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A foot-controlled interface for endoscope holder in functional endoscopic sinus surgery

Frontiers in Biomedical Devices, BIOMED - 2017 Design of Medical Devices Conference, DMD 2017

作者： Dai, Xinxin Zhao, Baoliang He, Yucheng Sun, Yu Hu, Ying Shenzhen Graduate School Harbin Institutes of Technology China Shenzhen Institutes of Advanced Technology Chinese Academy of Science China Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences China

ISBN: (纸本)9780791840672

Endoscopic nasal surgery is with minimal invasiveness for the surgical treatment of nasal disease. During traditional functional endoscopic sinus surgery (FESS), the surgeon uses one hand to hold the surgical instrument leaving the other hand to hold the endoscope. When the surgeon needs to use two hands to perform some complex procedure, an assistant surgeon is required to help holding the endoscope, and this requires good teamwork and long-time training. To solve this problem, researchers proposed to use robots to hold the endoscope, freeing the surgeon’s hands for bimanual operation. Sun developed a passive arm with pneumatic locking mechanism to hold the endoscope in FESS, but the surgeon needs to adjust the pose of the endoscope manually, which interrupts the surgery flow and lengthens the surgery time [1]. Many motor-driven endoscope holders have been proposed in literature [2], the surgeon interact with the robot with joystick, voice command, pedals or head movement [3–5]. However, there exists some drawbacks with these interacting methods, for example, joystick requires one of the surgeon’s hands, voice command is usually subject to interference and has long time-delay, foot pedals and head movement distract surgeon’s attention. Lin used a foot-attached IMU sensor to control an active robotic endoscopic holder, the inversion/eversion and abduction/adduction motions of foot are used to select and control different joints, but the motor can be only selected in order, which is unhandy for the four-joint scenario [6].

关键词： Endoscopic devices Surgery Endoscopes

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EEG Feature in Amputee with Five Projected Fingers: A Comparison Study

EEG Feature in Amputee with Five Projected Fingers: A Compar...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liang, Wenyuan Qin, Changyu Dong, Yuanzhe Tang, Xi Li, Guanglin Fang, Peng Bi, Sheng National Research Center for Rehabilitation Technical Aids Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Beijing100876 China Beijing Machine and Equipment Institute Beijing100039 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

The somatosensory ERP provides a tool to understand the tactile sensation mechanism for the projected finger. A total of four subjects (1 amputee & 3 able-bodied) were recruited. The tactile sensation is evoked via TENS. Through applying the stimulation electrodes on the target fingers, simultaneously the EEG data was recorded. Two major findings are concluded as follows. First, the somatosensory ERP of the early-stage in three groups are similar, and then it could infer that the projected finger may have a similar delivery pathway as the natural finger. Second, since three groups show similar cortical processing characteristics, it could infer that the projected finger evoke similar somatosensory neuro information as the natural finger. This tactile sensation mechanism study shows the uniformity between the natural finger and the natural finger. © 2022 IEEE.

关键词： Artificial limbs

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A New Approach for Hand Gesture Recognition Based on the Fusion of sEMG and Impedance Information

A New Approach for Hand Gesture Recognition Based on the Fus...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Yuan Wang Simin Yuan Pingao Huang Hui Wang Wenlong Yu Menglong Fu Xin Wang Oluwarotimi Williams Samuel Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT CAS Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

Surface electromyogram (sEMG) signals associated with multiple patterns of hand gestures is commonly used as input for prosthetic control. However, placing a relatively large number of sEMG sensors on the residual limb of the amputees, particularly via the prosthetic cavity constitute a major challenge. In this article, a new acquisition system that integrates fewer electrodes is developed to acquire high-quality physiological information for adequate recognition of various hand gestures. The developed system utilizes the characteristics of the chip and special circuit design to enable simultaneous acquisition of sEMG signal and hybrid-impedance (HI) signal from a pair of differential electrodes. The results obtained based on the recordings from 10 subjects indicate that adding impedance information increases the sEMG recognition rate of hand gesture with fewer electrodes. This study might provide a new solution for achieving high-precision of the hand gesture recognition with fewer electrodes.

关键词： sEMG Hybrid-Impedance Gesture Recognition Gesture recognition SEMA5B gene Fusion reactions Fusion, biological Gestures

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INTELLIGIBLE ENHANCEMENT OF 3D ARTICULATION ANIMATION BY INCORPORATING AIRFLOW INFORMATION

INTELLIGIBLE ENHANCEMENT OF 3D ARTICULATION ANIMATION BY INC...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Fei Chen Hui Chen Lan Wang Ying Zhou Jiaying He Nan Yan Gang Peng Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology

ISBN: (纸本)9781479999897

The 3D talking head has been developed fast, in which both external and internal articulators were demonstrated. For Mandarin pronunciation, the aspiration airflow is crucial to discriminate confusable Mandarin consonants. In this paper, we present a 3D talking head system for articulatory and aspiration animation with the use of EMA articulation data and airflow data simultaneously. The quantitative analyses of airflow data indicated confusable Mandarin consonants could be distinguished from each other by the mean airflow during voicing, peak expiratory airflow, and airflow duration. An airflow model was then incorporated into the 3D articulatory model to produce the airflow in accordance with articulator movements of Mandarin pronunciation. An audio-visual test was designed to evaluate the current 3D articulation and aspiration system, where minimal pairs were used to recognize the animation. The identification accuracy was significantly improved from 43.9% without airflow to 84.8% with airflow-incorporated information.

关键词： 3D articulatory dynamics Airflow PAS confusable consonants intelligible enhancement

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Microcavity assisted graphene pressure sensor for single-vessel local blood pressure monitoring

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Nano Research 2024年第11期17卷 10058-10068页

作者： Jinan Luo Jingzhi Wu Xiaopeng Zheng Haoran Xiong Lin Lin Chang Liu Haidong Liu Hao Tang Houfang Liu Fei Han Zhiyuan Liu Zhikang Deng Chuting Liu Tianrui Cui Bo Li Tian-Ling Ren Jianhua Zhou Yancong Qiao School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen 518107 China Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province School of Biomedical Engineering Sun Yat-sen University Guangzhou 510275 China School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing 100084 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen 518055 China Cardiovascular surgery Seventh affiliated Hospital of Sun Yat-sen University Shenzhen 518107 China

Dynamic monitoring of blood pressure (BP) is beneficial to obtain comprehensive cardiovascular information of patients throughout the day. However, the clinical BP measurement method relies on wearing a bulky cuff, which limits the long-term monitoring and control of BP. In this work, a microcavity assisted graphene pressure sensor (MAGPS) for single-vessel local BP monitoring is designed to replace the cuff. The microcavity structure increases the working range of the sensor by gas pressure buffering. Therefore, the MAGPS achieves a wide linear response of 0–1050 kPa and sensitivity of 15.4 kPa^(−1). The large working range and the microcavity structure enable the sensor to fully meet the requirements of BP detection at the radial artery. A database of 228 BP data (60-s data fragment detected by MAGPS) and 11,804 pulse waves from 9 healthy subjects and 5 hypertensive subjects is built. Finally, the BP was detected and analyzed automatically by combining MAGPS and a two-stage convolutional neural network algorithm. For the BP detection method at local radial artery, the first stage algorithm first determines whether the subject has hypertension by the pulse wave. Then, the second stage algorithm can diagnose systolic and diastolic BP with the accuracy of 93.5% and 97.8% within a 10 mmHg error, respectively. This work demonstrates a new BP detection method based on single vessel, which greatly promotes the efficiency of BP detection.

关键词： graphene pressure sensor microcavity assisted pressure sensor single-vessel BP monitoring two-stage neural network algorithm

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Skeleton-Embedded Network for action recognition

Skeleton-Embedded Network for action recognition

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Luo, Li Ren, Ziliang Qin, Yong Zhang, Qieshi Gao, Xiangyang Dongguan University of Technology Department of Computer Science Dongguan52300 China Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China The Chinese University of HongKong Hong Kong Hong Kong

ISBN: (纸本)9798350327182

Multimodality-based human action recognition is becoming an increasingly attractive topic as different modalities can provide complementary information. RGB and skeleton data have their pros and limitations for action recognition because they describe the action from distinctive views. Therefore, the fusion of these two modalities using their complementarity is meaningful to action recognition. However, it is difficult to completely utilize the supplementarity of RGB and skeleton data. In this paper, to take advantage of their complementary at the semantic level, A skeleton-embedded network(SE-Net) is proposed by us. The architecture is based on integrating their feature in the network not only a post-hoc fusion. With a sparse sampling strategy, which is efficient for learning using the whole action video. To examine the performance of our method, we conducted experiments on the wildly used datasets, obtaining an accuracy of 86.4% in C-sub and 88.2% in C-view on NTU-RGB+D 60. For NTU-RGB+D 120, the accuracy of 81.3% in C-sub and 83.4% in C-set is obtained. © 2023 IEEE.

关键词： Semantics

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A Spatial Feature Extraction Method for Enhancing Upper Limb Motion Intent Prediction in EMG-PR System 46

A Spatial Feature Extraction Method for Enhancing Upper Limb...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Peng, Boxing Zhang, Haoshi Li, Xiangxin Zheng, Yue Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9798350371499

High-Density Surface Electromyography (HD-sEMG) enriches motion intention pattern recognition systems by providing more spatial information. Multichannel linear descriptors (MLD) could provide a comprehensive description of the overall state characteristics within the muscle regions. In this study, an MLD-based spatial feature extraction method was proposed to capture differences and correlations in various muscle regions during movement, ultimately enhancing the system's classification accuracy. The performance of the feature extraction method was compared with traditional time domain feature extraction method under various classifiers and different movement types. The results show that employing the proposed method with the spatial features improves the classification error rates of combined movements from 11.14% to 7.28%, and better adaptability for all classifiers utilized in this study, which shows the effect of utilization of spatial information in different muscle regions. © 2024 IEEE.

关键词： Electromyography

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An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients' Motor Execution from Multi-Channel EEG-Recordings 46

An Efficient Deep Transfer Learning Network for Characteriza...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Kulwa, Frank McEwan, Alistair A. Aboyeji, Sunday Timothy Khushaba, Rami Fang, Peng Li, Guanglin University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom SIAT-CAS Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Sydney University Australian Centre for Field Robotics ChippendaleNSW2008 Australia

ISBN: (纸本)9798350371499

Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Rehabilitation Robots (IRR) that can effectively engage post-stroke patients (PSP) in intuitive motor training for full function recovery. Most existing rehabilitation robots incorporate functionalities that are passive in nature, constraining PSP to predetermined trajectories that often deviate from patients' limb movement intentions, consequently hindering recovery. To resolve this issue, a robust deep-transfer learning driven network (DTLN) is developed to adequately characterize PSP's motion intention signatures from neural oscillations towards achieving intuitive and active training. Thus, we investigated and proposed the utilization of mu-frequency spectrum (muFS) based CWT approach for Scalograms construction, which serves as inputs to the DTLN model that characterizes multiple classes of PSP's motor execution signatures from multi-channel electroencephalography (EEG) recordings. Then, we evaluated the proposed method using EEG data from six PSP and compared the decoding results to those of related approaches under similar experimental settings. The proposed method resulted in a significant increment of 10.84 % - 13.19% decoding accuracy across stroke patients and better convergence in comparison to other methods. Additionally, the method exhibited distinct task separability for individual motor execution signature across patients. In conclusion, our method offers a consistently accurate decoding of motor tasks that could enable intuitively active robotic training in PSPs with impaired motor function. © 2024 IEEE.

关键词： Brain computer interface

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Vision-based soldering process parameters calculation for robotic soldering

Vision-based soldering process parameters calculation for ro...

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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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EIP-VIO: Edge-induced points based monocular visual-inertial odometry

EIP-VIO: Edge-induced points based monocular visual-inertial...

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2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Hao, Luoying Li, Hongjian Cheng, Jun Zhang, Qieshi Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781728152097

Edge and line features boost the performance of simultaneous localization and mapping (SLAM) systems while bring more additional computational cost. In this paper, we propose an improved and practical monocular visual-inertial odometry method based on selective edge points. Focus on solving the issues of mismatching and tracking loss, the auxiliary edge is utilized to supervise the selection of features instead of using rough features directly. Features along edges are employed as stronger keypoints to steer point-selection away from less distinctive points. By combining matched edge-guided feature based method, selective sampling keypoints along edges based direct method and inertial measurement unit (IMU), the performance of absolute pose estimation is more accurate and efficient. In addition, a weight adjustment factor scheme before IMU error term is introduced in our optimization model to further reduce the influence of motion blur and less texture. Experimental results on public benchmark dataset demonstrate that the proposed method is more efficient and effective compared with the state-of-the-art methods. © 2019 IEEE.

关键词： Textures

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