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IEEE International Conference on Robotics and Biomimetics

作者： Qiuyu Shi Fajiang Liu Xinyu Wu Fei Gao Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Magnetorheological (MR) rotary brakes leverage the magnetically controllable rheological properties of MR fluids to provide damping torque in lower limb assistance devices. This paper utilizes an optimization algorithm (Whale Optimization Algorithm (WOA)) to optimize the geometric dimension of the MR brake to enhance field-dependent brake torque. First, the mechanical design and optimization process of the MR brake were outlined. Then, the magnetic density of the MR fluid is evaluated using the ANSYS platform. With that, the WOA was exploited to optimize key design parameters across multiple iterations. According to the simulated results, the torque was improved from 17.39 Nm to 25.89 Nm, which increased by 48%. Finally, a prototype with the initial dimension was fabricated and preliminarily tested.

关键词： Stator cores Torque Fluids Shape Magnetomechanical effects Prototypes Magnetic liquids Whale optimization algorithms Brakes Optimization

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Cam Profile Optimization for a Piezoelectric-Based Sole Energy Harvester

Cam Profile Optimization for a Piezoelectric-Based Sole Ener...

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IEEE International Conference on Robotics and Biomimetics

作者： Haisu Liao Zhongliang Ren Yidi Wang 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 Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Harvesting energy from the human body to extend the operational time for low-power has gained significant attention. Recently, researchers have focused on biomechanical energy harvesting from human feet while walking. This paper presents an optimization of the cam profile for a piezoelectric-based sole energy harvester designed to be worn on the bottom of shoes to power electronic devices. Specifically, cams with linear polynomial and exponential functions were designed to actuate the piezoelectric transducers (PZTs). Force analysis revealed that the exponential function cam induces more significant deformation in the PZTs than the linear polynomial cam. Preliminary tests on a healthy subject validated this design, showing that the exponential function cam produced an average output power of 0.16mW at a walking speed of 5.5 km/h, indicating a 78% improvement over the linear polynomial cam. Moreover, the optimization rate increased with the growing walking speed.

关键词： Legged locomotion Deformation Force Voltage Polynomials Energy harvesting Power generation Optimization Lead zirconate titanate Testing

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A Pilot Study on Coupling Behaviors of Limb Movements and Brain Activation Based on EEG and fNIRS Synchronization Information

A Pilot Study on Coupling Behaviors of Limb Movements and Br...

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

作者： Xi Tang Qingge Li Yao Tang Lan Tian Yue Zheng Xiangxin Li Naifu Jiang Peng Shang Guanglin Li Liang Peng 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 State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Motion is one of the basic physiological functions of human beings. However, many brain diseases such as stroke may cause different degrees of motor dysfunctions for patients. As a commonly used rehabilitation method, active and passive exercise training may enhance patients’ neuromuscular functions and recover their motor abilities. It is known that limb movements are strongly coupled with brain activation but there is currently insufficient exploration on the coupling behaviors from the perspective of informatics. In this study, the coupling relationship between limb movements and brain activation was preliminarily studied based on three healthy subjects. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals were synchronously collected during lower limb movements, and time-frequency analysis (TFA) and transfer entropy (TE) analysis were performed to quantitatively study the brain activation behaviors. In the experiments, a desynchronization phenomenon of μ rhythm in EEG was observed during exercise states, and the experimental results demonstrate the activation rule of motor and prefrontal cortexes upon limb movements. Calculations show that there exists a bidirectional flow of information between EEG and cerebral oxygen metabolism signals, but with a difference between different directions. This work may support the rehabilitation for patients with motor dysfunctions with a guidance of quantitative indicators and also benefit the exploration on neuroscience.

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CEBRA Method: Decoding Brain Activity for Advanced Brain-Computer Interface Technology 46

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

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

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

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 © 2024 IEEE.

关键词： Patient rehabilitation

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Attention mechanism-based monocular depth estimation and visual odometry

Attention mechanism-based monocular depth estimation and vis...

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

作者： Zhang, Qieshi Lin, Dian Ren, Ziliang Kang, Yuhang Wu, Fuxiang Cheng, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen China Chinese University of Hong Kong Hong Kong Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9781665436786

In recent years, visual-inertial simultaneous localization and mapping (SLAM) have been widely researched and used. The monocular vision-based depth estimation and visual odometry (VO) plays a significant role in the SLAM system because of its low cost and high efficiency, which can be used to analyze the indoor environment for intelligent move robot application. Existing methods generally aim at the difference in pixel contrast between the previous and later frames to obtain the environment structure and camera pose. But the indifferent attention to the overall image makes the network spend extra computing power and hard to obtain the ideal results. For overcoming these disadvantages, an attention mechanism is proposed on basis of the original network and modified some convolution methods to make the network pay more attention to the key areas in the image and improve the accuracy of the estimation. The proposed approach is evaluated on the KITTI dataset, compared with the state-of-the-art methods achieves better results. © 2021 IEEE.

关键词： Vision

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Design and preliminary testing of a piezoelectric-based sole energy harvester

Design and preliminary testing of a piezoelectric-based sole...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Wang, Yidi Zhao, Xuan Shi, Qiuyu Liao, Haisu Wu, Xinyu Gao, Fei Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin999077 Hong Kong

ISBN: (纸本)9781665481090

Harvesting energy from the human body to replenish existing energy sources is receiving substantial attention. Recently scientists have been focusing on harvesting biomechanical energy from human feet during walking. This paper develops a piezoelectric-based sole energy harvester that is designed to be worn on the bottom of shoes to harvest energy, thus powering wearable electronic devices. Specifically, a cam with piezoelectric(PZT) stacks is mounted on the heel to scavenge energy during the heel strike. The cam can change the input's direction from the vertical direction to the horizontal direction. In addition, the output power of this part can be optimized or improved by generating a specified cam profile. A bimorph piezoelectric plate is installed on the forefoot to convert the toes' bending deformation into electricity. Herein, piezoelectric Macro Fiber Composites (MFC) are employed to capture the large deformation. Preliminary testing on a healthy subject is conducted to validate this design. The average power output of the harvester is 8.83 m W. © 2022 IEEE.

关键词： Cams

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A Novel Metric based on Bootstrapping Approach for sEMG Signal Quality Assessment Towards Robust Decoding of Lower Limb Locomotion

A Novel Metric based on Bootstrapping Approach for sEMG Sign...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Fangning Tan Wenhao Wei Yuanzhe Dong Zhenyu Sun Xu Yong Oluwarotimi Williams Samuel Guanglin Li Chinese Academy of Sciences (CAS) Key Laboratory of Human- Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) CAS Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China department of Mechanical Engineering and Intelligent Systems The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo Japan

With the development of machine learning algorithms, the accuracy of Surface Electromyography (sEMG)based lower limb movement intention recognition has been dramatically improved. Unfortunately, the limitation of sEMG signal acquisition techniques caused by the acquisition instrument and instability between electrode and skin introduces unavoidable noises. In this regard, researchers have proposed different noise reduction methods without adequate means upon which they can be objectively evaluated and compared in terms of their performance. Therefore, we recorded the sEMG data of 3 subjects with diverse characteristics to properly compare different noise reduction algorithms’ performance. In addition, we proposed a Bootstrapbased Signal Quality Assessment Metric (Bootstrap-based SQAM) method to adequately evaluate and compare two essential sEMG denoising methods that include Butterworth and Wavelet-based denoising. The experimental results show that the resulting signal preprocessed by wavelet denoising yielded a higher signal quality with an overall frame-based segmental signal-to-noise-ration (SNRs) mean of20.16 dB compared to 0.08 dB for the Butterworth-based denoising. Afterward, we used the decoding accuracy of lower limb locomotion to validate the proposed SQAM, and the wavelet-based denoising method yielded an average accuracy of 94.60%, which is 7.30% higher than that of the Butterworth-based denoising for lower limb motion classification using the SVM classifier. The higher classification accuracy of the resulting signal preprocessed by wavelet-based denoising proves that SQAM proposed in this paper reflects a better means of assessing signal quality for decoding lower extremity motion intention.

关键词： Measurement Support vector machines machine learning algorithms Instruments Noise reduction Skin Decoding

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Significant loss suppression and large induced chirality via cooperative near- and far-field coupling in plasmonic dimer nanoantennas

arXiv

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arXiv 2024年

作者： Luo, Xiaoqing Huang, Rixing Lei, Dangyuan Li, Guangyuan CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Materials Science and Engineering City University of Hong Kong Hong Kong999077 Hong Kong Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

Plasmonic nanoantennas containing nano-gaps support "hotspots" for greatly enhanced light-matter interactions, but suffer from inherent high losses, a long-standing issue that hinders practical applications. Here we report a strategy to significantly suppress the losses of plasmonic dimer nanoantennas. Specifically, by introducing the concept of cooperative near- and far-field coupling, we observed an unprecedented transition from the weak coupling of localized resonances to strong coupling of collective (nonlocal) resonances, showing robustness to the gap distance between the dimer. We develop a generalized lattice sum approximation model to describe this transition and reveal its origins: the off-diagonal element of the anisotropic polarizability tensor due to near-field coupling, and the anisotropic lattice sums due to far-field coupling. This strong coupling leads to loss-suppressed plasmonic resonances with large modulation depths and meanwhile extremely high measured quality factors up to 3120 in the near-infrared regime, exceeding the record in the near infrared regime. Additionally, high-Q and large chiroptical responses can also be induced for achiral planar dimers under the critical coupling condition. This work paves an avenue toward extremely low-loss plasmonic devices, either chiral or not, for diverse important applications. © 2024, CC0.

关键词： Infrared devices

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A Novel Multi-Stream Informer Used for Lower Extremity Joint Angle Estimation 12

A Novel Multi-Stream Informer Used for Lower Extremity Joint...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Zhou, Xin Zhang, Liming Liu, Jiaqing 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 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 The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In lower extremity exoskeleton rehabilitation systems, synergy and proportionality between the human lower extremity and the exoskeleton robot have been a critical goal to pursue. In recent years, changeable deep learning models based on multi-source signals have been used for lower limb continuous motion estimation. During the process, multimode signals are fed into the time processing model as time series. However, there are some deficiencies in this framework, on the one hand, what kind of multi-source combination can be more efficient;on the other hand, most of the existing models need several iterations or network stacking to complete the extraction of temporal information. This study set out to address the above problems, we put forward Multi-Stream Informer (MS-Informer), using Multi-Stream combination and sparse attention mechanisms for lower limb continuous motion estimation. In the experiment, we compare MS-Informer and Temporal Convolutional Network (TCN) and Long Short-term Memory (LSTM) under distinctive multi-source combinations. In summary, MS-informer performs well in multiple evaluations compared with other models. Experimental results illustrate that the MS-Informer is more efficacious in lower limb joint angle estimation and is a promising method for achieving coordinated control of lower limb exoskeletons. © 2022 IEEE.

关键词： Motion estimation

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Vision-based soldering process parameters calculation for robotic soldering

Vision-based soldering process parameters calculation for ro...

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

作者： Ren, Hao Wu, Xinyu Shang, Wanfeng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

Robotic soldering has emerged as one of the key technologies for the through-hole technology (THT) assembly process. However, the determination of the soldering process parameters in those systems are still depends on engineer's experience. In this paper, we propose an automatic vision-based soldering process parameters determination method based on a mathematical model of standard solder joint. The system framework of this method and the detailed process of solder feeding volume calculation are also given. The proposed method is verified by experiments and compared with the soldering process parameters in published literature. Finally, we also analyze that the average computing time of the proposed method for new solder joints is 31.39ms, which is much faster than previous manual debugging method. © 2021 IEEE.

关键词： Soldering

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