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IEEE Symposium on Intelligent Vehicle

作者： Qingyi Liang Zhengmin Jiang Jianwen Yin Lei Peng Jia Liu Huiyun Li Southern University of Science and Technology Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Reinforcement learning has been shown to have great potential applications in autonomous driving. For collaborative driving scenarios, multi-agent reinforcement learning can be used to explore efficient and collaborative driving strategies. However, it still faces the challenge of non-stationary. Traditional methods focus on evaluating the similarities between the real state of the teammate and the modeled state. There is also the issue of partial observability. It can be addressed by establishing communication to share information with other surrounding agents. However, prior approaches overlook the efficient communication problem caused by unprocessed and redundant information. To tackle these two challenges, we propose an approach named Multi-Agent Collaboration via Cross-Attention and Communication (MACAC). MACAC leverages the agent’s local observations to analyze and capture environment and interaction information, while also incorporating teammate modeling through the exchange of concise state information via communication. In addition, to improve the learning process, we integrate the noisy advantage technique into MACAC to enhance the agent’s exploration capabilities. As a result, vehicles can effectively adapt to dynamic environments and exhibit efficient collaborative driving skills. In all, experiments conducted on an autonomous driving simulator demonstrate that our approach surpasses the performance of the baseline algorithms.

关键词： Scalability Collaboration Reinforcement learning Predictive models Prediction algorithms Trajectory Planning

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An intelligent prosthetic system for EMG pattern recognition based prosthesis control*

An intelligent prosthetic system for EMG pattern recognition...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Lan Tian Yue Zheng Naifu Jiang Haoshi Zhang Yan Liu Xiangxin Li Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China

Fitting an appropriate artificial limb is an effective method to help the Upper limb amputees to restore their motor function. In decades, scientists have conducted a series of studies to decode the motor intent of amputees to achieve naturally and dexterous control of prosthetic limbs. Among them, Movement intention by using EMG-based pattern recognition (PR) algorithms from the residual muscle electrophysiological information of the limbs is one of the important methods of multi-functional prosthetic control. However, the EMG-PR based prosthesis is still not widely applied in clinical. One of the major reasons is the real-time performance of various PR algorithms is not robustness enough for clinical application since laboratory performance metrics used to evaluate PR algorithms may be poorly associated with the clinical outcomes. And another important reason is the lack of bio-imitability of prostheses, because the developed bionic prostheses with five independently controlled fingers are heavy and lack of active wrist. Therefore, this paper proposes a multi-degree-of-freedom intelligent prosthetic system for EMG PR algorithms assessment, including the PR algorithm system software named REHPS, a Real-time Control System for Prosthesis (RCSP) and a lightweight prosthetic hand named S-HAND. Both able-bodied subjects and amputees could wear the prosthetic system to carry out tests which are mimicking the daily use of prosthesis. These tests would provide clinic performance metrics for the research of motion intent recognition algorithm. The system integrates advanced motion intent recognition algorithms into the prosthesis and also provides more effective evaluation methods for myoelectric pattern recognition algorithm research.

关键词： Measurement Wrist Prosthetic limbs Software algorithms Electromyography Real-time systems Robustness

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A Deep-Learning-Based Method for Gaining More Biomechanical Parameters with Fewer Sensors in Fast and Complex Movements

A Deep-Learning-Based Method for Gaining More Biomechanical ...

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

作者： Wang, Ye Shan, Gongbing Li, Hua Feng, Ruliang Zhang, Yilan Li, Guanglin 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 Guangdong Shenzhen518055 China University of Lethbridge Department of Mathematics and Computer Science LethbridgeABABT1K3M4 Canada Department of Kinesiology and Physical Education University of Lethbridge LethbridgeABT1K3M4 Canada

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Real-Time biomechanical feedback can provide direct and objective quantified information for any practitioners, such as the athletes/coaches, to assist in accelerating their motor skills' learning and training process. However, it is usually difficult to monitor the human motion in full and acquire the key biomechanical parameters (i.e., the kinematic and kinetic data, the EMG, etc.) just with few sensors in some elite sports involving fast movements and complex motor skills. Using too many sensors in the field tests may limit the athletes' motor ability and affect the collected data's validity and reliability. In this paper, we employ a deep learning method to immensely reduce the number of sensors required for providing the real-Time biomechanical feedback in field, according to the hammer-Throw local motion features found from our pilot study. Based on the Keras API imported from the TensorFlow open-source platform, two Sequential Neural Network models are implemented and compared. One model has two inputs (i.e., vertical displacements and velocities on waist) and six outputs (i.e., vital joint angles on lower limbs). The other one has four inputs (i.e., vertical displacements and velocities on wrist and waist) and thirteen outputs (i.e., vital joint angles on both upper and lower limbs). The experimental results demonstrate that the vital joint angles on the upper and lower limbs have strong correlation with the vertical wrist and waist/hip displacements respectively. This study indicates that fewer wearable sensors can be applied in fast and complex movements to obtain the most significant kinematic data, whereas more biomechanical parameters can be further gained by prediction. © 2022 IEEE.

关键词： Kinematics

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SEMG-based gesture recognition by rotation forest-based extreme learning machine

SEMG-based gesture recognition by rotation forest-based extr...

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

作者： Peng, Fulai Chen, Cai Zhang, Xikun Wang, Xingwei Wang, Changpeng Wang, Lin Chinese Academy of Sciences Shandong Institute of Advanced Technology Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

The motion information contained in surface electromyography (sEMG) signals contributes significantly to the prosthetic hand control. However, the accuracy and speed of gesture recognition from sEMG signals are still insufficient for natural control. In order to alleviate this problem, this paper propose a rotation forest-based extreme learning machine method (RoF-ELM) to improve the recognition performance based on sEMG signals. Firstly, the active motion segments were picked out and pre-processed by sliding window. Then, 104 features were extracted from each sample, and SVM-RFE method was used to reduce the feature space dimension. Finally, the RoF-ELM classification model was constructed and tested based on the EMG data for gestures Data Set containing a total of 72 recordings, each of which consists of six basic gestures. 100 trials were implemented to validate the performance of the proposed method, the results show that the RoF-ELM method have the highest accuracy (91.11%) across different subjects with relatively short runtime compared with decision tree (DT), ELM, random forest (RF), and RoF methods. © 2021 IEEE.

关键词： Decision trees

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Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-LSTM-Attention Network from Surface Electromyography

Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Wang, Zhuo Chen, Hui Yang, Fangliang Wang, Xiangyang Wu, Xinyu Chen, Chunjie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

In various fields such as robotics, rehabilitation, biomechanics, human-machine interfaces, and clinical research, human motion prediction has extensive applications. A hybrid networks model based on Convolutional Neural Network-Long Short-Term Memory-Attention (CNN-LSTM-Attention) was employed in this paper to extract features from the high-dimensional space and time series of surface electromyography (sEMG) data for continuous prediction of knee angles. Five able-bodied subjects participated in the study, walking at a self-selected pace while six surface electromyography (sEMG) signals from muscles involved in knee flexion and extension, along with knee angles, were synchronously recorded. The performance was assessed using the root mean square error (RMSE), Pearson Correlation Coefficient (r), and coefficient of determination (R2) between estimated and measured knee joint angles. Compared to the CNN, LSTM, and CNN-LSTM algorithms, the adopted method estimated knee angles more accurately, achieving an average RMSE, ρ, and R2 of 3.09± 0.90,\ 0.9911 ± 0.0052, and 0.9817± 0.0108, respectively. As the advance time increased, the accuracy of knee joint angle estimation decreased, particularly when the advance time reached 100 ms, at which point the RMSE is 4.83 degress. In addition, the computing time of the prediction model was less than 1 ms, which was shorter than the advance time 100 ms it could predict, thereby facilitating real-time computation in practical environments. Compared to previous studies, the proposed method excelled at extracting features directly from raw signals, avoiding the need for handcrafted feature extraction. Consequently, the model effectively achieved continuous and accurate predictions of joint motion angles and demonstrated potential to reduce latency in the human-machine interface. © 2024 IEEE.

关键词： Clinical research

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Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

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IET Cyber-systems and Robotics 2021年第1期3卷 77-88页

作者： Ejay Nsugbe Oluwarotimi Williams Samuel Mojisola Grace Asogbon Guanglin Li Independent Researcher Chinese Academy of Sciences(CAS)Key Laboratory of Human-Machine Intelligence-Synergy System Shenzhen Institutes of Advanced TechnologyShenzhenChina Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhenChina

In recent years,the electroencephalography(EEG)brain-computer interface(BCI)has been researched in the area of upper-limb prosthesis control due to the promise of being able to record neurological signals which follow activation patterns in the cortex directly from the brain with non-invasive *** is seen as a way of bypassing the limitation posed by acquiring neuromuscular signals predominantly with electromyography(EMG)directly from the stump,which possesses residual limb anatomy *** this study,the sequential forward selection algorithm to form a 10-optimal-channel representation,alongside an extended signal feature vector was applied,to investigate the motion intent decoding performance of EMG-only,EEG-only,and a fused EMG-EEG sensing configuration for four transhumeral amputees with varying stump *** results showed a considerable improvement for the EMG-only configuration with the advanced feature vector,but only a small increase for the EEG-only,and thus a marginal improvement when information from both signals was fused *** is likely due to the EEG requiring a greater number of channels spread across the skull to provide a reliable intent *** work will now involve optimisation studies to find a greater representation of electrode representation and parsimony,to minimise the number of channels while boosting motion intent decoding accuracy.

关键词： anatomy fused motion

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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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Comparison of Blood Pressure Modulation by Low-Intensity Focused Ultrasound Stimulation of Carotid Sinus, Nodose Ganglion, and Vagus Nerve

Comparison of Blood Pressure Modulation by Low-Intensity Foc...

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

作者： Jingjing Wei Ning Ji Lin Wang Xinyu Wu Guanglin Li Wan-Hua Lin CAS Key Lab of Human-Machine Intelligence-Synergy Systems of Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shandong Zhongke Advanced Technology CO. LTD. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Low-intensity focused ultrasound stimulation (FUS) of the vagus nerve has proven effective in lowering blood pressure (BP) in our pilot animal study, opening new possibilities for noninvasive physiotherapy for hypertension. To further explore the impact of BP regulation by FUS on other targets, we compared the BP modulation pattern when applying FUS to the left carotid sinus, nodose ganglion, and vagus nerve trunk, respectively. FUS with different acoustic intensities was applied to each target, while continuous BP waveforms were synchronously recorded in the right common carotid artery. Subsequently, systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR) were extracted and analyzed before, during, and after FUS. The results revealed that FUS targeting the carotid sinus and nodose ganglion could also induce reductions in BP. However, both the degree of BP reduction and the sustained duration were less than those achieved with vagus nerve stimulation. Moreover, the ultrasound stimulation intensity threshold for inducing a hypotensive effect is lower when stimulating the vagus nerve compared to stimulating the carotid sinus and nodose ganglion. This study provides compelling evidence supporting the selection of the vagus nerve as the appropriate target for FUS in regulating BP.

关键词： Heart rate Hypertension Ultrasonic imaging Modulation Brain modeling Pressure control Regulation Engineering in medicine and biology Ganglia Carotid arteries

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Design and Simulation of A Novel Electromagnetic Radial Shock Wave Therapy

Design and Simulation of A Novel Electromagnetic Radial Shoc...

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Rehabilitation Engineering and Assistive Technology (i-CREATe), International Convention on

作者： Xiaomeng Zhou Fengxi Cui Zijian Yang Chunlei Zhao Guanglin Li Shi-Chun Bao CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen China Shenzhen Dongdixin Technology Co. Ltd Shenzhen China National Innovation Center for Advanced Medical Devices Shenzhen China

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

Extracorporeal shock wave therapy is a non-invasive technique, which has been widely used in the clinical treatment of various musculoskeletal diseases such as frozen shoulder and muscle spasms. Conventional pneumatic radial shock wave therapy devices, as the mainstream products today, have shortcomings such as complex structure, high noise, and high requirements for airtightness. Alternatively, electromagnetic radial extracorporeal shock waves can effectively overcome the deficiencies of existing pneumatic technologies by introducing novel system designs. This article introduced the principles of electromagnetic launch technology, demonstrated the feasibility of electromagnetic radial extracorporeal shock wave therapy device by self-designed hardware and control strategy, and investigated the transmission process of shock waves from its generation to its effects on the human body. The feasibility and effectiveness of the design scheme were verified through finite element simulation and prototype manufacturing.

关键词： Shock waves Electromagnetic scattering Noise Medical treatment Prototypes Process control Muscles Manufacturing Electromagnetics System analysis and design

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Trace Finger Kinematics from Surface Electromyography by Using Kalman Decoding Method

Trace Finger Kinematics from Surface Electromyography by Usi...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Haoshi Zhang Xiaomeng Zhou Zijian Yang Lan Tian Yue Zheng Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China

Decoding hand motion intention from surface electromyography (sEMG) is facing with challenges brought by the requirements of multiple DOF and flexibility. Compared with discrete action classification based on pattern recognition, continuous kinematics estimation is considered more natural and intuitive. However, the accuracy of the existing estimation techniques is limited and the computational burden is large. Kalman filtering is an algorithm that uses linear system state equations to optimally estimate system state through system input and output observation data. It is easy to implement computer programming and can update and process the data collected on the spot in real time. In this study, the control process of human upper limb motion was considered as a linear dynamic system, in which the joint angle and the surface EMG were considered as the system state variable and the system observed variable, respectively. A Kalman decoding method was introduced for finger kinematics estimation. The model for finger joint angle estimation from the sEMG signal was established by training data set. And then the joint angle estimation is carried out according to the Kalman filter iterative method. The effects of the method were verified on the publicly available database, getting average correlation coefficient (CC) value 0.70 with the max value 0.81 and the minimum value 0.50. The establishment of the Kalman estimation model can promote its extended research and application in the field of finger motion continuous estimation technology.

关键词： Estimation Training data Kinematics Electromyography Data models Decoding Kalman filters

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