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Next-Generation Prosthetic Hand:from Biomimetic to Biorealistic

Research 2021年第1期2021卷 222-225页

作者： Ning Lan Manzhao Hao Chuanxin MNiu He Cui Yu Wang Ting Zhang Peng Fang Chih-hong Chou Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghaiChina Laboratory of Neurorehabilitation Engineering School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina Department of Rehabilitation Medicine Ruijin HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghaiChina Center for Excellence in Brain Science and Intelligent Technology Chinese Academy of SciencesShanghaiChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina i-lab Key Laboratory of Multifunctional Nanomaterials and Smart SystemsSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhouChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenChina

Integrating a prosthetic hand to amputees with seamless neural compatibility presents a grand challenge to neuroscientists and neural engineers for more than half *** anatomical structure or appearance of human hand does not lead to improved neural connectivity to the sensorimotor system of *** functions of modern prosthetic hands do not match the dexterity of human hand due primarily to lack of sensory awareness and compliant ***,progress in restoring sensory feedback has marked a significant step forward in improving neural continuity of sensory information from prosthetic hands to ***,little effort has been made to replicate the compliant property of biological muscle when actuating prosthetic ***,a full-fledged biorealistic approach to designing prosthetic hands has not been contemplated in neuroprosthetic *** this perspective article,we advance a novel view that a prosthetic hand can be integrated harmoniously with amputees only if neural compatibility to the sensorimotor system is *** ongoing research supports that the next-generation prosthetic hand must incorporate biologically realistic actuation,sensing,and reflex functions in order to fully attain neural compatibility.

关键词： neural compatibility primarily

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Effects of Uni- and Bidirectional Interaction During Dyadic Ankle and Wrist Tracking

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IEEE Transactions on Neural Systems and Rehabilitation Engineering 2025年 33卷 2013-2024页

作者： Short, Matthew R. Ludvig, Daniel Di Tommaso, Francesco Vianello, Lorenzo Kucuktabak, Emek Baris Perreault, Eric J. Hargrove, Levi Lynch, Kevin Burdet, Etienne Pons, Jose L. Shirley Ryan AbilityLab ChicagoIL United States Northwestern University Department of Biomedical Engineering EvanstonIL United States Northwestern University Department of Mechanical Engineering EvanstonIL United States Center for Robotics and Biosystems EvanstonIL United States Department of Physical Medicine and Rehabilitation Northwestern University ChicagoIL United States Università Campus Bio-Medico di Roma Rome Italy Imperial College of Science Technology and Medicine Department of Bioengineering London United Kingdom

Haptic human-robot-human interaction allows users to feel and respond to one another's forces while interfacing with separate robotic devices, providing customizable infrastructure for studying physical interaction during motor tasks (e.g., physical rehabilitation). For upper- and lower-limb tracking tasks, previous work has shown that virtual interactions with a partner can improve motor performance depending on the skill level of each partner. However, whether the mechanism explaining these improvements is identical in the upper and lower limbs is an open question. In this work, we investigate the effects of haptic interaction between healthy individuals during a trajectory tracking task involving single-joint movements at the wrist and ankle. We compare tracking performance and muscle activation during haptic conditions where pairs of participants were uni- and bidirectionally connected to investigate the contribution of real-time responses from a partner during the interaction. Findings showed similar improvements in tracking performance during bidirectional interaction for both the wrist and ankle. This was observed despite distinct strategies in muscle co-contraction between joints, as co-contraction was dependent on partner ability for the wrist but not the ankle. For each joint, bidirectional and unidirectional interaction resulted in similar improvements for the worse partner in the dyad. For the better partner, bidirectional interaction resulted in greater improvements than unidirectional interaction. While these results suggest that unidirectional interaction is sufficient for error correction of less skilled individuals during simple motor tasks, they also highlight the mutual benefits of bidirectional interaction which are consistent across the upper and lower limbs. © 2001-2011 IEEE.

关键词： Error correction

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MS-Glance: Bio-Inspired Non-Semantic Context Vectors and Their Applications in Supervising Image Reconstruction

MS-Glance: Bio-Inspired Non-Semantic Context Vectors and The...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Ziqi Gao Wendi Yang Yujia Li Lei Xing S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China Department of Radiation Oncology Stanford University Stanford CA USA Key Laboratory of Precision and Intelligent Chemistry USTC CAS Hefei Anhui

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction, non-semantic information, such as global spatial structures, is often overlooked. To bridge the gap, we propose a biologically informed non-semantic context descriptor, MS-Glance, along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context, while a local Glance vector is a flattened local image window, mimicking a zoom in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://***/Z7Gao/MSGlance.

关键词： Computer vision Visualization Magnetic resonance imaging Fitting Vectors Image restoration Indexes Image reconstruction biomedical imaging Visual perception

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基于自解耦三明治结构的横向运动栅场效应晶体管MEMS微力传感器

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Engineering 2023年第2期21卷 61-74,M0004页

作者：高文迪乔智霞韩香广王小章 Adnan Shakoor 刘存朗卢德江杨萍赵立波王永录王久洪蒋庄德孙东 State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150006China Eleventh Research Institute Sixth Academy of China Aerospace Science and Technology Co.Xi’an 710100China Department of Control and Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Department of Biomedical Engineering City University of Hong KongHong Kong 999077China

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵... 详细信息

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵列层和两个软光阻层SU-8组成。通过所提出的双差分传感结构,LMGFET横向工作在垂直干扰下的输出电流被大大消除,所提出传感器的相对输出误差从4.53%(传统差分结构)降低到0.01%。提出了一种实用的传感器制造工艺,并对其进行了模拟。基于LMGFET的力传感器的灵敏度为4.65μA·nN^(-1),可与垂直可移动栅极场效应晶体管(VMGFET)器件相媲美,但非线性度提高了0.78%,测量范围扩大了±5.10μN。这些分析为LMGFET器件的电气和结构参数提供了全面的设计优化,并证明了所提出的传感器在生物医学显微操作应用中具有良好的力传感潜力。

关键词：力传感器场效应晶体管横向运动三明治结构电气模型显微操作非线性度双差分

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SubZero: Subspace Zero-Shot MRI Reconstruction

arXiv

引用

arXiv 2023年

作者： Yu, Heng Arefeen, Yamin Bilgic, Berkin The Robotics Institute Carnegie Mellon University PittsburghPA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States

Recently introduced zero-shot self-supervised learning (ZS-SSL) has shown potential in accelerated MRI in a scan-specific scenario, which enabled high quality reconstructions without access to a large training dataset. ZS-SSL has been further combined with the subspace model to accelerate 2D T2-shuffling acquisitions. In this work, we propose a parallel network framework and introduce attention mechanism to improve subspace based zero-shot self-supervised learning and enable higher acceleration factors. We name our method SubZero and demonstrate that it can achieve improved performance compared with current methods in T1 and T2 mapping acquisitions. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Deep Brain Stimulation of Anterior Nuclei of the Thalamus and Hippocampal Seizure Rate Modulate Verbal Memory Performance

Deep Brain Stimulation of Anterior Nuclei of the Thalamus an...

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2022 IEEE International Conference on Electro Information technology, eIT 2022

作者： Marks, Victoria S. Richner, Tom J. Gregg, Nicholas M. Sladky, Vladimir Dolezal, Jaromir Kremen, Vaclav Worrell, Gregory A. Kucewicz, Michal T. Mayo Clinic Mayo Clinic Graduate School of Biomedical Sciences RochesterMN United States Mayo Clinic Bioelectronics Neurophysiology and Engineering Laboratory Department of Neurology RochesterMN United States Mayo Clinic Brain Adaptation and Stimulation Lab Department of Neurology RochesterMN United States Czech Technical University in Prague Faculty of Biomedical Engineering Kladno Prague Czech Republic St. Anne's University Hospital International Clinical Research Center Brno Czech Republic Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Prague Czech Republic Gdansk University of Technology BioTechMed Center Department of Multimedia Systems Faculty of Electronics Telecommunications and Informatics Gdansk Poland

ISBN: (纸本)9781665480093

Verbal memory impairment is a common symptom of temporal lobe epilepsy (TLE). The ability to encode and recall verbal memories can be probed with the classic free recall task. We hypothesized that increased seizure frequency will lead to decreased verbal memory. We investigated this hypothesis in patients receiving chronic stimulation to bilateral anterior nuclei of the thalamus (ANT) deep brain stimulation (DBS). Recordings were obtained from individuals with drug resistant mesial TLE implanted with an investigational Medtronic Summit RC+S sensing and stimulation device. As patient seizure diaries are notoriously inaccurate, the patient's intracranial electroencephalography (iEEG) was scored for seizures using a validated seizure classifier with trained epileptologist review, resulting in a reliable seizure diary. The subjects completed free recall memory tasks in their home environment with iEEG and behavioral data streamed to a handheld device and cloud repository. Using multivariate analysis of variance, we were able to determine that ANT stimulation modulates memory performance. Further, changes in the seizure rate were predictive of changes in memory performance as captured by a generalized linear model (GLM). Emerging devices for electrical stimulation of the brain enabling continuous streaming of the iEEG data are crucial for long-term studies of underlying brain dynamics during cognitive tasks. However, not all patients are candidates for devices with chronic sensing capabilities, and this study suggests that changes in verbal memory performance may provide a suitable metric of therapy effectiveness in lieu of a reliable, objective seizure diary. © 2022 IEEE.

关键词： Electroencephalography

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Development a cross-loop during adaptive stimulation of hippocampal neural networks by an artificial neural network 4

Development a cross-loop during adaptive stimulation of hipp...

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4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022

作者： Lebedeva, Albina Beltyukova, Anna Fedulina, Anastasiya Gerasimova, Svetlana Mishchenko, Mikhail Matveeva, Mariya Maltseva, Kseniya Belov, Alexey Mikhaylov, Alexey Pisarchik, Alexander Kazantsev, Victor University of Nizhny Novgorod Neurotechnologies Department N.I. Lobachevsky State Nizhny Novgorod Russia University of Nizhny Novgorod Faculty of Radiophysics N.I. Lobachevsky State Department of Theory of Oscillations and Automatic Control Nizhny Novgorod Russia Intitute of Neuroscience N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Research Institute of Physics and Technology N.I. Lobachevsky State University of Nizhny Novgorod Laboratory of Physics and Technology of Thin Films Department of Solid State Electronics and Optoelectronics Nizhny Novgorod Russia Center for Biomedical Technology Technical University of Madrid Pozuelo de Alarcón Madrid Spain Center for Technologies in Robotics and Mechatronics Component Innopolis University Innopolis Russia Center for Neuroscience and Mashine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (数字)9781665463294

ISBN: (纸本)9781665463294

The development of promising neuromorphic compact devices (including micro- and nanochips) that reproduce the information and computational functions of the brain is currently one of the breakthrough areas of modern science and technology. Firstly, this is a promising direction in regenerative medicine - the creation of artificial neuromorphic implants that allow the regulation and modulation of electrically excitable cells, as well as replacing damaged areas of the brain. The use of a memristive structure and its ability to change and maintain internal resistance when voltage is applied and turned off. This makes the structure an almost perfect electronic analogue of synapses. Recent discoveries that nanoscale memristors can mimic the basic properties of synaptic plasticity have set the stage for the fusion of nanoelectronics and living neurons to create biohybrid neural networks (bNNs) interconnected by electronics-based plasticity elements. Thus, investigation the interaction of artificial neural networks and memristive connection with living brain neurons is actually problem in neuroscience and technologies. This study is devoted to the development of neuromorphic system for neuroprosthetics problems. © 2022 IEEE.

关键词： Memristors

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning

Preliminary Assessment of Two Simultaneous and Proportional ...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Fabio Egle Dario Di Domenico Andrea Marinelli Nicolò Boccardo Michele Canepa Matteo Laffranchi Lorenzo De Michieli Claudio Castellini Department Artificial Intelligence in Biomedical Engineering Assistive Intelligent Robotics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Rehab Technologies Lab Italian Institute of Technology Genova Italy Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Bioengineering Lab University of Genova DIBRIS Genova Italy Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT) Genova Italy German Aerospace Center Institute of Robotics and Mechatronics Wessling Germany

Despite progressive developments over the last decades, current upper limb prostheses still lack a suitable control able to fully restore the functionalities of the lost arm. Traditional control approaches for prostheses fail when simultaneously actuating multiple Degrees of Freedom (DoFs), thus limiting their usability in daily-life scenarios. Machine learning, on the one hand, offers a solution to this issue through a promising approach for decoding user intentions but fails when input signals change. Incremental learning, on the other hand, reduces sources of error by quickly updating the model on new data rather than training the control model from scratch. In this study, we present an initial evaluation of a position and a velocity control strategy for simultaneous and proportional control over 3-DoFs based on incremental learning. The proposed controls are tested using a virtual Hannes prosthesis on two healthy participants. The performances are evaluated over eight sessions by performing the Target Achievement Control test and administering SUS and NASA-TLX questionnaires. Overall, this preliminary study demonstrates that both control strategies are promising approaches for prosthetic control, offering the potential to improve the usability of prostheses for individuals with limb loss. Further research extended to a wider population of both healthy subjects and amputees will be essential to thoroughly assess these control paradigms.

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Gesture Control Method for Oral Blood Mixed Liquid Removal Robot∗ 5

Gesture Control Method for Oral Blood Mixed Liquid Removal R...

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5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Ma, Xudong Kuang, Ziao Li, Han Chen, Anqi Li, Changsheng Liu, Wenyong Wang, Junchen Hu, Yida David Gao, Li Su, Bai-Quan Beijing University of Posts and Tele-communications Medical Robotics Laboratory School of Automation Beijing100876 China Beijing University of Posts and Tele-communications School of Computer Beijing100876 China Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beihang University Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beijing100083 China Beihang University School of Mechanical Engineering and Automation Beijing100083 China Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Peking University School and Hospital of Stomatology National Clinical Research Center for Oral Diseases National Stomatological Center Department of Periodontology Beijing China

ISBN: (纸本)9798350307320

All of oral surgeries are accompanied by bleeding. Manual removal of blood is carried out by a nurse and dozens of repetitive blood suction operations are implemented by the nurse during a single oral surgery. In our previous work, a robot for removal of blood-water mixed liquid is designed. However, the robot requires an initial order to autonomous removal of blood mixed liquid. In this paper, a hand gesture control method for typical operations of the robot is developed. Gesture dataset are developed at first and a deep learning neural network is trained for detection of hand gesture. The gesture control procedures for four typical operations are provided in detail. In a group of experiments, it's demonstrated that an oral surgeon controls the robot's motion by presenting prescribed hand gesture. The proposed method could be an alternative manner for blood mixed liquid removal. © 2023 IEEE.

关键词： Blood

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