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

作者： Ijaz Ahmad Xin Wang Lin Li Zhenzhen Liu Jun Huang Sunday Timothy Aboyeji Guanglin Li Subhas Chandra Mukhopadhyay Zhiyuan Liu Guoru Zhao Yi Guo Shixiong Chen CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Surgery Division Epilepsy Center Shenzhen Children’s Hospital Shenzhen China Shenzhen Fengsheng Biotechnology Co. LTD Shenzhen China Department of Engineering Macquarie University Sydney NSW Australia Department of Neurology Shenzhen People’s Hospital Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Automatic detection of epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific models and have shown promising results, which is not suitable for clinical application, especially when new patient data are used for diagnosis in EEG epileptic seizure detection (ESD). Therefore, the proposed study introduces a novel hybrid deep learning approach consisting of a one-dimensional convolutional neural network (1D CNN), a Multi-Long Short-Term Memory Network (MLSTM) with a multi-attention layer (MAT) for patient-specific, cross-patient, and patient-independent seizure detection. The 1D CNN model extracts spatial features, while the MLSTM extracts temporal features from segmented EEG data. Moreover, the MAT layer conducts feature fusion and identifies relevant patterns. Experiments conducted using the CHB-MIT EEG dataset confirm our method’s superiority over other sibling and state-of-the-art methods by an average of 2% in classification accuracy, recall, specificity, and *** of using patient-specific, cross-patient, and patient-independent seizure detection, demonstrating a robust and effective framework in EEG ESD.

关键词： Deep learning Biological system modeling Epilepsy Feature extraction Brain modeling Electrostatic discharges Electroencephalography Data models Convolutional neural networks Data mining

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Skeleton-based action recognition with multi-scale spatial-temporal convolutional neural network

Skeleton-based action recognition with multi-scale spatial-t...

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

作者： Cheng, Qin Ren, Ziliang Cheng, Jun Zhang, Qieshi Yan, Hao Liu, Jianming Guilin University of Electronic Technology Guilin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Key Laboratory of Human-Machine Intelligence-Synergy Shenzhen Systems China The Chinese University of Hong Kong Hong Kong Hong Kong University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665436786

The skeleton data convey significant information for human action recognition since they can robustly accommodate cluttered background and illumination variation. Early convolutional neural networks (CNN) based method mainly structure the skeleton sequence into pseudo-image and feed it into image classification neural network such as Resnet, which can not capture comprehensive spatial-temporal feature. Recently, graph convolutional networks (GCNs) have obtained superior performance. However, the computational complexity of GCN-based methods is quite high, some works even reach 100 GFLOPs for one action sample. This is contrary to the highly condensed attributes of skeleton data. In this paper, a Multi-scale Spatial-temporal Convolution Neural Network (MSST-Net) is proposed for skeleton-based action recognition. Our MSST-Net abandons complex graph convolutions and takes the implicit complementary advantages across different scales of spatial-temporal representations, which are often ignored in the previous work. On two datasets for action recognition, MSST-Net achieves impressive recognition accuracy with a small amount of calculation. © 2021 IEEE.

关键词： Convolutional neural networks

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Tactile grasp stability classification based on graph convolutional networks

Tactile grasp stability classification based on graph convol...

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

作者： Mi, Tingting Que, Dashun Fang, Senlin Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Yi, Zhengkun Wu, Xinyu School of Information Engineering Wuhan University of Technology Wuhan430070 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute 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)9781665436786

One of the challenges for robots to grasp unknown objects is to predict whether objects will fall at the beginning of grasping. Evaluating robotic grasp state accurately and efficiently is a significant step to address this issue. In this paper, based on the different fusion approaches of multi-sensor tactile signals, we propose two novel methods based on Graph Convolution Network (GCN) for robotic stability classification. Specifically, we propose two deep learning methods including GCN based on data-level fusion (GCN-DF) and GCN based on feature-level fusion (GCN-FF). We explore the optimal parameters for transforming sensor signals into a graph structure. Furthermore, we verify the effectiveness of the proposed methods on the BioTac Grasp Stability (BiGS) dataset. The experimental results prove that the proposed approaches achieve higher classification accuracy than Support Vector machine (SVM) and Long Short-Term Memory (LSTM). © 2021 IEEE.

关键词： Convolution

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Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors

Enhanced Deep Transfer Learning Model based on Spatial-Tempo...

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

作者： Oluwarotimi Williams Samuel Mojisola G. Asogbon Frank Kulwa Alireza Rezaie Zangene Tolulope T. Oyemakinde Tobore Igbe Alistair A. McEwan Yongcheng Li Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China Department of Sports Biomechanics Kharazmi University Tehran Iran Center for Diabetes Technology School of Medicine University of Virginia University Virginia United States of America

Intelligent rehabilitation robotics (RR) have been proposed in recent years to aid post-stroke survivors recover their lost limb functions. However, a large proportion of these robotic systems operate in a passive mode that restricts users to predefined trajectories that rarely align with their intended limb movements, precluding full functional recovery. To address this issue, an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) model is proposed to decode post-stroke patients’ motion intentions toward realizing dexterously active robotic training during rehabilitation. For the first time, we use Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns. We evaluated the STD-CWT method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. We then validated the method using electromyogram signals of five stroke survivors who performed twenty-one distinct motor tasks. The results showed that the proposed technique recorded a significantly higher (p<0.05) decoding accuracy and faster convergence compared to the common method. Our method equally recorded obvious class separability for individual motor tasks across subjects. The findings suggest that the STD-CWT Scalograms have the potential for robust decoding of motor intention and could facilitate intuitive and active motor training in stroke *** Relevance— The study demonstrated the potential of Spatial Temporal based Scalograms in aiding precise and robust decoding of multi-class motor tasks, upon which dexterously active rehabilitation robotic training for full motor function restoration could be realized.

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Two stream dynamic threshold network for weakly-supervised temporal action localization

Two stream dynamic threshold network for weakly-supervised t...

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

作者： Yan, Hao Cheng, Jun Zhang, Qieshi Ren, Ziliang Sun, Shijie Cheng, Qin Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Chinese University of Hong Kong Hong Kong Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781665436786

The current mainstream temporal action localization methods are fully-supervised, which needs a lot of time to annotate the required frame-level labels. The emergence of weakly-supervised methods greatly alleviates such problem, as they only require video-level labels to train the models. In order to generate accurate action localization boundary, recent two stream consensus network (TSCN) proposes an attention normalization loss to explicitly force the attention values approach extreme values to avoid ambiguity. However, most previous methods including TSCN use a fixed threshold applied on attention loss to polarize the attention values, which lacks flexibility for different videos. In this paper, we propose a Dynamic Threshold Weakly-supervised action Localization (DH-WTAL) method to address this problem. The proposed DH-WTAL features a dynamic attention threshold decision for the attention mechanism. Specifically, the dynamic threshold can dynamically control the number of snippets selected for different videos, which further adjust the extreme values of the attention mechanism for different videos accordingly. Extensive experiments demonstrate that the proposed DH-WTAL outperforms the TSCN baseline, and ablation study validates the effectiveness of this method. © 2021 IEEE.

关键词： Dynamics

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A Phone-Level Speaker Embedding Extraction Framework with Multi-Gate Mixture-of-Experts Based Multi-Task Learning

A Phone-Level Speaker Embedding Extraction Framework with Mu...

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International Symposium on Chinese Spoken Language Processing

作者： Zhijunyi Yang Mengjie Du Rongfeng Su Xiaokang Liu Nan Yan Lan Wang School of Information Engineering Wuhan University of Technology Wuhan China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9798350397970

Previous research has demonstrated that phonetic information can be incorporated into speaker embedding models to improve the performance of the speaker recognition system. However, the current speaker recognition process base on phonetic information is not in the line with the forensic voice identification process, where the identity of a speaker is determined by comparing confident phones one by one. This mainly manifests in two aspects. Firstly, real-world speech signals containing unreliable noisy data are directly used as model inputs. Secondly, the traditional utterance-level speaker embedding consisting of a single vector is a coarse-grained representation of speaker identity. To address these issues, this paper proposes a phone-level speaker embedding extraction framework with Multi-gate Mixture-of-Experts (MMoE) based multi-task learning. In the proposed framework, confident mono-phone segments are obtained by a well-trained ASR model and used as the inputs of the speaker embedding extractor with residual structure and self-attention mechanism. MMoE in the framework is supposed to increase the modeling sensitivity of different accents. To improve the fineness of the speaker representation, a vector set generated from different mono-phone segments is used as the phone-level speaker embedding. On a large-scale Mandarin speaker identification dataset, the proposed system using phone-level speaker embeddings and the MMoE technique significantly outperformed the utterance-level baseline system by the Top-l average error rate reduction of 47.0% relatively.

关键词： Sensitivity Error analysis Forensics Phonetics Multitasking Data models Speaker recognition

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EEG-Based Epileptic Seizure Detection via machine/Deep Learning Approaches: A Systematic Review

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Computational intelligence and Neuroscience 2022年第0期2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems 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-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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Design and 3D Printing of Lower Limb Prosthetic Socket *

Design and 3D Printing of Lower Limb Prosthetic Socket *

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Sheng Li Bin Xie Ye Chen Xinyu Wu Guoxin Fang Yan Zhang Fei Gao 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 Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China State Key Laboratory of Powder Metallurgy Central South University Changsha Hunan China

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

The prosthetic socket is a crucial interface connecting the residual limb to the prosthetic device, significantly affecting force transmission and wearer comfort. This work presents a comprehensive framework for design, simulation, and fabrication of the prosthetic socket. A 3D model of the residual limb, including the skeleton model and skin contour, is reconstructed from CT scan data. Using this model, the prosthetic socket and liner are designed based on biomechanical principles. The interaction force between the residual limb and the prosthetic socket and liner is simulated via finite element analysis (FEA) when the body weight of a below-knee amputee exerts on the prosthetic socket. Finally, the prosthetic socket and residual limb are fabricated using additive manufacturing (AM) technology, and a soft prosthetic liner is made of ethylene-vinyl-acetate (EVA) polymer. Clinical trials on a below-knee amputee were conducted to preliminarily test the prosthetic socket’s performance.

关键词： Limbs Solid modeling Sockets Biological system modeling Force Three-dimensional printing Skin Data models Finite element analysis Prosthetics

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A modular rehabilitation lower limb exoskeleton for stroke patients with hemiplegia

A modular rehabilitation lower limb exoskeleton for stroke p...

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

作者： Li, Pengbo Wei, Wenhao Bao, Ruoyu He, Bailin Su, Zhilong Wang, Can 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 University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering The Chinese University of Hong Kong College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shaanxi Xi'an710021 China

ISBN: (纸本)9781665436786

A modular rehabilitation exoskeleton robot for hemiplegic patients (SIAT-H) is created in this paper. The design of the exoskeleton is based on the gait analysis of hemiplegic patients and the movement data of healthy people. Through the motion capture experiments at different speeds, the walking style of patients with hemiplegia is understood. A modular exoskeleton robot, including an independent hip joint, knee joint and ankle joint, is designed. The mechanical structure which is optimized based on the physiological characteristics of human body would not cause additional damage to the joints during walking. In order to ensure the reliability of use, the structural strength is also considered. The kinematics relationship is verified by comparison of simulation data and calculated data. In the end, we made an experimental prototype, and the wearing experiment verified the reliability of the mechanism. © 2021 IEEE.

关键词： Exoskeleton (Robotics)

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Impedance control for a novel composite modular lower-limb hemiplegic exoskeleton

Impedance control for a novel composite modular lower-limb h...

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

作者： Bao, Ruoyu Li, Pengbo He, Bailin Wang, Can 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 University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering Chinese University of Hong Kong College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shaanxi Xi'an710021 China

ISBN: (纸本)9781665436786

This paper proposes a control scheme based on impedance control for the smoothing control of the operating movements of a lower-limb hemiplegic exoskeleton robot on the basis of a new composite modular lower-limb hemiplegic ex-oskeleton(CMLHE) structure. The CMLHE has good structural performance and can be used in the rehabilitation of patients with hemiplegia caused by stroke and other diseases. At the same time, the smoothing control by means of impedance control ensures that patients with lower limb hemiplegia can use the CMLHE to achieve smooth walking movements without additional injuries caused by the CMLHE during the rehabilitation training. This paper analyses the performance of this CMLHE robot based on walking movements and demonstrates through kinematic and kinetic modelling and simulation that the impedance control scheme of this CMLHE robot is feasible and effective and can be more widely used in the rehabilitation training of patients with lower limb hemiplegia. © 2021 IEEE.

关键词： Exoskeleton (Robotics)

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