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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Frank Kulwa Oluwarotimi Williams Samuel Mojisola Grace Asogbon Tolulope Tofunmi Oyemakinde Olumide Olayinka Obe Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Computer Science Federal University of Technology Akure Nigeria

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

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A Highly Sensitive Complex Ion-Conductive Hydrogel for Flexible 3d Tactile Interactive Applications

SSRN

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SSRN 2024年

作者： Huang, Jiaxin Hou, Yuanyuan Deng, Xiongjun Arshad, Muhammad Lubineau, Gilles Greish, Yaser E. Tai, Yanlong Zhu, Shanshan Chen, Dazhu Shenzhen Key Laboratory of Polymer Science and Technology Guangdong Research Center for Interfacial Engineering of Functional Materials College of Materials Science and Engineering Shenzhen University Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Thuwal23955-6900 Saudi Arabia Department of Chemistry National Water and Energy Center UAE University Al-Ain15551 United Arab Emirates

Ion-conductive hydrogels possess great potential in the field of flexible sensing due to their unique mechanical characteristics and internal ionic conduction. However, the drawback lies in their inability to simultaneously achieve excellent mechanical strength and high conductivity, which largely limits their practical applications. Here, we report a PVA/PAANa ion-conductive composite hydrogel with dual improvement in both aspects, which owns the macroscopic honeycomb-like porewall structure and a microscopic triple network (TN). Specifically, the H-bonding-rich networks are firstly formed intrinsically within between the PVA chains and between the PVA and PAANa chains, through thermal polymerization and combined freeze-thawing approach. Next the conductive percolation network is incorporated through the addition of silver nanowires, to further boost the overall conductivity. Then, Na+ ions are introduced through salting-out effect to enhance its electrostatic interactions with COO- groups in the hydrogel, forming an electrostatic network. Hence, based on the salting-out assisted freeze-thaw strategy, the ion-conductive composite hydrogel achieved Young's modulus of 201 kPa, toughness of 0.66 MJ/m3, and a conductivity of 0.646 S/m. When implemented as a pressure-sensitive tactile sensor, the hydrogel exhibited a pressure sensitivity of 0.189 kPa-1 (pressure range: 0.797- 2.171 kPa) with a response time of 143 ms, and an exceptional fatigue resistance with signal stability maintained over 1500 compression cycles. Lastly, a 3x3 tactile sensor array based on P/P/A-NaCl hydrogels was designed for multi-dimensional sensing and pressure distribution mapping. As a flexible tactile sensor, the proposed ion-conductive hydrogel shows promising potential for diverse interactive applications in human health monitoring, sports tracking, and soft robotics. © 2024, The Authors. All rights reserved.

关键词： Silver nanowires

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A one-step pseudolinear kalman filter for invasive target tracking in three-dimensional space

A one-step pseudolinear kalman filter for invasive target tr...

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

作者： Zhang, Ying Liang, Rui Xu, Sheng Zhang, Liyuan Zhang, Ye Xiao, Dandan State Grid Tianjin Electric Power Company City West Power Supply Branch Tianjin300190 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

This work studies the characteristics of the 3-dimensional (3D) pseudolinear Kalman filter (PLKF) proposed in the previous work [1] for angle-of-arrival (AOA) target tracking in the 3D space. The previous two-step 3D PLKF contains an xy-PLKF and a z-PLKF. This paper develops a one-step PLKF algorithm, which has low computational complexity. The estimation performance analysis of these two different PLKFs is provided. The one-step PLKF sacrifices the estimation accuracy to acquire a low computational complexity. In the distributed target tracking problem, the one-step PLKF can save a lot of communication costs compared with the two-step one. Furthermore, the stability of two PLKFs are also been simply proven. Simulation examples verify the effectiveness of the proposed one-step 3D PLKF to track an invasive target. Besides, the difference between the two PLKFs based on the theoretical analysis is also demonstrated by the simulation examples. © 2021 IEEE.

关键词： Target tracking

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Multimodal surface material classification based on ensemble learning with optimized features 22

Multimodal surface material classification based on ensemble...

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

作者： Liu, Xiang Wu, Hancheng Fang, Senlin Yi, Zhengkun Wu, Xinyu CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781728162676

In this paper, we propose a novel method for multimodal material classification based on ensemble learning and optimized features. The proposed method consists of three key steps. Firstly, we extract a set of features for each modality. Compared to existing methods, the extracted features are relatively simple but more effective when they are incorporated into the classifiers. Then, the feature selection algorithms, including Multi-Cluster Feature Selection (MCFS) and Laplacian Score (LS) are employed to reduce the feature dimension due to the curse of dimensionality. Finally, an ensemble learning method is proposed to integrate the merits of different feature selection methods. The effectiveness of the proposed method is demonstrated on the LMT-108 surface material dataset which includes multiple modalities such as sound, acceleration, and image. The experimental results have shown that our approach performs better than the competing methods. © 2021 IEEE.

关键词： Learning systems

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A framework of cooperative UAV-UGV system for target tracking

A framework of cooperative UAV-UGV system for target trackin...

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

作者： Su, Zhilong Wang, Can Wu, Xinyu Dong, Yao Ni, Jiangpeng He, Bailin Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

This paper proposed a framework of cooperative UAV-UGV system. This system consists of three modules: UAV, UGV, and ground station console. It has the characteristics of high cohesion, low coupling, cross platform, and cross language. Compared with the prior methods which UAV-UGV cooperation system that only uses UAV to track UGV, our method is more effective and can give full play to the advantages of UAV and UGV. We use UAV visual tracking algorithm and coordinate transformation to get the target location, and use UGV to navigate to the target location to keep up with the target. The high mobility of UAV makes the target tracking range of the whole system larger, the robustness of UGV is good, and the tasks can be performed more precisely. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A data-driven shared control system for exoskeleton rehabilitation robot

A data-driven shared control system for exoskeleton rehabili...

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

作者： Li, Feng He, Yong Li, Jinke Ni, Jiangpeng Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

It is very important to establish a control operating system for rehabilitation robots, especially to improve the rehabilitation effect and human-machine interaction ability of patients. Exoskeletons have been proved to be effective in providing highly repeatable and accurate rehabilitation exercises, but most existing exoskeletons have inconsistent operating systems and data loss. This paper designed a novel data-driven shared control system(DDSCS) farmework, applied to different exoskeleton rehabilitation robots (ERR). Due to the unique physical characteristics of exoskeleton rehabilitation robots, it can't be adapt to different patients. Firstly, the DDSCS framework is established via the data-driven and shared technology, and the feasibility is analyzed. Secondly, to iterate the individualized gait trajectory, data-driven gait trajectory correction model is designed. Finally, the shared human-machine interface is developed, and the superiority and effectiveness of DDSCS framework are verified by the exoskeleton robot experiments. © 2021 IEEE.

关键词： Control systems

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Dynamic modeling and vibration analysis of a 6-DOFs industrial robot considering joint flexibility

Dynamic modeling and vibration analysis of a 6-DOFs industri...

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

作者： Shen, Guodong Xu, Sheng Chen, Chunjie Wang, Qiang Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 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 Shenzhen518055 China

ISBN: (纸本)9781665436786

For the industrial robots, the vibration caused by the joint flexibility could affect the manipulator's performance significantly. Therefore, the modeling and vibration analysis of industrial robot considering joint flexibility has become an important topic in recent years. However, the research on flexible-joint manipulator is usually performed for planar robots, whereas the research on general spatial multi degrees of freedom (DOFs) industrial robots are still limited. In this paper, the dynamic modeling approach is developed for vibration analysis of the serial industrial robot having six DOFs. Firstly, based on the Spong's model of joint deformation, the flexibility of all joints is considered concurrently, and the partial velocity relationship between each link and the joint are deduced. Then, the dynamic model of the 6-DOFs flexible robot is derived according to the Kane equation. The results using the proposed Kane-based method are verified by several numerical simulations. Finally, vibration responses of the end-effector under different joint stiffness parameters are analyzed. The results in this study can offer some useful guidance for the path planning and control to suppress the vibration of the multi-DOFs industrial robots. © 2021 IEEE.

关键词： Vibration analysis

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Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo Multilabel

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 Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Speech Emotion Recognition (SER) in a single language has achieved remarkable results through deep learning approaches in the last decade. However, cross-lingual SER remains a challenge in real-world applications due to a great difference between the source and target domain distributions. To address this issue, we propose an unsupervised cross-lingual Neural Network with Pseudo Multilabel (NNPM) that is trained to learn the emotion similarities between source domain features inside an external memory adjusted to identify emotion in cross-lingual databases. NNPM introduces a novel approach that leverages external memory to store source domain features and generates pseudo multilabel for each target domain data by computing the similarities between the external memory and the target domain features. We evaluate our approach on multiple different languages of speech emotion databases. Experimental results show our proposed approach significantly improves the weighted accuracy (WA) across multiple low-resource languages on Urdu, Skropus, ShEMO, and EMO-DB corpus. To facilitate further research, code is available at https://***/happyjin/NNPM Copyright © 2021, The Authors. All rights reserved.

关键词： human computer interaction

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Data Augmentation for end-to-end Code-Switching Speech Recognition

Data Augmentation for end-to-end Code-Switching Speech Recog...

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IEEE Spoken Language Technology Workshop

作者： Chenpeng Du Hao Li Yizhou Lu Lan Wang Yanmin Qian MoE Key Lab of Artificial Intelligence SpeechLab Shanghai Jiao Tong University Shanghai China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology

ISBN: (数字)9781728170664

ISBN: (纸本)9781728170671

Training a code-switching end-to-end automatic speech recognition (ASR) model normally requires a large amount of data, while code-switching data is often limited. In this paper, three novel approaches are proposed for code-switching data augmentation. Specifically, they are audio splicing with the existing code-switching data, and TTS with new code-switching texts generated by word translation or word insertion. Our experiments on 200 hours Mandarin-English code-switching dataset show that all the three proposed approaches yield significant improvements on code-switching ASR individually. Moreover, all the proposed approaches can be combined with recent popular SpecAugment, and an addition gain can be obtained. WER is significantly reduced by relative 24.0% compared to the system without any data augmentation, and still relative 13.0% gain compared to the system with only SpecAugment.

关键词： Training Speech coding Splicing Conferences Data models Automatic speech recognition

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Learning Compliant Assembly Strategy From Demonstration

Learning Compliant Assembly Strategy From Demonstration

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

作者： Sheng Liu Juyi Sheng Yongsheng Ou Southern University of Science and Technology (SUSTech) 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 (#2019B121205007)

Compared with robots, humans can complete the different assembly tasks of parts flexibly and quickly. By teaching robots with human experiences, not only the industrial assembling tasks can be resolved, but also many other robot applications can be realized. The pose adjustment stage is the most critical part of the peg-in-hole assembly process. This paper analyzes the contact force in the pose adjustment stage and calculates the control variables that affect the assembly motion. Based on the Gaussian mixture model (GMM), the nonlinear mapping relationship between the control variables and the state variables during the assembly is established using the human demonstration data, and the parameters of the model are solved by the expectation maximization (EM) algorithm, and thus the human-like compliant assembly is completed by a robot. In order to verify the effectiveness of the demonstration-learning algorithm, a peg-in-hole assembly experiment was carried out using KUKA manipulator. Finally, the experiment shows that the proposed learning-based method not only improves the efficiency of the robot peg-in-hole assembly but also makes the manipulator have a satisfied adaptive ability to the complex environments in the assembly process.

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