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

作者： Ren, Hao Wu, Xinyu Shang, Wanfeng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

Robotic soldering has emerged as one of the key technologies for the through-hole technology (THT) assembly process. However, the determination of the soldering process parameters in those systems are still depends on engineer's experience. In this paper, we propose an automatic vision-based soldering process parameters determination method based on a mathematical model of standard solder joint. The system framework of this method and the detailed process of solder feeding volume calculation are also given. The proposed method is verified by experiments and compared with the soldering process parameters in published literature. Finally, we also analyze that the average computing time of the proposed method for new solder joints is 31.39ms, which is much faster than previous manual debugging method. © 2021 IEEE.

关键词： Soldering

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A Preliminary Study on the Functional Coupling between Nerve and Blood Microcirculation for Applications in Rehabilitation Robots

A Preliminary Study on the Functional Coupling between Nerve...

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IEEE International Conference on Robotics and Biomimetics

作者： Qingge Li Yuanzhe Dong Yuxiang Zhang Xin Wang Naifu Jiang Jianping Huang Han Cui Lan Tian Yue Zheng Xiangxin Li Lin Wang Guanglin Li Wenyuan Liang Liang Peng Peng Fang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China National Research Center for Rehabilitation Technical Aids Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Rehabilitation robots play an important role in the motor function rehabilitation for stroke survivors with hemiplegia. However, the rehabilitation effect of current robots is still limited partly because a single training of motor function can be strongly affected by the decreased blood supply function of the bedridden patients. This work proposed an approach to study the coupling relationship between the motor and blood supply functions by combining the synchronously recorded EEG and cerebral blood oxygen information, where the cerebrations in different movement paradigms were analyzed from an aspect of "functional coupling". The results show that the information of oxyhemoglobin concentration change (ΔHbO) can effectively indicate the cortex activation, and a stronger blood supply is needed in cortexes to perform body movements. The correlations within motor cortexes are significantly stronger than the ones between motor and prefrontal cortexes, and a higher resistance level of extremity training will cause stronger correlations. Calculation of transfer entropy (TE) shows that there exists a bidirectional information transmission between the electrophysiological and blood supply signals, and more information is transmitted always in the direction from ΔHbO to EEG than in the opposite direction. The information transmission or the coupling relationship can be significantly enhanced by extremity movements, and large TE values are achieved in the Theta, Beta and Gamma frequency bands of EEG that correspond to motor functions. This work has demonstrated the functional coupling between nerve and blood microcirculation, which would provide a technical guidance to improve the rehabilitation effect for current robot systems and have great application potentials.

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A Precise Hip Protection System with Multi-scale Fall Warning Algorithm Based on Offset Displacement

A Precise Hip Protection System with Multi-scale Fall Warnin...

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

作者： Qiangqiang Chen Yanan Diao Yaping Wang Yumin Chen Yunkun Ning Guanglin Li Guoru Zhao CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Research Center for Neural Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China College of Computer Science and Technology Xiamen University of Technology China Department of Neurology Shenzhen Second People's Hospital Shenzhen China

Falls occur frequently in daily life and the damage to the body is irreversible. Therefore, it is crucial to implement timely and effective warning and protection systems for falls to minimize the damage caused by falls. Currently, the fall warning algorithm has shortcomings such as low recognition rates for falls and fall-risk movements and insufficient lead-time, the time before the subject impacts the floor, making it difficult for falling protection devices to function effectively. In this study, a multi-scale falls warning algorithm based on offset displacement is built, and a hip protection system is designed. The performance of the algorithm and the system is validated using 150 falling and 500 fall-risk actions from 10 volunteers. The results showed that the recognition accuracy for falling actions is 98.7% and the recognition accuracy for fall-risk actions is 99.4%, with an average lead-time of 402ms. The protection rate for falling movements reached 98.7%. This proposed algorithm and hip protection system have the potential to be applied in elderly communities, hospitals, and homes to reduce the damage caused by *** Relevance— This study provides important reference for clinicians in analyzing fall behaviors to patients at risk of falls in clinical settings, offering valuable technical support for ensuring the safety of patients in danger of falling. It also contributes to further promoting the development of falling-prevention medical devices.

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Review of biomechanical deviations among nonpregnant,pregnant,and postpartum cohorts

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Medicine in Novel Technology and Devices 2023年第2期18卷 24-35页

作者： Wei Linjuan Wang Yan Peng Yinghu Tan Qitao Gu Yaodong Li Li Zhang Ming Department of Biomedical Engineering Faculty of EngineeringThe Hong Kong Polytechnic University999077Hong KongChina The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518057China Research Institute for Sports Science and Technology The Hong Kong Polytechnic University999077Hong KongChina CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences518055ShenzhenChina Faculty of Sports Science Ningbo UniversityNingbo315211China School of Fashion and Textiles The Hong Kong Polytechnic University999077Hong KongChina

During pregnancy,women experience substantial changes in physiology,morphology,and hormonal *** changes have profound effects on the biomechanics of the human body,particularly the musculoskeletal system,resulting in discomfort,pain,and decreased body *** biomechanical knowledge is critical for understanding the etiology and precautions of musculoskeletal *** awareness of health problems in the pregnant cohort,identification,intervention,and precaution of problems have garnered *** have conducted studies to determine the biomechanics of *** have been review studies on ***,to the best of our knowledge,few studies have comprehensively described biomechanical changes throughout pre-,in-,and postpartum *** review analyzed available studies on biomechanical changes during these three periods in the electronic databases of PubMed,Scopus,and Cochrane from inception until June 2,*** the general information,age of the studied subjects,investigated periods,sample size,objectives,measurement tools,and outcomes of reviewed *** Using National Institutes of Health quality assessment tool for observational cohort and cross-sectional studies to assessment the quality of the reviewed *** studies revealed biomechanical deviations in body stability,motion patterns,and gait modes during these three *** research content,there are insufficient studies on certain critical biomechanical aspects,such as the kinetic parameters of the inner body,which are the most direct factors related to musculoskeletal *** to the National Institutes of Health quality assessment tool for observational cohort and cross-sectional studies,a more comprehensive and explicit understanding of pregnancy biomechanics can be expected.

关键词： Pregnancy The center of pressure(COP) Kinematic Kinetic Motion patterns Gait modes

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A comprehensive channel and feature selection method for myoelectric pattern recognition 22

A comprehensive channel and feature selection method for myo...

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

作者： Li, Mengyao Ma, Yue Zheng, Liangsheng Wang, Can Feng, Wei Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781728162676

The advent of myoelectric control schemes provides promising chances for locomotion empowerment and restoration of those with disabilities. Despite substantial efforts have been made into advancing sEMG-based motion recognition, it may be a little tricky to determine appropriate muscles and features for people with muscle disorders or different muscle use preferences. To mitigate it, an advantageous sEMG channel and feature selection method based on ReliefF algorithm was proposed. Related experiments were conducted on a eight able-bodied subject database to showcase the feasibility and efficiency of the proposed approach, that is, considerably high classification performance was maintained with the original feature set reduced by more than half. Ulteriorly, we also investigated the influences of different number of neighbors or features on classification accuracy for ascertaining the optimal values. The strengths of our proposed method lie in not only customizing channel and feature selection for individual users, but also offering preliminary insight for a general mapping mechanism between human muscles and corresponding motions. © 2021 IEEE.

关键词： Feature Selection

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Tunable collective electromagnetic induced transparency-like effect due to coupling of dual-band bound states in the continuum

arXiv

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arXiv 2024年

作者： Chen, Jian Huang, Rixing Zhao, Xueqian Fan, Qingxi Chang, Kan Zhang, Zhenrong Li, Guangyuan Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning530004 China School of Electrical Engineering Guangxi University Nanning530004 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

The coupling between dual-band or multi-band quasi-bound states in the continuum (q-BICs) is of great interest for their rich physics and promising applications. Here, we report tunable collective electromagnetic induced transparency-like (EIT-like) phenomenon due to coupling between dual-band collective electric dipolar and magnetic quadrupolar q-BICs, which are supported by an all-dielectric metasurface composed of periodic tilted silicon quadrumers. We show that this collective EIT-like phenomenon with strong slow light effect can be realized by varying the nanodisk diameter or the tilt angle, and that the transparency window wavelength, the quality factor, and the group index can all be tuned by changing the nanodisk size. We further find that as the nanodisk size decreases, the slow light effect becomes stronger, and higher sensitivity can be obtained for the refractive index sensing. Interestingly, the sensitivity first increases exponentially and then reaches a plateau as the nanodisk size decreases, or equivalently as the group index increases. We therefore expect this work will advance the understanding of the collective EIT-like effect due to coupling between q-BICs, and the findings will have potential applications in slow-light enhanced biochemical sensing. © 2024, CC0.

关键词： Refractive index

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A method for recognition of dynamic hand gestures based on wrist tendon sounds 22

A method for recognition of dynamic hand gestures based on w...

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

作者： He, Bailin Wang, Can Wang, Hao Li, Mengyao Duan, Shengcai Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Samsung Electronics Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781728162676

Hand gesture recognition are widely applied in game industry and rehabilitation engineering. Some recognition methods based on physiological data are attracting attention. In this paper, a novel method for dynamic hand gesture recognition based on sounds of wrist tendon is proposed. Bone conduction sensors which automatically filter the ambient noise is utilized to collect the wrist tendon sounds of three types of dynamic hand gestures. Continuous Wavelet Transform(CWT) is utilized extract the features of sounds and Support Vector machine(SVM) is utilized to train the classifier model. Five healthy subjects participated the experiment and the average test accuracy of three classes achieves 92.33%. The proposed method provides an effective perspective for hand gesture recognition. © 2021 IEEE.

关键词： Support vector machines

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Adaptive admittance control of human-exoskeleton system using RNN optimization

Adaptive admittance control of human-exoskeleton system usin...

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

作者： Lian, Pengchen He, Yong Ma, Yue Liu, Jingshuai 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

ISBN: (纸本)9781665436786

For wearable exoskeletons, ensuring safety and stability while reducing the effort of human lower limbs during walking remains a challenge. The level of safety can be guaranteed by constraining the exoskeleton robot to never exhibit any erratic behavior. But beyond that, the cooperative motion generated in human-exoskeleton interactions should be truly intuitive and should minimize the limitations of human performance. Therefore, this paper presented a new adaptive admittance control law, which not only ensures the stability of the lower limb exoskeleton in the constrained motion but also provides a very harmonious human-exoskeleton interaction. The proposed approach is applied to the movement of the exoskeleton containing the knee joint in the swing phase. The input of the admittance control system is the human-exoskeleton interaction force measured by one single-dimensional force sensor. The performance of the method was theoretically analyzed and discussed. Experiments were conducted in Matlab simulation environment to verify the effectiveness of aid to the swinging leg. Compared with the fixed admittance control, the adaptive admittance control system optimized by a Recurrent Neural Network(RNN) greatly improves the level of interaction between the exoskeleton and the wearer. © 2021 IEEE.

关键词： Recurrent neural networks

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Relative Entropy Regularized Sample Efficient Reinforcement Learning with Continuous Actions

TechRxiv

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TechRxiv 2022年

作者： Shang, Zhiwei Li, Renxing Zheng, Chunhua Li, Huiyun Cui, Yunduan Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Software Engineering University of Science and Technology of China China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

In this paper, a novel reinforcement learning (RL) approach, continuous dynamic policy programming (CDPP) is proposed to tackle the issues of both learning stability and sample efficiency in the current RL methods with continuous actions. The proposed method naturally extends the relative entropy regularization from the value function-based framework to the actor-critic (AC) framework of deep deterministic policy gradient (DDPG) to stabilize the learning process in continuous action space. It tackles the intractable softmax operation over continuous actions in the critic by Monte Carlo estimation and explores the practical advantages of the Mellowmax operator. A Boltzmann sampling policy is proposed to guide the exploration of actor following the relative entropy regularized critic. Evaluated by several benchmark tasks, the proposed method clearly illustrates the positive impact of the relative entropy regularization including efficient exploration behavior and stable policy update in RL with continuous action space and successfully outperforms the related baseline approach in both sample efficiency and learning stability. © 2022, CC BY.

关键词： Reinforcement learning

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Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis

Inspection of EEG Signals for Noninvasive Blood Glucose Moni...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Igbe, Tobore Samuel, Oluwarotimi Williams Li, Jingzhen Kulwa, Frank Kandwal, Abhishek Nie, Zedong University of Virginia Center for Diabetes Technology Department of Psychiatry and Neurobehavioural Sciences School of Medicine CharlottesvilleVA United States University of Derby School of Computing and Engineering DerbyDE22 1GB United Kingdom Center for Minimally Invasive and Surgical Devices Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guandong Shenzhen China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems and Shenzhen College of Advanced Technology Ucas Guangdong Shenzhen518055 China Shoolini University Department of Physics and Materials Science Himachal Pradesh India

ISBN: (纸本)9781665493840

Prediabetes is a metabolic disorder where the blood glucose (BG) level is higher than normal but not high as diabetes;early diagnosis can prevent health complications and death. However, to determine the BG level, it is required to prick the finger, which causes pain and discomfort. To eliminate this problem, there is a need to investigate noninvasive techniques to estimate BG values and continuous BG monitoring. In this paper, we investigated the changes in electroencephalogram (EEG) frequency parameters that have been scarcely considered for prediabetes diagnosis. We analyzed EEG signals after carrying out an oral glucose tolerance test on 25 participants. Five frequency bands of EEG signals were obtained continuously in 3 positions;frontal (F), occipital (O), and parental (P). The analysis is performed using a boxplot to examine the pattern of the recorded signals. The result shows that the EEG signal from the O position has a sensitivity of 95.3% in the left hemisphere, the P location has 90.3% in the right hemisphere, and F has 93% in the left hemisphere. This observation shows the appropriate location and the combination of EEG frequency parameters, such as the alpha and beta mean power from O and P, which can be integrated into a wearable device to provide a promising clinical solution for noninvasive blood glucose monitoring and prediabetes diagnosis. © 2023 IEEE.

关键词： Electroencephalography

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