IEEE transactions on medical robotics and bionics最新文献_第5页

Toward Scalable Bionic Cardiac Systems: Energy-Efficient and High-Performance FPGA Modeling of Pacemaker Cells via Nonlinear Simplification 迈向可扩展的仿生心脏系统：基于非线性简化的起搏器细胞的节能和高性能FPGA建模

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699219

Milad Ghanbarpour;Muhammad Akmal Chaudhary;Maher Assaad;Gilda Ghanbarpour

引用次数: 0

Remote Catheter System With Enhanced Haptic Feedback for Use in Endovascular Procedures 用于血管内手术的增强触觉反馈远程导管系统

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-07-16 DOI: 10.1109/TMRB.2026.3713994

Takenori Akiyama;Tomoya Kitamura;Hirotsugu Nogawa;Takashi Iwama;Yuki Saito;Hiroshi Asai;Shunsuke Shibao;Katsuhiro Mizutani;Kouhei Ohnishi

{"title":"Remote Catheter System With Enhanced Haptic Feedback for Use in Endovascular Procedures","authors":"Takenori Akiyama;Tomoya Kitamura;Hirotsugu Nogawa;Takashi Iwama;Yuki Saito;Hiroshi Asai;Shunsuke Shibao;Katsuhiro Mizutani;Kouhei Ohnishi","doi":"10.1109/TMRB.2026.3713994","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3713994","url":null,"abstract":"We propose a sensorless remote catheter control system that delivers real-time haptic feedback. Bilateral control combined with a reaction force observer estimates contact forces and transmits them to the operator in real time, while frequency-shaped feedback improves the perceived sensitivity and reduces fatigue. Using three catheter types and three operators, we evaluated position–force synchronization, loop delay, and vessel-wall load relative to manual manipulation. The average position error was approximately <inline-formula> <tex-math>$2.14times 10^{-5}{,}mathrm {m}$ </tex-math></inline-formula>, and the force error was <inline-formula> <tex-math>$0.493{,}mathrm {N}$ </tex-math></inline-formula>. Cross-correlation (<inline-formula> <tex-math>$leq 1{,}mathrm {Hz}$ </tex-math></inline-formula>) showed no measurable lag in the low-frequency synchronization analysis within the 1 ms resolution. For force equivalence, the <italic>prespecified primary analysis employed a two one-sided tests (TOST) procedure based on estimates obtained from a linear mixed-effects (LME) model, with the smallest effect size set to ±10% of the manual mean; equivalence was supported (b <inline-formula> <tex-math>$= -0.029$ </tex-math></inline-formula>, 90% confidence interval [−0.119, 0.061]), whereas a stricter ±5% sensitivity analysis was not supported. All operators completed the tasks after minimal system familiarization. These findings indicate that the system replicates manual precision in a benchtop phantom setting while enabling real-time haptic feedback and maintaining vessel-wall loading comparable to that of manual manipulation. Our sensorless haptic technology represents a promising step toward future clinical translation of remote catheter systems, with applications in safer, standardized, and more intuitive endovascular procedures.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1428-1437"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11612960","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HoleNet: Neural Network for 5-DoF Skull-Hole-Center Localization for Minimally Invasive Brain–Machine Interfaces Surgery HoleNet：用于微创脑机接口手术五自由度颅孔中心定位的神经网络

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699212

Angen Ye;Xiaofeng Wang;Yujia Song;Yuqing Li;Shan Yu;Dapeng Zhang

{"title":"HoleNet: Neural Network for 5-DoF Skull-Hole-Center Localization for Minimally Invasive Brain–Machine Interfaces Surgery","authors":"Angen Ye;Xiaofeng Wang;Yujia Song;Yuqing Li;Shan Yu;Dapeng Zhang","doi":"10.1109/TMRB.2026.3699212","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699212","url":null,"abstract":"Minimally invasive surgery shows promise in Brain-machine interfaces (BMIs), as it facilitates sampling specific neural signals with a low risk of infection. During the minimally invasive operation, 5-Degree of Freedom (DoF) skull-hole-center localization (3-DoF point, 2-DoF orientation) is desired to insert micro-electrodes. Conventional circular-hole localization methods are based on fitting ellipses, which is numerically unstable in challenging BMIs scenarios. Although learning-based ellipse detection methods alleviate this problem, they require sophisticated circular contour annotations. Unlike the aforementioned ellipse-based approaches, we introduce HoleNet which leverages 2D keypoints representation, requiring minimal annotation effort. Based on stereo 2D keypoints, the geometric and photometric constraints are imposed to match stereo keypoints for triangulation. Besides, we introduce the Stereo Hough Voting module to enhance 3D consistency. Finally, to determine the 2-DoF orientation, a skull-surface plane is regressed based on the proposed <italic>coplanarity assumption. We provide theoretical analysis to verify the <italic>coplanarity assumption and conduct extensive experiments to validate the proposed method. The results show that the mean distance error of the 3-DoF point is lower than <inline-formula> <tex-math>$15{mathrm {mu text {m} }}$ </tex-math></inline-formula>, and the mean relative angle error of the 2-DoF orientation is within 3°, which satisfies the requirement for collision-free in surgery process.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1086-1098"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Slope Grade Using Knee Joint Angle Data Based on a GAF-CNN Algorithm 基于GAF-CNN算法的膝关节角度坡度分类

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699208

Zhuo Wang;Chunjie Chen;Fangliang Yang;Hui Chen;Jiale Zhang;Xiangyang Wang;Xinyu Wu

{"title":"Classification of Slope Grade Using Knee Joint Angle Data Based on a GAF-CNN Algorithm","authors":"Zhuo Wang;Chunjie Chen;Fangliang Yang;Hui Chen;Jiale Zhang;Xiangyang Wang;Xinyu Wu","doi":"10.1109/TMRB.2026.3699208","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699208","url":null,"abstract":"This study proposes a Gramian angular field-convolutional neural network (GAF-CNN) framework for classifying slope-grade groups using knee joint angle data. First, non-negative matrix factorization was applied to an open-source slope-walking dataset to analyze muscle synergies, revealing that the biceps femoris, semitendinosus, and rectus femoris were the primary contributors to knee-angle variations. Based on Pearson correlation analysis, a weighted combination of the root-mean-square (RMS) values of the biceps femoris and rectus femoris was selected as a representative indicator. Using this indicator, the 27 treadmill slope levels used in the experiments, ranging from −6% to 20%, were clustered into 13 slope-grade groups as classification labels. Three input representations, namely Datastance, Dataswing, and Datagait, were evaluated using three CNN architectures: a 1D-CNN, a Gramian summation angular field CNN (GASF-CNN), and a Gramian difference angular field CNN (GADF-CNN), yielding nine input–model combinations. The GADF-CNN using Datagait achieved the highest accuracy of 95.27%, followed by Datastance at 95.18%, whereas Dataswing produced the lowest accuracy of 95.05% when five convolutional blocks were used. The proposed framework exhibited stable performance under leave-one-subject-out and group-wise five-fold cross-validation, demonstrating its potential for real-time deployment. Overall, GAF transformed knee-angle sequences into images, improving slope-grade classification accuracy.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1206-1217"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using the Internal Operational Logs of the hinotori Surgical Robot System to Evaluate Latent Structural Metrics of Robotic Surgical Skill 利用hinotori手术机器人系统的内部操作日志评估机器人手术技能的潜在结构指标

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699228

Munenori Uemura;Kiyoyuki Chinzei;Takeshi Urade;Nobuaki Yamasaki;Junichiro Hirata;Yasuyoshi Okamura;Tomoko Yamaguchi;Hideto Ueki;Kaito Nanchi;Tatsuya Hattori;Yuki Mitani;Yasuo Chihara;Seiichi Ozawa;Takumi Fukumoto

{"title":"Using the Internal Operational Logs of the hinotori Surgical Robot System to Evaluate Latent Structural Metrics of Robotic Surgical Skill","authors":"Munenori Uemura;Kiyoyuki Chinzei;Takeshi Urade;Nobuaki Yamasaki;Junichiro Hirata;Yasuyoshi Okamura;Tomoko Yamaguchi;Hideto Ueki;Kaito Nanchi;Tatsuya Hattori;Yuki Mitani;Yasuo Chihara;Seiichi Ozawa;Takumi Fukumoto","doi":"10.1109/TMRB.2026.3699228","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699228","url":null,"abstract":"Surgical robot systems generate detailed kinematic telemetry, yet these internal logs remain underexplored as a resource for quantitative analysis of operative behavior. This study investigated whether motion features derived from maintenance-grade telemetry of the hinotori Surgical Robot System capture the characteristics of robotic surgical expertise. Thirty-nine participants—robotic surgeons, laparoscopic surgeons without robotic experience (Lap), and novices—performed a standardized suturing task while internal motion logs were recorded. Motion features, including conventional kinematic metrics and geometric coordination descriptors, were computed from instrument and endoscope trajectories. Feature importance was analyzed using multiple tree-based machine-learning models with repeated resampling. Across all models, spatial coordination features consistently ranked highest. The most robust feature was the Triangular Stability Index (TSI), which represented the geometric stability of the endoscope–instrument configuration. Robotic surgeons exhibited significantly lower and more stable TSI values than Lap and novice participants, indicating coordinated scope–arm control. These findings suggest that robotic expertise may be characterized by robot-specific visuomotor coordination rather than general surgical experience. Internal telemetry provides an objective and scalable foundation for behavioral analysis, surgical training, and adaptive robotic assistance.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1015-1024"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Residual-Limb Movement-Based Embedded Sensing System for Prosthetic Devices 基于残肢运动的嵌入式假肢传感系统

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-07-17 DOI: 10.1109/TMRB.2026.3713988

Mohammad Ehshan Khan;Benish Jan;Iqbal Kagdi;Shufali Ashraf Wani;Hadi Heidari;Shahid Malik

{"title":"Residual-Limb Movement-Based Embedded Sensing System for Prosthetic Devices","authors":"Mohammad Ehshan Khan;Benish Jan;Iqbal Kagdi;Shufali Ashraf Wani;Hadi Heidari;Shahid Malik","doi":"10.1109/TMRB.2026.3713988","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3713988","url":null,"abstract":"In contrast to electromyography (EMG), which quantifies electrical activity, Forcemyography (FMG) captures mechanical deformations during contraction, making it less vulnerable to noise, electromagnetic interference, and perspiration. However, it still depends on muscle volumetric changes, sensor contact variability, and fatigue, limiting prosthetic control reliability. This paper introduces a novel human-machine interface (HMI) for transradial prosthetic devices, utilizing force-sensitive resistors (FSRs) embedded in the prosthetic socket rather than affixed to muscles, ensuring independence from muscle-related variability. Since the system relies on residual-limb movements, it ensures full independence from muscle activity. Experimental validation involving 30 healthy individuals and one subject with upper-limb impairment revealed enhanced signal stability and binary classification accuracy of 94.7% versus 82.9% for a single-channel threshold-based EMG baseline. The proposed FSR-based system demonstrated superior robustness, sustaining high classification accuracy under both perspiration and fatigue conditions, while EMG exhibited substantial performance degradation. Key contributions include: 1) novel sensing system for binary prosthetic control based on stump rotation; 2) constant current-excitation circuit that linearly converts resistance changes; 3) microcontroller-implemented transformation algorithm optimizing FSR output; and 4) proof-of-concept prototype with two FSR sensors. Results validate enhanced performance and robustness of the FSR-based HMI for binary control tasks, providing a promising proof-of-concept for robust and user-friendly prosthetic control.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1276-1286"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motion Analysis of a Multi-Stiffness Concentric Tube Magnetic Continuum Robot and Its Remote Constraint Application for Minimally Invasive Surgical Operations 多刚度同心管磁连续机器人运动分析及其在微创手术中的远程约束应用

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699226

Wenguang Yang;Huibin Liu;Zhixing Ge;Haibo Yu

{"title":"Motion Analysis of a Multi-Stiffness Concentric Tube Magnetic Continuum Robot and Its Remote Constraint Application for Minimally Invasive Surgical Operations","authors":"Wenguang Yang;Huibin Liu;Zhixing Ge;Haibo Yu","doi":"10.1109/TMRB.2026.3699226","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699226","url":null,"abstract":"Conventional magnetic continuum robots (MCRs) show great potential for minimally invasive surgery due to their simple structure and ease of manipulation. However, their monolithic continuous structure and single-stiffness design limit kinematic performance and manipulation accuracy. To address these limitations, we developed a concentric tube magnetic continuum robot (CT-MCR) system with replaceable concentric tubes and adjustable magnetic particle positions. Motion analysis and modeling were performed using the Cosserat rod model to optimize structural characteristics and enhance manipulation accuracy. Within a 270° driving range, the operating end-effector achieves stable trajectory following with a fit rate exceeding 90%. The distal constraint effect minimizes end-effector hopping, improving stability by over 50% compared to conventional MCRs. Navigation and foreign body retrieval experiments in a complex branching vascular model within a 3D Helmholtz coil workspace demonstrate the CT-MCR’s potential for targeted drug delivery and minimally invasive cardiovascular surgery.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1420-1427"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic-Based Autonomous Context Tracking in Cognitive Robotic Surgery 认知机器人手术中基于语义的自主上下文跟踪

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-07-13 DOI: 10.1109/TMRB.2026.3712828

Andrea Roberti;Marco Bombieri;Daniele Meli;Riccardo Muradore

{"title":"Semantic-Based Autonomous Context Tracking in Cognitive Robotic Surgery","authors":"Andrea Roberti;Marco Bombieri;Daniele Meli;Riccardo Muradore","doi":"10.1109/TMRB.2026.3712828","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3712828","url":null,"abstract":"Autonomous robotic surgery can benefit from advances in artificial intelligence to improve the outcome of surgical procedures, enhance situation awareness, and optimize the user experience of surgeons. In this paper, we focus on the important problem of autonomous context tracking in robotic surgery, aiming at tracking the relevant items in the surgical scene with the endoscopic camera arm (ECM) of the da Vinci Research Kit (dVRK) robot. We propose SemTrack, a novel method to track both instruments and anatomical parts of interest, and overcome the lack of interpretability and trustworthiness of recent deep learning solutions. We leverage natural language processing to extract a symbolic task formalization from surgical notes and texts. We then use this interpretable formalization to track the flow of the phases in the operation, including both inter-phase transitions and intra-phase target anatomies and instruments. In the context of the tumor removal of phantom-based lateral partial nephrectomy, we validate the feasibility and accuracy of our methodology at tracking relevant scene items for enhanced situation awareness. Moreover, our approach has better performance also in terms of task duration, even with moving targets, than static and teleoperated ECM.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1152-1160"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11606464","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Medical Robotics and Bionics Information for Authors IEEE医学机器人与仿生学信息汇刊

IF 3.8

IEEE transactions on medical robotics and bionics Pub Date : 2026-03-10 DOI: 10.1109/TMRB.2025.3648633

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IEEE Transactions on Medical Robotics and Bionics Society Information 医学机器人与仿生学学会汇刊

IF 3.8

IEEE transactions on medical robotics and bionics Pub Date : 2026-03-10 DOI: 10.1109/TMRB.2025.3648631

引用次数: 0