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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Liu, Guodong Jiang, Guolai Xiong, Rong Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China 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-Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two sub-networks. The first sub-network is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. The second is a fully convolutional deep network, which reconstructions the depth map to original resolution. The two sub-networks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods. © 2019 IEEE.

关键词： Convolution

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Terrain recognition and gait cycle prediction using IMU

Terrain recognition and gait cycle prediction using IMU

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

作者： Wang, Zhuo Zhang, Yu Ni, Jiangpeng Wu, Xinyu Liu, Yida Ye, Xin Chen, Chunjie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SZ DJI Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9781665436786

It is well known that terrain recognition and gait cycle prediction are important for powered exoskeleton. However, only a few works have focused on the concerns of complexity of the control system caused by using redundant sensors. In this paper, only two IMU sensors are applied to collect information of the angle and angular velocity of the hip joint in the situation of level-ground walking, ramp ascent, and ramp descent. Based on information acquired from these two IMU sensors, two methods are proposed to achieve terrain recognition. One method uses the angle of the hip joint when the two legs intersect as the threshold of terrain recognition. It can identify the terrain (level-ground walking, ramp ascent, ramp descent) during stable walking, but it cannot recognize the transitional terrain (from level-ground walking to ramp ascent, from ramp ascent to ramp descent, and so on) and its robustness is limited. The other method selects the angle and angular velocity of the hip joints as the eigenvector, and uses SVM for terrain recognition. The accuracy of terrain recognition is improved from 69.7% to 100% after introducing the Gaussian kernel function instead of Linear kernel function. For gait cycle prediction, Wiener one step prediction is applied in predicting the GC. Compared to actual GC, the error from predicted GC based on mean prediction is more than 8.0%, while the error from Wiener on step prediction is less than 4.35%. © 2021 IEEE.

关键词： Forecasting

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Variation of sEMG with Muscle Fatigue for Lower Limb Stroke Patient During Walking

Variation of sEMG with Muscle Fatigue for Lower Limb Stroke ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Shang, Wenze Huang, Ruisen Liao, Wei-Hsin Wu, Xinyu Gao, Fei Southern University of Science and Technology Shenzhen518005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong999077 Hong Kong

ISBN: (纸本)9781665481090

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. © 2024 IEEE.

关键词： Human robot interaction

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Neural learning of stable dynamical systems based on extreme learning machine

Neural learning of stable dynamical systems based on extreme...

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International Conference on Information and Automation (ICIA)

作者： Jianbing Hu Zining Yang Zhiyang Wang Xinyu Wu Yongsheng Ou Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Chinese University of Hong Kong China Key Lab of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences

This paper presents a method based on extreme learning machine to learn motions from human demonstrations. We model a motion as an autonomous dynamical system and define sufficient conditions to ensure the global stability at the target. A detailed theoretic analysis is proposed on the constraints regarding to input and output weights which yields a globally stable reproduction of demonstrations. We solve the corresponding optimization problem using nonlinear programming and evaluate it on an available data set and a real robot. Combined with the generalization capacities of extreme learning machine, the results show that the human movement strategies within demonstrations can be generalized well.

关键词： Asymptotic stability Stability analysis Trajectory Robot kinematics Mathematical model Optimization

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Using Thin-Film Piezoelectret to Detect Tactile and Slip Signals for Restoring Sensation of Prosthetic Hands

Using Thin-Film Piezoelectret to Detect Tactile and Slip Sig...

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

作者： Peng Fang Lan Tian Yue Zheng Jianping Huang Guanglin Li Key Laboratory of Human-Machine-Intelligence Synergic System of Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Chinese Academy of Sciences Beijing Beijing CN

Most of the currently available prosthetic hands do not have a proper sensation of touching and slipping. Thus it is not easy for arm amputees to grasp objects properly only with an assistance of visual feedback. In this pilot work, a sensor based on thin-film piezoelectret was used to detect the possible tactile and slip information of a prosthetic hand. The piezoelectret sensor is flexible and is able to be bended, and therefore it could be properly mounted on the surface of prosthetic finger. Our preliminary results demonstrated that both the tactile and slip information could be acquired with the same sensor unit. For a grasp without slippage, the tactile signal was usually a single large peak, whereas the slip signal was a series of vibrations in a small range. Thus these two types of signals could be easily separated based on their different characteristics. This study suggested that by using thin-film piezoelectret sensor, a primary control with involuntary feedback might be achieved for the present prosthetic hands. More studies would be required on the detailed signal processing and control strategy for the restoration of sensation function in prosthetic hands.

关键词： Prosthetic hand Force Thumb Robot sensing systems Piezoelectric transducers Electromyography

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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Optimization of a Piezoelectric Bending Beam-based Human Knee Energy Harvester

Optimization of a Piezoelectric Bending Beam-based Human Kne...

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IEEE International Conference on Mechatronics and Automation

作者： Haisu Liao Yidi Wang Qiuyu Shi Ruisen Huang Fei Gao 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 System

The concept of extracting energy from the human body to recharge depleted energy sources has gained significant attention. Recently, scientists have focused on harnessing biomechanical energy from the human body while walking. The aim of this study is to optimize a piezoelectric bending beam-based human knee energy harvester to enhance performance. Specifically, the shape of the bending beam (the substrate: a carbon fiber plate) is optimized to modulate the deformation, such that the average strain of the piezoelectric macro fiber composites (MFC) attached to the bending beam is improved to generate more electricity. In addition, the effects of the substrate’s thickness and the hinge base’s height for the bending beam on the output power are also analyzed. Experimental testing on a subject is conducted to validate our design and test the performance. The harvester generates an average power output of 24.55 mW when walking at 4 km/h on a treadmill. The harvester’s total weight is 278g.

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∊-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks

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Electronic Commerce Research and Applications 2021年 50卷 101105-101105页

作者： Tian, Hu Zheng, Xiaolong Zhang, Xingwei Zeng, Daniel Dajun The State of Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

With the explosive growth of social networks, privacy preservation as a social good has been one common concern. Graph neural networks (GNNs) have been utilized by social network service providers to improve business service. However, traditional anonymization techniques of social networks cannot satisfy the desired privacy preservation of node attribute and graph structure and introduce information disturbance from the anonymization, leading to the performance degradation of GNNs in social network analysis. To protect sensitive user data and persist GNNs’ performance in social network analysis, we propose a two-stage privacy-preserving method of graph neural networks in the social network domain. During the first stage, we design a novel ∊-k anonymization method that can achieve ∊-local differential privacy (∊-LDP) and k-degree anonymity by incorporating the classical LDP and k-degree anonymization (k-DA) while retaining as much network community information as possible. At the second stage, we develop an adversarial training mechanism for GNNs to resist the disturbance from ∊-k anonymization and retain as much task performance as possible on anonymous social network data. Comprehensive experiments on several real-world social network datasets demonstrate the effectiveness of the proposed method for privacy-preserving node classification, link prediction, and graph clustering in social networks. The proposed method represents an interesting and important combination of classical anonymous technologies and recent GNNs and can preserve user privacy while providing business service. © 2021 Elsevier B.V.

关键词： Graph neural networks

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system development for micro-electrode assembly of cochlear implant

System development for micro-electrode assembly of cochlear ...

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

作者： Shaokui Weng Weibin Rong Zeyang Xia Hao Deng Yangzhou Gan Jing Xiong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology

ISBN: (纸本)9781467389600

Cochlear Implant is an implantable medical device for severe deafness patients to restore hearing function. The micro-electrode in cochlear implant is directly contacted with the human cochlea for signals transmission, thus its assembly is an important step of cochlear implant production. However, currently micro-electrode is assembled manually and the assembly precision and efficiency are unable to meet the industrial standard. In this paper, an automatic assembly system of micro-electrode for Cochlear Implant was designed. In this system, several mechanical mechanism specially for the micron level parts transmission and assembly were designed, including a wire feeder, a ring transmission mechanism, and a welding mechanism. And combined with the human-computer interaction interface, the effectiveness of the overall performance of the assembly system was verified by experiments.

关键词： Wires Welding Cochlear implants Assembly systems Tungsten Needles Electrodes

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Dissolved oxygen control system based on the T-S fuzzy neural network

Dissolved oxygen control system based on the T-S fuzzy neura...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wen-Tao Fu Jun-Fei Qiao Gai-Tang Han Meng College of Electronic Information and Control Engineering Beijing University of Technology Beijing CN College of Electronic Information and Control Engineering Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

In this paper, a novel kind of the dissolved oxygen (DO) concentration control system was proposed based on the T-S fuzzy neural network. The proposed T-S fuzzy neural network controller was used to control the DO concentration in the Benchmark Simulation Model No.1 (BSM1) wastewater treatment platform. The parameters of the neural network were adjusted online through the error back propagation algorithm to get the minimum error. By adjusting the learning rate online, the convergence speed of the system was accelerated, and then the DO concentration in the wastewater treatment system was controlled fast and efficiently in real-time. Compared with BP and PID controllers through the digital simulation, the results showed that the control effect of the DO concentration based on T-S fuzzy neural network control system was better. Besides, the test results under three kinds of weather condition showed that better adaptability and robustness were also gained in this control system.

关键词： Fuzzy control Benchmark testing Artificial neural networks Recycling Neurons

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