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

作者： Fangliang Yang Chunjie Chen Zhuo Wang Yinsheng Xu Yao Liu Xinyu Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab 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 Guilin University of Technology Guilin China

For wearable soft exosuits, an imprecise control strategy can easily injure the wearer, while real-time terrain recognition and accurate gait phase estimation can effectively improve the control strategy of the exosuit and make the wearer comfortable and safe. In this paper, a real-time terrain recognition algorithm based on Transformer and a gait estimation algorithm based on CNN-LSTM-Attention are implemented. The Transformer-based recognition of different terrains improves the recognition accuracy to some extent, and the CNN-LSTM-Attention based feature extraction for temporal signals such as gait phase is also extremely noticeable. Experiments show that the Transformer-based algorithm achieves 99.64 % recognition accuracy in six different terrain environments. In the gait phase estimation experiment, CNN-LSTM-CBAM achieved the best performance with an evaluation index r2 of 0.9221. The aforementioned terrain recognition algorithms and gait phase estimation algorithms may have a positive impact on soft exosuit and dynamic prosthetics research.

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A Tendon-Driven Actuator with Cantilever Initiated Variable Stiffness Used for Robotic Fingers

SSRN

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SSRN 2024年

作者： Li, Chenning Yang, Shangkui Branson, David T. Song, Zhibin Sun, Tao Dai, Jiansheng Kang, Rongjie Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin University Tianjin300072 China Advanced Manufacturing Technology Research Group Faculty of Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Beijing Institute of Technology China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen518055 China

Variable stiffness actuators (VSAs) have emerged as a key actuation technology known for their bionic performance and task adaptability. However, current VSAs often exhibit relatively large sizes, making them possible for use in robotic arms and legs but less convenient for integrations into robotic hands. This paper introduces a compact design of a tendon-driven variable stiffness actuator (TVSA) based on an adjustable cantilever mechanism, which can be embedded into a robotic finger. This implementation endows the robotic finger with the decoupled regulation of joint position and stiffness. A concise and computationally efficient stiffness mapping model from the TVSA to the finger joints is then established, providing a theoretical foundation for the stiffness regulation of the tendon-driven fingers. A prototype of a robotic hand equipped with the presented TVSA demonstrates safe interactions with various objects with diverse shapes, weights and stiffness. © 2024, The Authors. All rights reserved.

关键词： Stiffness

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Pulsed Microfluid Force-Based On-Chip Modular Fabrication for Liver Lobule-Like 3D Cellular Models

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Cyborg and Bionic systems 2021年第1期2021卷 222-233页

作者： J.Cui H.P.Wang Q.Shi T.Sun Science and Technology on Electronic Test and Measurement Laboratory North University of ChinaTaiyuan 030051China Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China

In vitro three-dimensional(3D)cellular models with native tissue-like architectures and functions have potential as alternatives to human tissues in regenerative medicine and drug ***,it is difficult to replicate liver constructs that mimic in vivo microenvironments using current approaches in tissue engineering because of the vessel-embedded 3D structure and complex cell distribution of the *** paper reports a pulsed microflow-based on-chip 3D assembly method to construct 3D liver lobule-like models that replicate the spatial structure and functions of the liver *** heterogeneous cell-laden assembly units with hierarchical cell distribution are fabricated through multistep photopatterning of different cell-laden *** fluid force interaction by pulsed microflow,the hierarchical assembly units are driven to a stack,layer by layer,and thus spatially assemble into 3D cellular models in the closed liquid chamber of the assembly *** 3D models with liver lobule-like hexagonal morphology and radial cell distribution allow the dynamic perfusion culture to maintain high cell viability and functional expression during long-term culture in *** results demonstrate that the fabricated 3D liver lobule-like models are promising for drug testing and the study of individual diagnoses and treatments.

关键词： culture maintain spatially

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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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The measuring ZMP of self-balancing exoskeleton robot is calibrated by using the neural network

The measuring ZMP of self-balancing exoskeleton robot is cal...

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

作者： Xu, Yang Xiao, Yang Ma, Yue Zheng, Liangsheng He, Yong Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665436786

The exoskeleton robot is an auxiliary device to help the disabled people walk, and the self-balancing exoskeleton robot is one which is to keep balance without the assistance of external crutches. In order to keep the balance of the self-balancing exoskeleton robot, it is necessary to get the position of the Zero Moment Point by measuring the pressure of the footplate, and make the position of ZMP in range of supporting area. In this experiment, the footplate is used with the double-deck structure, this structure is compared with the single-deck structure, the double-dack structure will not lose the information of the collected ZMP without direct touch with the sensor, and it is lighter than another structure with dozens of sensors. But there is an inevitable structural coupling in the double-deck structure, which makes the ZMP have a large measurement error. In order to solve this problem, a novel idea is proposed, with the help of the powerful processing and learning capabilities of the neural network, four kinds of neural networks are used to calibrate measured position of ZMP so that reducing error of the measured ZMP. By comparing position of the actual ZMP before and after the calibration with the ideal position of ZMP and computing the errors to judge the effect of the calibration. Through experimental comparison, it is concluded that the different neural networks eliminate error of the measured ZMP in different extent. When the GRNN neural network is used to calibrate position of ZMP, the effect is the most ideal. © 2021 IEEE.

关键词： Errors

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

作者： Ma, Jiajun Liang, Guoyuan Liang, Yu Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology 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

3D facial similarity measure is a critical research direction in fields such as 3D face recognition, craniofacial reconstruction, and heritability estimation, etc. and represents a challenging and meaningful task. In this paper, we propose a novel method based on deformation field to establish the similarity relationship between 3D faces. The main idea is divided into two steps as follows: (1) The landmarks are semi-automatically marked on all faces to build correspondences between landmarks (2) According to the distance field and the dense corresponding relationship of faces, a deformation field is generated between 3D faces that deform the source model to the target model. The path of deformation is computed to measure the similarity between two 3D faces. Comprehensive experiments conducted on the BU3DFE database illustrate that the similarity measure method is consistent with the subjective evaluation and provides high discriminability for different individuals. © 2021 IEEE.

关键词： Deformation

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A method for recognition of dynamic hand gestures based on wrist tendon sounds 22

A method for recognition of dynamic hand gestures based on w...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： He, Bailin Wang, Can Wang, Hao Li, Mengyao Duan, Shengcai Wu, Xinyu Guangdong Provincial Key Laboratory 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 Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Samsung Electronics Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781728162676

Hand gesture recognition are widely applied in game industry and rehabilitation engineering. Some recognition methods based on physiological data are attracting attention. In this paper, a novel method for dynamic hand gesture recognition based on sounds of wrist tendon is proposed. Bone conduction sensors which automatically filter the ambient noise is utilized to collect the wrist tendon sounds of three types of dynamic hand gestures. Continuous Wavelet Transform(CWT) is utilized extract the features of sounds and Support Vector Machine(SVM) is utilized to train the classifier model. Five healthy subjects participated the experiment and the average test accuracy of three classes achieves 92.33%. The proposed method provides an effective perspective for hand gesture recognition. © 2021 IEEE.

关键词： Support vector machines

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A comprehensive channel and feature selection method for myoelectric pattern recognition 22

A comprehensive channel and feature selection method for myo...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Li, Mengyao Ma, Yue Zheng, Liangsheng Wang, Can Feng, Wei Wu, Xinyu Guangdong Provincial Key Laboratory 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 Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781728162676

The advent of myoelectric control schemes provides promising chances for locomotion empowerment and restoration of those with disabilities. Despite substantial efforts have been made into advancing sEMG-based motion recognition, it may be a little tricky to determine appropriate muscles and features for people with muscle disorders or different muscle use preferences. To mitigate it, an advantageous sEMG channel and feature selection method based on ReliefF algorithm was proposed. Related experiments were conducted on a eight able-bodied subject database to showcase the feasibility and efficiency of the proposed approach, that is, considerably high classification performance was maintained with the original feature set reduced by more than half. Ulteriorly, we also investigated the influences of different number of neighbors or features on classification accuracy for ascertaining the optimal values. The strengths of our proposed method lie in not only customizing channel and feature selection for individual users, but also offering preliminary insight for a general mapping mechanism between human muscles and corresponding motions. © 2021 IEEE.

关键词： Feature Selection

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Modeling and closed-loop control of ferromagnetic nanoparticles microrobots 22

Modeling and closed-loop control of ferromagnetic nanopartic...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Hao, Zhiming Xu, Tiantian Huang, Chenyang Lai, Zhengyu Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Information Technology Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing China Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781728162676

Microrobots capable of performing minimally invasive surgery, targeted drug delivery, and manipulation of micro-objects have shown great potentials in multiple application areas. However, microrobots assembled by nanoparticles with stable configuration and good performance on closed-loop control are still to be further studied. In this paper, ferromagnetic nanoparticles are used as experimental materials, rather than paramagnetic nanoparticles that require complex synthesis processes, and the advantage is demonstrated, for example, high pattern stability. The locomotion velocity as a function of the magnetic field frequency is modeled, analyzed and verified by experiment. Moreover, a path following experiment based on the arbitrary planar path following algorithm is performed. The nanoparticle microrobots are of great significance for biomedical applications. In the future works, experiments in bio-fluids, multimodal locomotion, and targeted drug delivery tasks will be investigated. © 2021 IEEE.

关键词： Targeted drug delivery

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Chemically Bonded Flexible Pressure Sensor Based on Divergent Metallic Grid with High Linearity, Low Hysteresis, and Excellent Stability

SSRN

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SSRN 2024年

作者： Luo, Guoxi Zhang, Bingcheng Xu, Yu Pi, Diying Li, Min Yang, Ping Zhao, Libo Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai Yantai265503 China School of Instrument Science and Technology Xi'an Jiaotong University Xi’an710049 China School of Mechanical Engineering Xi'an Jiaotong University Xi’an710049 China Revopoint 3D Technologies Inc Xi’an710199 China Chongqing Key Laboratory of Micro-Nano Systems and Intelligent Sensing Chongqing Academician Workstation Chongqing 2011 Collaborative Innovation Center of Micro/Nano Sensing and Intelligent Ecological Internet of Things Chongqing Technology and Business University Chongqing400067 China

As intelligent robots and smart wearable electronics become commonplace, the demand for flexible pressure sensors is increasing rapidly. Despite the enormous effort in the development of flexible pressure sensor with high sensitivity, limited progress has been made in terms of developing a flexible pressure sensor with high linearity, low hysteresis, and high stability to avoid additional signal processing and signal distortion, and meanwhile promise good accuracy. Herein, we developed a flexible pressure sensor on foundation of the metallic strain effect. The divergent metallic grid was manufactured on polymide (PI) film with the mature magnetron sputtering and lift-off method, and a novel chemical bonding between PI and outer covering polydimethylsiloxane (PDMS) was developed for a flexible encapsulation. Benefiting from the excellent intrinsic properties of rigid sensitive materials and high-strength bonding. The as-fabricated pressure sensors exhibited several superior properties that are characterized by high linearity (linear regression coefficient of R2 = 0.991) in whole sensing range, low hysteresis (2.78%), rapid response/relaxation time (ca. 29/21 ms), remarkable durability (over 13,000 bending and stretching cycles), and excellent stability. The integrated merits of this novel pressure sensor were demonstrated in applications including human physiological signal monitoring, material identification, and pressure space mapping. © 2024, The Authors. All rights reserved.

关键词： Pressure sensors

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