machine (SVM) classifiers. The experiment was performed on a publicly accessible CHB-MIT dataset in a patient-independent approach. The model's performance was assessed using accuracy, sensitivity, and specificity. The results show an improved performance of the ML models with the integration of stepwise algorithm into the mRMR technique. Among the classifiers, RF exhibited superior performance with accuracy, sensitivity, and specificity of 87.69%, 91.53%, and 83.86%, respectively, when 12 features were selected. Our proposed stepwise feature selection method (PSFS) performs similarly to generalize forward feature selection (GFFS), with an average accuracy of 88.37% and 88.57%, respectively across selected features with less computation. This makes PSFS a very efficient and effective FS in epileptic seizure classification. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Evoking and Evaluation of Non-invasive Sensory Feedback for Applications in Neural Rehabilitation: A Pilot Study

Evoking and Evaluation of Non-invasive Sensory Feedback for ...

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

作者： Yuanzhe Dong Xi Tang Naifu Jiang Jun Xie Wenyuan Liang Peng Shang Guanglin Li Peng Fang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems & the Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China School of Mechanical Engineering Xi’an Jiaotong University Xi’an China National Research Center for Rehabilitation Technical Aids Beijing China

Protheses are very useful for limb amputees to restore their lost motor functions. However, most commercially available prostheses lack an intuitive and natural sensory feedback function, which may strongly limit the performance of prosthetic control and lowers the acceptance of amputees. As a non-invasive stimulation method, Transcutaneous Electrical Nerve Stimulation (TENS) has been proven to effectively evoke intuitive and natural sensations, which is practical for upper-limb amputees in clinical applications. In this paper, we explored the relationship between TENS modalities and induced sensory information based on able-bodied subjects by using electroencephalogram (EEG). We computed the Event-related Potential (ERP) and Brain Electrical Activity Mapping (BEAM) to evaluate the performance of sensation evoking. The results demonstrated that the TENS applied on wrist areas with proper parameters could stably induce multiple types of intuitive sensations of different fingers, including touching, vibrating, and flapping, and the mapping relationship between stimulation modality and sensory feedback were objectively verified and evaluated with EEG. This pilot study might provide a support for future researches on restoration of sensory paths and feedback for upper-limb amputees.

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