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1st International Conference on Cognitive Computation and systems, ICCCS 2022

作者： Duan, Qingling Zhang, Qi Luo, Dong Yang, Ruofan Zhu, Chi Liu, Zhiyuan Ou, Yongsheng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes 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 Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen China

ISBN: (纸本)9789819927883

human skin can accurately sense subtle changes of both normal and shear forces. However, tactile sensors applied to robots are challenging in decoupling 3D forces due to the inability to develop adaptive models for complex soft materials. Therefore, a new soft tactile sensor has been designed in this paper to detect shear and normal forces, including a soft probe and image acquisition device. First, to capture the deformation of the sensor, colored silicone squares were embedded in the soft probe. Capcamera movement of the colored squares under external forces. The image dataset collected at different 3D forces is then input into a deep learning model. Finally, a custom miniature image device is acquired and embedded in the soft probe to miniaturize the sensor. Computing results obtained from experimental datasets show that the proposed method can accurately decouple the 3D forces. Robots can grap vulnerable objects with sensors prepared at the robot’s tip. The tactile sensors studied in this paper are expected to be applied in robotics fields such as adaptive grasping, dexterous manipulation and human-computer interaction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Probes

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Admittance Control based Hemiplegic Exoskeleton Motion Control

Admittance Control based Hemiplegic Exoskeleton Motion Contr...

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

作者： He, Bailin Zhou, Xin Huai, Yaping Liu, Jiaqing Li, Pengbo Wang, Can Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In this paper, a method based on admittance control is developed to improve patients' comfort wearing an exoskeleton robot for rehabilitation training. In our previous work, we designed a modular and convenient hemiplegic exoskeleton robot. The mechanical structure will cause stiffness when it comes to contact with the human body in practical applications. We introduced admittance control to improve the flexibility of the exoskeleton robot and improve the subjects' comfort. This paper considers coupling of model between the exoskeleton and the human body as the human-computer interaction force of the system at the main human-computer interaction point. Measuring the interaction force between exoskeleton members and limbs, the work uses the virtual admittance model to correct the system's inherent characteristics and studies the control method's stability with Lyapunov's theory. The experiment collected the subject movement information which was converted the movement information of the exoskeleton. human-computer interaction forces in the thigh and calf were measured while the exoskeleton assisted the subject's movement. The experimental results show that adding the admittance controller reduces the human-computer interaction force from 5g to 2g and 6g to 3g, respectively. © 2022 IEEE.

关键词： human computer interaction

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DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization

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Computers in Biology and Medicine 2025年 185卷 109558-109558页

作者： Aboyeji, Sunday Timothy Ahmad, Ijaz Wang, Xin Chen, Yan Yao, Chen Li, Guanglin Tong, Michael Chi Fai Siu, Alice K.Y. Zhao, Guoru Chen, Shixiong CAS Key Laboratory of Human–Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China Surgery Division Epilepsy Center Shenzhen Children's Hospital Shenzhen518036 China Department of Neurosurgery The National Key Clinic Specialty Shenzhen Key Laboratory of Neurosurgery the First Affiliated Hospital of Shenzhen University Shenzhen Second People's Hospital Shenzhen518035 China Department of Otorhinolaryngology Head and Neck Surgery The Chinese University of Hong Kong Hong Kong School of Medicine The Chinese University of Hong Kong Shenzhen Guangdong518172 China

Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Computer-aided diagnosis systems have been proposed to reduce manual inspection of EEG signals by neurologists. However, direct analysis of EEG signals is difficult due to their complex and dynamic nature, with variation across multiple patients. Therefore, researchers have proposed the short-time Fourier transform (STFT) to capture dynamic events indicative of seizures through time-varying frequency representation of EEG signals. However, tradeoffs between time and frequency resolution limited the spectrogram's interpretability and affected clinical deployment. Hence, this study proposes extracting high-resolution channels via a novel STFT spectrogram construction algorithm encompassing taper functions for seizure diagnosis. Initially, we extracted seizure and non-seizure segments from each channel of selected patients in the CHB-MIT dataset. Next, we systematically apply taper functions like Hann and Gaussian windows to minimize the edge effect during the construction of spectrogram images. Finally, we employ Dilated Convolutional Squeeze and Excitation Networks (DCSENets) through leave-one-patient-out cross-validation (LOPOCV) to perform patient-independent seizure classification. The proposed DCSENets achieve an average accuracy of 87.20±11.48% and 87.29±10.48% with Hann and Gaussian taper functions, respectively, and 86.85±11.56% without the taper function. Most patients with high performances indicate similarity in train-test sample distribution using the Kolmogorov–Smirnov test at 0.010.05. Furthermore, the Grad CAM deep visual explainer integration enhances the interpretability of the deep learning model's decision-making process. Consequently, neurologists are provided not only with enhanced visualized spectrograms but also a transparent model for improved seizure diagnosis. © 2024 Elsevier Ltd

关键词： Spectrographs

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The influence of structural parameters on the design function of the tendon driven prosthetic hand

The influence of structural parameters on the design functio...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Yue Zheng Xiangxin Li Lan Tian Yan Liu Yingxiao Tan Xugang Jiang Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy SystemsShenzhen Institutes of Advanced Technology Chinese Academy of Sciences (CAS) SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China

Wearing a prosthesis is one of an alternative way to restore the lost limb functions for amputees. Various prosthetic hands have been designed with different driven mechanisms and structures, which lead to distinct prosthesis functions. The prosthesis function is one of the main reasons for deciding whether the amputees use the prosthesis or not Therefore, this study focuses on analyzing the influence of structural parameters on the prosthesis function through a tendon driven prosthetic hand. Seven parameters of a tendon driven prosthetic finger in four categories, including transmission ratio, spring coefficient, friction coefficient and phalanx thickness, were analyzed. The relationship between structural parameters and phalange forces was studied. Besides, each structural parameter’s influence level on phalange forces has been roughly compared. The friction and thickness influence the contact forces, which may lead to an obvious discrepancy between theoretical calculation and practical application if they were ignored in the mechanical model and simulation. This study aims to provide structural design guidance for the specific requirement of the prosthetic hand.

关键词： Friction Biological system modeling Structural engineering Springs Prosthetic hand Man-machine systems Tendons

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A method of Monocular Visual Odometry Combining Feature points and Pixel Gradient for Dynamic Scene 91

A method of Monocular Visual Odometry Combining Feature poin...

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91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Li, Panwei Li, Huiyun Chen, Beizhang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems China

ISBN: (纸本)9781728152073

In the outdoor dynamic scene, the current Monocular Visual odometry methods of simultaneous localization and mapping system (SLAM) have Low utilization of image information, and they are not enough to meet the problem of stable image matching tracking. An information fusion method combining feature points and direct method gray matching method is proposed, which not only has no serious feature loss when demanding in fast motion, but also reduces the dependence on gray-scale invariant hypothesis. The proposed method also has better stability in the scenes with insufficient texture information, and the method reduces the dependence on gray-scale invariant hypothesis. Experimental results show that our novel front-end visual odometry simplifies the feature points, enriches the front-end utilization information, improves the pixel information of the image, and improves the feature points loss and excessive dependence on the intensity. The average real-time frame rate is around 32Hz, meeting the requirements of real-time performance. In the large-scale scene of the KITTI visual odometry datasets, the jitter effect of the dynamic scene is weakened, and the root mean square error of the positioning is 1. 79m, The average root mean square error of the method is reduced to 22.77% of ORB-SLAM, 26.48% of Semi-Direct Monocular Visual Odometry(SVO),and 37.68% of Large-scale direct SLAM(LSD). © 2020 IEEE.

关键词： Textures

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Respiratory and laryngeal influences on voice in post-stroke dysarthria: a pilot study

Respiratory and laryngeal influences on voice in post-stroke...

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International Symposium on Chinese Spoken Language Processing

作者： Tinghao Zhao Xiaoxia Du Juan Liu Rongfeng Su Nan Yan Lan Wang 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 Department of Neurorehabilition China Rehabilitation Research Center Beijing China

ISBN: (纸本)9798350397970

Dysarthria is a common disorder among stroke-patients that affects a wide range of speech and voice production processes. While voice disorders in post-stroke dysarthric patients have been well documented, few research has set out to investigate the complicated cause of the disorders, which potentially involve altered respiratory and laryngeal functions and their interaction. In this paper, we report a pilot study that preliminarily examined the respiratory and laryngeal influences on vocal performances in post-stroke dysarthric patients and healthy controls. Respiratory, laryngeal and vocal measures were collected in a maximum phonation time task and analyzed with linear mixed-effect regressions. The results suggested that pathways of influence may be established from respiratory to laryngeal functions and from laryngeal to vocal functions. Patients demonstrated increased laryngeal effort in response to reduced respiratory volumes and expiratory speed, which in turn may have contributed to a reduced voice quality and stability among patients. In addition, a few other findings suggested that reduced respiratory functions may also influence vocal performance through alternative pathways, although more work is needed to establish a clear chain of influence in these cases by unveiling the precise relations between the respiratory, laryngeal and vocal abnormalities in question.

关键词： Production Stability analysis Time measurement Behavioral sciences Task analysis Speech processing

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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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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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Instant Motion Recognition of Lower Limb Using Surface Electromyography Signal Based on Convolutional Neural Network

Instant Motion Recognition of Lower Limb Using Surface Elect...

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IEEE International Conference on Robotics and Biomimetics

作者： Xiaohui Li Xiaoyue Yu Hao Zhou Dingxun Jin Guanglin Li Lin Wang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) China University of Chinese Academy of Sciences China Shenzhen Lower Limb Rehabilitation Intelligent Aids Engineering Research Center China Shenzhen Technology University China Southern University of Science and Technology China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The field of rehabilitation robotics has emerged as a prominent area of research in the medical community in recent years, offering innovative and promising solutions for patients with physical disabilities. The existing lower limb rehabilitation robots have some shortcomings in human-machine compatibility and motion synergy due to the limitation of the real-time human motion intention recognition. In this paper, an instant motion recognition of the lower limb was proposed using surface electromyography (sEMG) for significantly shortening the time delay between the robot motion and the actual human motion. The first 200ms data of the complete sEMG signal for a single motion cycle was employed in the recognition algorithm. The proposed recognition algorithm demonstrated superior accuracy compared to traditional machine learning methods. The accuracy of 9-class recognition was 94.32%, while 12-class recognition achieved that of 89.32%. A data augmentation method based on magnitude warping followed by shifting was employed to enhance recognition accuracy by 11.37% in 9-class recognition and 19.23% in 12-class recognition when comparing with no augmentation. The optimal results for motion recognition were observed when the first 200ms data of the complete sEMG signal for a single motion cycle with a duration of 1.0s were utilized, as evidenced by comparative results. The proposed method offers a novel approach to enhancing human-machine compatibility and comfort in the application of lower limb rehabilitation robots in the future.

关键词： Robot motion Limbs machine learning algorithms Accuracy human-machine systems machine learning Assistive robots Inference algorithms Electromyography Sustainable development

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