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

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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Finger joint angle estimation based on sEMG signals and deep learning method

Finger joint angle estimation based on sEMG signals and deep...

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

作者： Ma, Chenfei Guo, Weiyu Xu, Lisheng Li, Guanglin College of Medicine and Biological Information Engineering Northeastern University Liaoning Shenyang110819 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Conventional movement recognition methods are normally based on classification algorithms, which could only provide discrete movement classification rather than natural human body continuous movements. In this paper, we utilized the deep learning methods to estimate eight complicated movements of fingers by extracting the kinematic information based on surface electromyographic (sEMG) signals. Aiming at realizing continuous estimation, we adopted four representative models, AlexNet, Residual neural network (ResNet), Long Short-term Memory network (LSTM) and Gate Recurrent Unit (GRU) in this study. Convolutional kind models (AlexNet and ResNet) are chosen because of their irreplaceable feature extraction ability. And recurrent kind models (LSTM and GRU) are chosen because they are suitable for time-series signal processing. We took 10 degrees of freedom (DoFs) of joint angles from one hand as the target, 12 channels of sEMG as input and trained the models with the stochastic gradient descent and backpropagation. The models were tested on 8 abled subjects. The results indicated that the employed AlexNet turned out to show the best estimation performance and stability than other models. We realized the AlexNet is more suitable for sEMG based continuous movement estimation. © 2021 IEEE.

关键词： Long short-term memory

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SEMG-based gesture recognition by rotation forest-based extreme learning machine

SEMG-based gesture recognition by rotation forest-based extr...

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

作者： Peng, Fulai Chen, Cai Zhang, Xikun Wang, Xingwei Wang, Changpeng Wang, Lin Chinese Academy of Sciences Shandong Institute of Advanced Technology Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

The motion information contained in surface electromyography (sEMG) signals contributes significantly to the prosthetic hand control. However, the accuracy and speed of gesture recognition from sEMG signals are still insufficient for natural control. In order to alleviate this problem, this paper propose a rotation forest-based extreme learning machine method (RoF-ELM) to improve the recognition performance based on sEMG signals. Firstly, the active motion segments were picked out and pre-processed by sliding window. Then, 104 features were extracted from each sample, and SVM-RFE method was used to reduce the feature space dimension. Finally, the RoF-ELM classification model was constructed and tested based on the EMG data for gestures Data Set containing a total of 72 recordings, each of which consists of six basic gestures. 100 trials were implemented to validate the performance of the proposed method, the results show that the RoF-ELM method have the highest accuracy (91.11%) across different subjects with relatively short runtime compared with decision tree (DT), ELM, random forest (RF), and RoF methods. © 2021 IEEE.

关键词： Decision trees

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Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

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IET Cyber-systems and Robotics 2021年第1期3卷 77-88页

作者： Ejay Nsugbe Oluwarotimi Williams Samuel Mojisola Grace Asogbon Guanglin Li Independent Researcher Chinese Academy of Sciences(CAS)Key Laboratory of Human-Machine Intelligence-Synergy System Shenzhen Institutes of Advanced TechnologyShenzhenChina Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhenChina

In recent years,the electroencephalography(EEG)brain-computer interface(BCI)has been researched in the area of upper-limb prosthesis control due to the promise of being able to record neurological signals which follow activation patterns in the cortex directly from the brain with non-invasive *** is seen as a way of bypassing the limitation posed by acquiring neuromuscular signals predominantly with electromyography(EMG)directly from the stump,which possesses residual limb anatomy *** this study,the sequential forward selection algorithm to form a 10-optimal-channel representation,alongside an extended signal feature vector was applied,to investigate the motion intent decoding performance of EMG-only,EEG-only,and a fused EMG-EEG sensing configuration for four transhumeral amputees with varying stump *** results showed a considerable improvement for the EMG-only configuration with the advanced feature vector,but only a small increase for the EEG-only,and thus a marginal improvement when information from both signals was fused *** is likely due to the EEG requiring a greater number of channels spread across the skull to provide a reliable intent *** work will now involve optimisation studies to find a greater representation of electrode representation and parsimony,to minimise the number of channels while boosting motion intent decoding accuracy.

关键词： anatomy fused motion

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A Prosthetic Hand System with human-Like Grasping by Combining EMG and Visual Information 30

A Prosthetic Hand System with Human-Like Grasping by Combini...

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30th International Conference on Mechatronics and machine Vision in Practice, M2VIP 2024

作者： Zheng, Yue Chen, Xinxing Tian, Lan Li, Xiangxin Zhang, Haoshi Jiang, Xugang Li, Guanglin Fu, Chenglong Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems China

ISBN: (纸本)9798350391916

This paper proposed a novel prosthetic hand system integrated with a designed hybrid control method for achieving human-like grasping. The system design has been elaborated, including the mechanism design of a humanoid prosthetic hand equipped with multimodal sensing capabilities, a multi-channel EMG collector, a sensory feedback device, and a real-time central processing unit. A hybrid control method that combines electromyogram (EMG) and visual information was proposed to recognize the control intention of the system user. Preliminary verification of grasping experiments had been conducted, which proved the effectiveness of the prosthetic hand system and the hybrid control method. The development of this system aims to enhance the control performance and perceptual interaction capabilities of prosthetic hands while addressing limitations encountered in traditional myoelectric prosthetic hands. Besides, it could serve as a validation platform for assessing the control performance of diverse algorithms applied in practical applications. © 2024 IEEE.

关键词： Sensory feedback

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Movement Control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2618-2628页

作者： Wu, Xinyu Liu, Jia Zhou, Yimin Lv, Qin Hu, Chaofang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China

In this paper, a series of control algorithms have been designed for the climbing cloth robot, "Clothbot" to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equippedwith semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.

关键词： Climbing robot flexible surface linear quadratic regulator load observer sliding mode control with integral action

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A Multi-Scale Layer-Channel Attention Network for Image Super-Resolution

A Multi-Scale Layer-Channel Attention Network for Image Supe...

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2021 China Automation Congress, CAC 2021

作者： Wang, Jian Hirota, Kaoru Pan, Bei Dai, Yaping Jia, Zhiyang Jiang, Naifu School of Automation Beijing Institute of Technology Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781665426473

Recently, image super-resolution (SR) methods widely employ deep convolutional neural networks (CNNs) to improve the performance of reconstructed images through extracting informative features. However, the most of existing SR networks ignore the interdependencies among layers from different modules, which might hinder the further improvement of representational ability. In this paper, a newly designed multi-scale layer-channel attention network (MSLCAN) is proposed for single image super resolution (SISR) task. Our contributions are twofold. First, the layer-channel attention module (LCAM) can adaptively emphasize the correlations among different channels and interrelationships among multi-scale layers. Second, the multi-scale feature extraction module (MSFEM) effectively extracts adequate image information and enhances network ability of feature representation. In the experiments on five standard benchmarks, we have observed that our MSLCAN outperforms other SISR methods on quantitative and perceptual quality. © 2021 IEEE

关键词： Deep neural networks

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A novel multi-scale fourier descriptor based on plane orthogonal vectors for fast shape retrieval

A novel multi-scale fourier descriptor based on plane orthog...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Ma, Jiajun Zhu, Jiafeng Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong Province China

ISBN: (纸本)9781728163215

Shape retrieval is an interesting and challenging issue in computer vision and robot vision. The recent works employing Fourier-based multi-scale descriptors achieve some success in shape retrieval, especially make real-time application possible by improving the efficiency. However, these methods are commonly adopting really complex shape descriptors to present the shape information which is not intuitive enough. In our work, we propose a new Fourier-based method with novel multi-scale descriptor which is a combination of contour feature and regional feature. This novel descriptor has two main characteristics: First, utilize six different invariant signatures which are based on plane orthogonal vectors;Second, leverage multiple scale structures to describe local and global information of shapes. Except the easiness in understanding, this descriptor approach also produces a more distinct topology of shapes than other approaches which means the property of stability on variations, linear or nonlinear. The method is tested on some popular databases that provides a promising retrieval rate(87.46% and 99.27%) with constant time complexity optimization. The results confirm that our method is more capable of representing the topology of shapes than most other methods based on Fourier descriptor. © 2019 IEEE.

关键词： Topology

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Investigation of tibialis anterior muscle activation patterns during walking on different terrains

Investigation of tibialis anterior muscle activation pattern...

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2015 IET International Conference on Biomedical Image and Signal Processing, ICBISP 2015

作者： Zhou, Hui Wang, Yingying Ji, Ning Zhang, Haoshi Wang, Hui Chen, Shixiong Li, Guanglin Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong518005 China

ISBN: (纸本)9781785610448

Trapezoidal stimulation profile has been used for many years in clinical applications to activate the weak tibialis anterior muscle during gait for correcting foot drop. However, this stimulation profile could not well adapt to the different types of terrain, such as level ground, upstairs and downstairs. Thus an environment aware and adaptive stimulation mode would be needed to help drop foot patients walk normally in various environments. As a part of this new stimulation system, tibialis anterior (TA) muscle activation pattern during different locomotion modes were investigated in this study. Foot pressure signals from four force sensitive resistor (FSR) and TA muscle electromyogram (EMG) signal were recorded during gait from five normal subjects. Then the averaged TA muscle activation patterns for level ground, upstairs and downstairs were calculated from the EMG signals in the study, respectively. The results of the study could provide some helpful suggestions on how to design a suitable stimulation envelope for walking over different terrains.

关键词： Muscle

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Event-Triggered Group Attitude Coordinated Control of Multi-spacecraft System with Directed Topology

Event-Triggered Group Attitude Coordinated Control of Multi-...

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International Conference on Guidance, Navigation and Control, ICGNC 2020

作者： Zhou, Shaolei Wang, Shuailei Liu, Wei Wang, Donglai Gao, Xiangyang Naval Aviation University Yantai Shandong264001 China CAS Key Laboratory of human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen518055 China

ISBN: (纸本)9789811581540

Event-triggered group attitude coordinated control of multi-spacecraft system with directed topology is studied. The event-triggered strategy ensures that the system communicate at discrete time instants, thus the communication burden is reduced. Based on assumptions on the topology, the Laplacian matrix of the multi-spacecraft system is a block upper triangular matrix, and the nonnegative eigenvalues of it are positive integers, which can simplify the analysis process. With variable substitution and the Laplacian matrix decomposition, the problem is turned into a second-order group consensus problem, and then turned into a stability problem of a linear system. By properly chosen parameters, the system matrix of the linear system is Hurwitz. With centralized event-triggered mechanism, the control input proposed can lead the system to group attitude coordination, and Zeno behavior is avoided. Simulation results show that the variable substitution method utilized and controller designed in this paper are effective. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linear systems

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