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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Su, Tong Song, Chengqun Cheng, Jun CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong Hong Kong Wuhan Research Institute of Post and Telecomunication Wuhan China

ISBN: (纸本)9789811966125

Fine-grained visual classification focus on accurately identifying the subordinate categories from a base class. One key of this task is to find discriminative local parts. Convolutional neural network-based methods using attention mechanism can enhance the representation of local regions and improve the classification accuracy. Recently, vision transformer has shown great potential in traditional image classification with its self-attention mechanism, which can grab important regions. However, one main difficulty in fine-grained visual classification is the subtle inter-class variance. Different classes may share the same or similar features and these features may be identified by attention. This discriminative information is redundant for related classes and may damage the performance of network. To this end, we apply information bottleneck to vision transformer training to relieve the interference of redundant information. The experiments demonstrate the effectiveness of this simple method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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A quantitative biomechanical study for precise orthopedic intervention in idiopathic scoliosis

A quantitative biomechanical study for precise orthopedic in...

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

作者： Feng, Ruliang Tao, Huiren Ye, Canhua Li, Guanglin Bai, Xueling Wang, Lin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macau Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Institute of Biomedical and Health Engineering Chinese Academy of Sciences Center for Translational Medicine Research and Development Shenzhen518055 China Shenzhen University General Hospital Department of Spine Surgery Shenzhen China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

The scoliosis with a prevalence of about 2.4% has become the top 3 major 'killer' of children and adolescents' health. Only 0.02% of scoliosis would be severe enough to require surgical intervention, while the rest was treated with orthotics or exercise training. However, the location of the orthotic forces and its effects were still not clear, resulting in a great deal of blindness in the fabrication of precise individualized orthoses and the later applied orthopedic forces. In this paper, we built a 3D spine model of a patient with idiopathic scoliosis based on CT tomography data, applied different orthopedic forces to the spine model to compare the results and clarify the relationship between them in order to determine the optimal location and magnitude of the orthopedic force, which were necessary for precise interventions in patients. The present results showed that 1) the greater the applied force, the better the correction effect (within reasonable limits) and 2) the effect of multiple forces applied for correction was better than that of a single force applied, as reflected by a greater displacement of the vertebrae and almost identical mean Von Mises stress in the discs, which could support the production of effective personalized orthopedic robots. © 2022 IEEE.

关键词： Finite element method

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A Control Method With Terrain Classification and Recognition for Lower Limb Soft Exosuit

A Control Method With Terrain Classification and Recognition...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Jiangpeng Ni Chunjie Chen Zhuo Wang Youfu Liu Xinyu Wu Yida Liu CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (数字)9781728162676

ISBN: (纸本)9781728162683

Soft Exosuit is a kind of Lower-limb wearable robots to augment and assist the wearer's performance. The wearer need different assistance modes to reduce the metabolic rate when walking on different terrains. Therefore, assistance modes need to be selected according to different terrains for the wearer of soft Exosuit. Recently, our team has designed a stable terrain classification and recognition system (TCRS) for the soft Exosuit to discriminate terrains and estimating environmental features. Through this system, soft Exosuit can perceive the environment to auxiliary control of the locomotion modes. A depth sensor with an inertial measurement unit(IMU) can acquire to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from the origin 3D point cloud. Then, they are classified to estimate terrain environmental features, including the incline angle of the slope, the width, and the stairs' height. Finally, the TCRS was evaluated by classifying and recognizing five basic terrains in three different experimental scenarios while the subject was wearing the soft Exosuit with the TCRS module. The results show that the terrain classification accuracy rate reaches 97.74 %, and the environmental features estimation error is less than 15 %. The promising results indicate the robustness and the potential application of the presented TCRS to provide proper auxiliary force to reduce the metabolic rate of wearers on different terrains.

关键词： Three-dimensional displays Measurement units Exoskeletons Wearable robots Visual systems Robustness Force control

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A novel gait prediction method for soft exosuit base on limit cycle and neural network 22

A novel gait prediction method for soft exosuit base on limi...

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

作者： Chen, Lingxing Chen, Chunjie Fang, Tao Zhang, Yu Liu, Youfu Wu, Xinyu Shenzhen Institutes of Adanced 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 Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

The important purpose of soft exosuit is to decrease the energy consumption of users by providing assistance. If there is something wrong with the judgment of the human gait during the assisting process, the assisting effect will be poor and even people's walking will be affected. A novel method is designed to predict human gait information in the paper, which utilizes the curve shape and mathematical characteristics of the Rayleigh oscillator equation in limit cycle to fit gait information. Only 1/4 gait cycle data is needed to input into the trained neural network to output an Rayleigh oscillator equation that can better predict the remaining gait cycle information. Data of four subjects on different terrains which include flat ground and upstairs are collected. Experiment results showed that the trained ANN model costs about 0.006s in CPU, it has a prediction speed similar to traditional prediction methods. Simultaneously the Rayleigh oscillator equation has good performance in predicting gait information, and it can show a higher stability and accuracy compared with traditional gait prediction methods. © 2021 IEEE.

关键词： Forecasting

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Bone Layer Perception in Milling Process Based on Video Sequence Images during Robot-assisted Laminectomy

Bone Layer Perception in Milling Process Based on Video Sequ...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Li, Haiyang Li, Meng Qi, Xiaozhi Yang, Yuanyuan Hu, Ying Shenyang Aerospace University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Harbin Institute of Technology China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665481090

To optimize the operation ability of the autonomous robot under the background of complex surgery, this paper extracted the state features of milling video sequence images to realize bone layer perception and improve the perception ability of the spinal robot. The bone layer sensing algorithm mainly consists of four parts: improved moving object detection algorithm, moving object tracking algorithm, state feature extraction algorithm and bone layer recognition algorithm. Aiming at the shortcomings of the existing moving target detection methods, this paper proposes an improved moving target detection algorithm based on the existing Background Subtractor MOG algorithm, which is suitable for milling video sequences. The grinding machine state obtained by the improved algorithm is used as the input of the moving target tracking algorithm, and the kernel correlation filter (KCF) is used to realize the target tracking. According to the tracking of the moving target, the state features of the milling area are extracted and the bone layer is identified, so as to realize the bone layer perception in the milling process. © 2022 IEEE.

关键词： Object detection

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A novel soft exosuit based on biomechanical analysis for assisting lower extremity 22

A novel soft exosuit based on biomechanical analysis for ass...

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

作者： Liu, Youfu Chen, Chunjie Lu, Jun Liu, Yida Zhang, Yu Wang, Zhuo College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology 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 Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which added extra inertial to low limb and restricted wearers' movement, the soft wearable lower extremity exoskeleton minimized the impact of these factors on the human body locomotion. In this paper, a novel design of exosuit that provided assistive force for both hip extension and flexion through the variation in hip joint dynamics during strides was proposed. Based on the change of hip moment, an assistance strategy and assistance force curve were came up with. PD type iterative learning control (ILC) method was introduced to reduce the error caused by wearing position and biological characteristics to improve assistance performance. To evaluate assistance performance, the metabolic cost of four subjects wearing exosuit and walking on treadmill at 5km/h in the situations of that without assistance, assisting both hip extension and hip flexion, and assisting hip extension respectively was measured. Compared with assisting hip extension only and wearing exosuit with no assistance, results indicated that the decrease in average net metabolic cost of assisting both hip extension and flexion was 0.445W/kg and 1.027W/kg respectively, corresponding to the average net metabolic cost rate decrease was 7.45% and 15.67%. © 2021 IEEE.

关键词： Exoskeleton (Robotics)

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GAN-SLAM: GAN based Monocular Visual-Inertial Simultaneous Localization and Mapping in Dark Environments 5

GAN-SLAM: GAN based Monocular Visual-Inertial Simultaneous L...

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5th International Symposium on Autonomous systems, ISAS 2022

作者： Zhang, Qieshi Hao, Luoying Li, Hongjian Ren, Ziliang Cheng, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Southern University of Science and Technology Shenzhen China School of Science and Technology Dongguan University of Technology Dongguan China

ISBN: (纸本)9781665487085

In recent years, visual-inertial Simultaneous Localization and Mapping (SLAM) techniques have achieved excellent results due to the complementary nature of visual and inertial sensors, the challenges remain in some environments. When encountering a challenging environment, image features become an important factor affecting the success rate and accuracy of localization of SLAM system. Due to the Convolutional Neural Network (CNN) has the advantage of extracting effective features more accurately, this paper makes a new attempt to replace traditional features with those generated by the network. We combine the state-of-the-art CNN of optical flow estimation and iterative optimization model of traditional SLAM framework, which achieves the accuracy and robustness in challenging environments. To show the performance of our method, some state-of-the-art visual-inertial methods are tested and compared with the challenging sequences of public dataset. Experimental results show that the proposed method can achieve better results in accuracy and robustness and works reliably well for challenging environments. © 2022 IEEE.

关键词： Generative adversarial networks

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FDN: Finite difference network with hierarchical convolutional features for text-independent speaker verification

arXiv

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arXiv 2021年

作者： Li, Jin Yan, Nan Wang, Lan CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

In recent years, using raw waveforms as input for deep networks has been widely explored for the speaker verification system. For example, RawNet and RawNet2 extracted speaker’s feature embeddings from waveforms automatically for recognizing their voice, which can vastly reduce the front-end computation and obtain state-of-the-art performance. However, these models do not consider the speaker’s high-level behavioral features, such as intonation, indicating each speaker’s universal style, rhythm, etc. This paper presents a novel network that can handle the intonation information by computing the finite difference of different speakers’ utterance variations. Furthermore, a hierarchical way is also designed to enhance the intonation property from coarse to fine to improve the system accuracy. The high-level intonation features are then fused with the low-level embedding features. Experimental results on official VoxCeleb1 test data, VoxCeleb1-E, and VoxCeleb-H protocols show our method outperforms and robustness existing state-of-the-art systems. To facilitate further research, code is available at https://***/happyjin/FDN Copyright © 2021, The Authors. All rights reserved.

关键词： Speech recognition

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A control method with terrain classification and recognition for lower limb soft exosuit 22

A control method with terrain classification and recognition...

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

作者： Ni, Jiangpeng Chen, Chunjie Wang, Zhuo Liu, Youfu Wu, Xinyu Liu, Yida Shenzhen Institutes of Adanced 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 Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

关键词： Metabolism

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Time-frequency decomposition-based weighted ensemble learning for motor imagery EEG classification

Time-frequency decomposition-based weighted ensemble learnin...

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

作者： Zheng, Liangsheng Ma, Yue Li, Mengyao Xiao, Yang Feng, Wei Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781665436786

Motor imagery brain-computer interface system based on Electroencephalogram (EEG) is an effective way to help the disabled recover part of their motor abilities. However, decoding the movement intention contained in the EEG signal accurately presents many challenges. In this paper, we propose a time-frequency decomposition-based weighted ensemble learning (TFDWEL) method, which aims to improve the classification performance of motor imagery EEG signals. The TFDWEL method divides the EEG signal into multiple subsets, and uses four time-frequency processing methods to extract the time-frequency sub-bands of each subset. Then the feature extraction model and classifier model of each subset trained by the common spatial pattern (CSP) algorithm and the support vector machine (SVM) algorithm are used to build a set of base learners. The least square error estimation method is used to learn the weight of each base learner, and finally the weighted summation method is used to obtain the final decision. The classification performance of the TFDWEL method is evaluated on the BCI Competition IV Data Set 2b, and the results show that the classification accuracy of 81.58% can be obtained. Superior classification performance indicates that the TFDWEL method can be used in further research to help the rehabilitation of the disabled. © 2021 IEEE.

关键词： Electroencephalography

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