The development of procedure-specific surgical robots has become essential for tackling complex clinical challenges. Flexible bronchoscope robots (FBRs) have emerged over the past decade, revealing broad prospects for the safe, precise, and reliable diagnosis of peripheral pulmonary nodules (PPNs), which is crucial for enabling early lung cancer treatment. However, in advancing FBR development, roboticists sometimes stray from or overlook practical surgical considerations, which might impede its clinical implementation. This review aims to bridge this gap by offering an engineering-focused perspective enriched with critical medical insights to drive the clinical translation of next-generation FBRs. We begin by highlighting the medical significance and current state of FBR research. Then, we outline the “ambient environments” of FBRs: the supported procedure, robotic system, steering tools, and deployment modes. Subsequently, we summarize recent progress in FBR technology, focusing on two key areas: procedure-specific design and modeling to improve intervention capabilities, and autonomous navigation and control strategies to enhance autonomy. Based on the given analysis, we discuss the development directions of next-generation FBRs according to the current clinical challenges and the engineering approaches to their realization.
{"title":"A Review of Flexible Bronchoscope Robots for Peripheral Pulmonary Nodule Intervention","authors":"Yuzhou Duan;Jie Ling;Micky Rakotondrabe;Zuoqing Yu;Lei Zhang;Yuchuan Zhu","doi":"10.1109/TMRB.2025.3583172","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3583172","url":null,"abstract":"The development of procedure-specific surgical robots has become essential for tackling complex clinical challenges. Flexible bronchoscope robots (FBRs) have emerged over the past decade, revealing broad prospects for the safe, precise, and reliable diagnosis of peripheral pulmonary nodules (PPNs), which is crucial for enabling early lung cancer treatment. However, in advancing FBR development, roboticists sometimes stray from or overlook practical surgical considerations, which might impede its clinical implementation. This review aims to bridge this gap by offering an engineering-focused perspective enriched with critical medical insights to drive the clinical translation of next-generation FBRs. We begin by highlighting the medical significance and current state of FBR research. Then, we outline the “ambient environments” of FBRs: the supported procedure, robotic system, steering tools, and deployment modes. Subsequently, we summarize recent progress in FBR technology, focusing on two key areas: procedure-specific design and modeling to improve intervention capabilities, and autonomous navigation and control strategies to enhance autonomy. Based on the given analysis, we discuss the development directions of next-generation FBRs according to the current clinical challenges and the engineering approaches to their realization.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"845-862"},"PeriodicalIF":3.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887776","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}
Surgical robots are used in minimally invasive surgery. The operator performs compensatory movements because the robot differs structurally from the human hand and arm. This paper investigates surgical robots’ optimal configuration by considering variations in the operator’s suturing accuracy and muscle burden across trials and between individual operators. The design factors were the motion scale, viewing angle, pivot point, tip length, and needle gripping angle. We developed a virtual surgical simulator implementing haptic feedback. As 20 participants manipulated the simulator in three trials for 27 surgical robot configurations, we evaluated each participant’s suturing error and joint burden. Considering the variation among trial and individual differences, we investigated the best-fitted probability distribution model, calculated the expectation and deviation in each index using the reliability design method, and constructed the response surface for the relationship between factors. Furthermore, we optimized the surgical robot using the satisficing trade-off method. Finally, when comparing theoretical and experimental values for the best solution, relative errors in suturing error and muscle burden were less than 7.14% and 15.1%, respectively. Moreover, the optimized surgical robot improved suturing error and joint energy by 18% relative to the average values across the 27 configurations.
{"title":"Optimization of Surgical Robotic Configuration Considering Variations in Suturing Accuracy and Muscle Burden Through Virtual Simulation","authors":"Satoshi Miura;Hirotaro Kikuchi;Victor Parque;Tomoyuki Miyashita","doi":"10.1109/TMRB.2025.3583145","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3583145","url":null,"abstract":"Surgical robots are used in minimally invasive surgery. The operator performs compensatory movements because the robot differs structurally from the human hand and arm. This paper investigates surgical robots’ optimal configuration by considering variations in the operator’s suturing accuracy and muscle burden across trials and between individual operators. The design factors were the motion scale, viewing angle, pivot point, tip length, and needle gripping angle. We developed a virtual surgical simulator implementing haptic feedback. As 20 participants manipulated the simulator in three trials for 27 surgical robot configurations, we evaluated each participant’s suturing error and joint burden. Considering the variation among trial and individual differences, we investigated the best-fitted probability distribution model, calculated the expectation and deviation in each index using the reliability design method, and constructed the response surface for the relationship between factors. Furthermore, we optimized the surgical robot using the satisficing trade-off method. Finally, when comparing theoretical and experimental values for the best solution, relative errors in suturing error and muscle burden were less than 7.14% and 15.1%, respectively. Moreover, the optimized surgical robot improved suturing error and joint energy by 18% relative to the average values across the 27 configurations.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"993-1004"},"PeriodicalIF":3.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11051027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887825","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}
Pub Date : 2025-06-25DOI: 10.1109/TMRB.2025.3583142
M. V. Mallikarjuna Reddy;S. N. Kartik;P. S. Pandian;P. A. Karthick
Surface electromyography (sEMG) signals from the coactivation of agonist and antagonist muscles can provide precise and powerful control of a lower limb prosthesis along with proprioceptive sensory feedback. However, the analysis of coactivation is challenging due to the inherent nonlinearity of the signals and the nonlinear interactions within the muscular systems during dynamic contractions. In this study, a novel nonlinear approach based on symbolic transfer entropy (STE) is proposed to characterize the coactivation of muscles at different speeds of gait. For this purpose, the sEMG is recorded from the rectus femoris (RF) and vastus lateralis (VL) of the quadriceps, as well as the biceps femoris (BF) and semitendinosus (SEM) of the hamstring muscles. The signals are collected from 20 healthy subjects walking on a treadmill at gait speeds of 2.5, 3.5, and 4.5 kilometres per hour (km/h). In addition, the knee joint angles are also obtained from the inertial measurement units. The sEMG signals are pre-processed, and eight distinct phases of gait are segmented using joint angles. A suitable symbolic scale is selected after a detailed analysis, and STE is extracted to characterize the coactivation of agonist and antagonist muscle pairs: RF-BF, RF-SEM, VL-BF and VL-SEM. The results show that STE increases with gait speed irrespective of muscle combinations, which indicates the stronger coactivation during faster locomotion. The variation of STE with respect to each phase exhibits a complex dynamic pattern in muscle coactivation. The information transfer is bidirectional and the distribution of STE is found to have significant differences across directions, phases and speeds (p¡0.001). Furthermore, the proposed STE is superior to traditional transfer entropy in terms of capturing nonlinear interactions. The study facilitates researchers in developing gait phase-based features that account for coactivation, enabling them to achieve significantly more natural and efficient gait patterns in prosthetic lower limbs.
{"title":"Lower Limb Muscle Coactivation Analysis Using Symbolic Transfer Entropy of Simultaneous Surface EMG Measurements","authors":"M. V. Mallikarjuna Reddy;S. N. Kartik;P. S. Pandian;P. A. Karthick","doi":"10.1109/TMRB.2025.3583142","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3583142","url":null,"abstract":"Surface electromyography (sEMG) signals from the coactivation of agonist and antagonist muscles can provide precise and powerful control of a lower limb prosthesis along with proprioceptive sensory feedback. However, the analysis of coactivation is challenging due to the inherent nonlinearity of the signals and the nonlinear interactions within the muscular systems during dynamic contractions. In this study, a novel nonlinear approach based on symbolic transfer entropy (STE) is proposed to characterize the coactivation of muscles at different speeds of gait. For this purpose, the sEMG is recorded from the rectus femoris (RF) and vastus lateralis (VL) of the quadriceps, as well as the biceps femoris (BF) and semitendinosus (SEM) of the hamstring muscles. The signals are collected from 20 healthy subjects walking on a treadmill at gait speeds of 2.5, 3.5, and 4.5 kilometres per hour (km/h). In addition, the knee joint angles are also obtained from the inertial measurement units. The sEMG signals are pre-processed, and eight distinct phases of gait are segmented using joint angles. A suitable symbolic scale is selected after a detailed analysis, and STE is extracted to characterize the coactivation of agonist and antagonist muscle pairs: RF-BF, RF-SEM, VL-BF and VL-SEM. The results show that STE increases with gait speed irrespective of muscle combinations, which indicates the stronger coactivation during faster locomotion. The variation of STE with respect to each phase exhibits a complex dynamic pattern in muscle coactivation. The information transfer is bidirectional and the distribution of STE is found to have significant differences across directions, phases and speeds (p¡0.001). Furthermore, the proposed STE is superior to traditional transfer entropy in terms of capturing nonlinear interactions. The study facilitates researchers in developing gait phase-based features that account for coactivation, enabling them to achieve significantly more natural and efficient gait patterns in prosthetic lower limbs.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"1201-1211"},"PeriodicalIF":3.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887827","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}
Pub Date : 2025-06-25DOI: 10.1109/TMRB.2025.3583164
Haruki Umezawa;Chanvicharo Ly;Toru Omata
Force sensing for multi-degree-of-freedom articulated forceps is challenging for several reasons, including size constraints, the harsh environment inside the patient’s body, and sterilizability. This paper proposes a sensing method that overcomes the fragility and production cost disadvantages of conventional force/tension sensors that use flexure elements. Vibration-based tension sensing induced vibrations in forceps’ driving cables. The resulting fundamental frequencies were measured using photo interrupters. A rotating shaft was mounted on an axis parallel to cables suspended between two pulleys, with a small protrusion in the radial direction that plucked at the middle of those concentrically arranged cables. Cable tensions were estimated from the measured fundamental frequencies with satisfactory accuracy, repeatability, and vibration-to-noise magnitude ratio. The plucking negatively affects the wear of the protrusion and cables while inducing undesired vibration transmission and changes in the contact/grasping force at the forceps tip. A nylon protrusion and nylon-coated cables were used because nylon is wear-resistant, low-friction, biocompatible, and sterilizable. The results revealed minimal wear after 106 plucking cycles, minimal vibration transmissions and changes in contact force, and little interference with the photo interrupter signal from the plucking on another cable.
{"title":"Cable Vibration-Based Tension Sensing for Cable-Driven Articulated Forceps","authors":"Haruki Umezawa;Chanvicharo Ly;Toru Omata","doi":"10.1109/TMRB.2025.3583164","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3583164","url":null,"abstract":"Force sensing for multi-degree-of-freedom articulated forceps is challenging for several reasons, including size constraints, the harsh environment inside the patient’s body, and sterilizability. This paper proposes a sensing method that overcomes the fragility and production cost disadvantages of conventional force/tension sensors that use flexure elements. Vibration-based tension sensing induced vibrations in forceps’ driving cables. The resulting fundamental frequencies were measured using photo interrupters. A rotating shaft was mounted on an axis parallel to cables suspended between two pulleys, with a small protrusion in the radial direction that plucked at the middle of those concentrically arranged cables. Cable tensions were estimated from the measured fundamental frequencies with satisfactory accuracy, repeatability, and vibration-to-noise magnitude ratio. The plucking negatively affects the wear of the protrusion and cables while inducing undesired vibration transmission and changes in the contact/grasping force at the forceps tip. A nylon protrusion and nylon-coated cables were used because nylon is wear-resistant, low-friction, biocompatible, and sterilizable. The results revealed minimal wear after 106 plucking cycles, minimal vibration transmissions and changes in contact force, and little interference with the photo interrupter signal from the plucking on another cable.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"1349-1360"},"PeriodicalIF":3.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887685","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}
Vitreoretinal surgery includes a group of highly complex microsurgical procedures that demand precision. Robotic systems can enhance surgical performance, particularly for novice surgeons, while ensuring patient safety through advanced sensing capabilities. Optical Coherence Tomography (OCT), commonly used for eye anatomy imaging, is typically implemented via microscopes or diagnostic devices. This paper introduces the GEYEDANCE system, a bilateral teleoperated microsurgery platform integrating OCT directly at the end-effector of its remote manipulator, offering multi-modal feedback. The system enables intraoperative global eye modelling and surface reconstruction by exploiting a neural network-based tool-to-tissue distance estimation module. Its performance was validated in the operating room using ex vivo eyes, effectively simulating the surgical steps of various vitreoretinal procedures.
{"title":"GEYEDANCE: An OCT-Enhanced Multi-Modal Feedback Platform for Robot-Assisted Ophthalmic Surgery","authors":"Nicola Piccinelli;Ludwig Haide;Marius Briel;Alain Jungo;Eleonora Tagliabue;Tommaso Da Col;Moritz Schmid;Raphael Sznitman;Marco Pellegrini;Angeli Christy Yu;Massimo Busin;Marco Mura;Riccardo Muradore;Gernot Kronreif","doi":"10.1109/TMRB.2025.3583133","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3583133","url":null,"abstract":"Vitreoretinal surgery includes a group of highly complex microsurgical procedures that demand precision. Robotic systems can enhance surgical performance, particularly for novice surgeons, while ensuring patient safety through advanced sensing capabilities. Optical Coherence Tomography (OCT), commonly used for eye anatomy imaging, is typically implemented via microscopes or diagnostic devices. This paper introduces the GEYEDANCE system, a bilateral teleoperated microsurgery platform integrating OCT directly at the end-effector of its remote manipulator, offering multi-modal feedback. The system enables intraoperative global eye modelling and surface reconstruction by exploiting a neural network-based tool-to-tissue distance estimation module. Its performance was validated in the operating room using ex vivo eyes, effectively simulating the surgical steps of various vitreoretinal procedures.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"1017-1028"},"PeriodicalIF":3.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11051007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887799","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}
Pub Date : 2025-06-25DOI: 10.1109/TMRB.2025.3583182
Thomas E. Shkurti;M. Cenk Çavuşoğlu
The physically challenging and time-consuming nature of robotic minimally invasive surgery (RMIS) presents an incentive for automation of routine surgical tasks. We perform a comprehensive review of the current state of the art in the automation of laparoscopic surgical robots for the tasks of suturing, retraction, incision/dissection/resection, palpation, and debridement. Particular attention is paid to the various performance metrics employed by different studies, and methodological accommodations that differ from operating-room conditions. We conclude that the field remains in an exploratory state and rigorous definitions of success or performance in a given subtask have yet to materialize.
{"title":"A Systematic Review of Task Automation in Surgical Robotics","authors":"Thomas E. Shkurti;M. Cenk Çavuşoğlu","doi":"10.1109/TMRB.2025.3583182","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3583182","url":null,"abstract":"The physically challenging and time-consuming nature of robotic minimally invasive surgery (RMIS) presents an incentive for automation of routine surgical tasks. We perform a comprehensive review of the current state of the art in the automation of laparoscopic surgical robots for the tasks of suturing, retraction, incision/dissection/resection, palpation, and debridement. Particular attention is paid to the various performance metrics employed by different studies, and methodological accommodations that differ from operating-room conditions. We conclude that the field remains in an exploratory state and rigorous definitions of success or performance in a given subtask have yet to materialize.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"863-880"},"PeriodicalIF":3.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887666","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}
Pub Date : 2025-06-02DOI: 10.1109/TMRB.2025.3573024
Neri Niccolò Dei;Evangelos B. Mazomenos;Shuai Zhang;Sophia Bano;José M. M. Montiel;Danail Stoyanov;Gastone Ciuti
Colonoscopy is considered the gold standard for detecting and diagnosing colorectal cancer (CRC), which is the second most common cause of cancer-related deaths worldwide. While colonoscopy is generally safe and effective at reducing CRC mortality, lesions can be missed during procedures, with adverse impacts on the patient. Latest innovations in hardware and software led to the development of adjunct tools for complementing standard colonoscopy to ensure optimal outcomes. Such tools aim to enhance the detection of lesions, standardize procedures, enhance safety, and minimize discomfort. Ultimately, they contribute to reducing the morbidity and mortality rates associated with CRC. This survey comprehensively explores both clinically tested and emerging advanced hardware and software adjunct tools, categorizing them based on their role in targeting three clinical challenges: mucosal visualization, lesion detection and classification, and navigation and procedure assessment. Moreover, this analysis allows exploring synergistic strategies for the future of the practice, with a focus on the promising role of AI-embedded robotic technologies.
{"title":"Adjunct Tools for Colonoscopy Enhancement: A Comprehensive Review","authors":"Neri Niccolò Dei;Evangelos B. Mazomenos;Shuai Zhang;Sophia Bano;José M. M. Montiel;Danail Stoyanov;Gastone Ciuti","doi":"10.1109/TMRB.2025.3573024","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3573024","url":null,"abstract":"Colonoscopy is considered the gold standard for detecting and diagnosing colorectal cancer (CRC), which is the second most common cause of cancer-related deaths worldwide. While colonoscopy is generally safe and effective at reducing CRC mortality, lesions can be missed during procedures, with adverse impacts on the patient. Latest innovations in hardware and software led to the development of adjunct tools for complementing standard colonoscopy to ensure optimal outcomes. Such tools aim to enhance the detection of lesions, standardize procedures, enhance safety, and minimize discomfort. Ultimately, they contribute to reducing the morbidity and mortality rates associated with CRC. This survey comprehensively explores both clinically tested and emerging advanced hardware and software adjunct tools, categorizing them based on their role in targeting three clinical challenges: mucosal visualization, lesion detection and classification, and navigation and procedure assessment. Moreover, this analysis allows exploring synergistic strategies for the future of the practice, with a focus on the promising role of AI-embedded robotic technologies.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"910-925"},"PeriodicalIF":3.8,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887826","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}
Pub Date : 2025-04-18DOI: 10.1109/TMRB.2025.3562266
Naveed Ahmad Khan;Fahad Hussain;Tanishka Goyal;Prashant K. Jamwal;Shahid Hussain
Robotic-assisted rehabilitation for wrist movements demands adaptive systems capable of balancing patient autonomy with robotic support. The integration of artificial intelligence (AI) into robotic-assisted rehabilitation offers transformative potential in delivering personalized, dynamic, and effective therapeutic interventions. This study introduces a novel neuromechanical control framework integrating a passivity observer with Quantum-Enhanced Deep Reinforcement Learning (QDRL) for adaptive impedance scaling in wrist rehabilitation robotics. The passivity observer continuously monitors energy exchanges to classify patient states into passive (patient requiring robotic assistance) and non-passive (patient actively participating) categories, dynamically guiding the robot’s impedance adjustments. Experiments were conducted with ten unimpaired human subjects (eight male and two female), who were instructed to simulate rehabilitation scenarios, focusing on three key wrist movements, flexion/extension (FL/EX), abduction/adduction (AB/AD), and pronation/supination (PR/SU). Experimental results showed high correlations (> 0.83) between energy-based and electromyography (EMG)-based passivity classifications, confirming the reliability of the proposed approach. Furthermore, the designed QDRL model significantly outperformed traditional reinforcement learning methods, achieving superior adaptability, stability, and higher average rewards during robotic impedance control. The framework offers advancement in optimizing robotic assistance during motor recovery, promoting personalized rehabilitation by tailoring interventions to the specific needs of each patient.
{"title":"Quantum Driven Dynamic Passivity-Based Neuromechanical Control for Wrist Rehabilitation Robot","authors":"Naveed Ahmad Khan;Fahad Hussain;Tanishka Goyal;Prashant K. Jamwal;Shahid Hussain","doi":"10.1109/TMRB.2025.3562266","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3562266","url":null,"abstract":"Robotic-assisted rehabilitation for wrist movements demands adaptive systems capable of balancing patient autonomy with robotic support. The integration of artificial intelligence (AI) into robotic-assisted rehabilitation offers transformative potential in delivering personalized, dynamic, and effective therapeutic interventions. This study introduces a novel neuromechanical control framework integrating a passivity observer with Quantum-Enhanced Deep Reinforcement Learning (QDRL) for adaptive impedance scaling in wrist rehabilitation robotics. The passivity observer continuously monitors energy exchanges to classify patient states into passive (patient requiring robotic assistance) and non-passive (patient actively participating) categories, dynamically guiding the robot’s impedance adjustments. Experiments were conducted with ten unimpaired human subjects (eight male and two female), who were instructed to simulate rehabilitation scenarios, focusing on three key wrist movements, flexion/extension (FL/EX), abduction/adduction (AB/AD), and pronation/supination (PR/SU). Experimental results showed high correlations (> 0.83) between energy-based and electromyography (EMG)-based passivity classifications, confirming the reliability of the proposed approach. Furthermore, the designed QDRL model significantly outperformed traditional reinforcement learning methods, achieving superior adaptability, stability, and higher average rewards during robotic impedance control. The framework offers advancement in optimizing robotic assistance during motor recovery, promoting personalized rehabilitation by tailoring interventions to the specific needs of each patient.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"1237-1247"},"PeriodicalIF":3.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887772","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}
Pub Date : 2025-04-17DOI: 10.1109/TMRB.2025.3561865
Ziqian Li;Zhengyu Wang;Xinzhou Xu;Yongfa Chen;Björn W. Schuller
Accurate semantic segmentation for surgical instruments is crucial in robot-assisted minimally invasive surgery, mainly regarded as a core module in surgical-instrument tracking and operation guidance. Nevertheless, it is usually difficult for existing semantic surgical-instrument segmentation approaches to adapt to unknown surgical scenes, particularly due to their insufficient consideration for reducing the domain gaps across different scenes. To address this issue, we propose an unsupervised domain-adaptive semantic segmentation approach for surgical instruments, leveraging Dropout-enhanced Dual Heads and Coarse-Grained classification branch (D2HCG). The proposed approach comprises dropout-enhanced dual heads for diverse feature representation, and a coarse-grained classification branch for capturing complexities across varying granularities. This incorporates consistency loss functions targeting fine-grained features and coarse-grained granularities, aiming to reduce cross-scene domain gaps. Afterwards, we perform experiments in cross-scene surgical-instrument semantic segmentation cases, with the experimental results reporting the effectiveness for the proposed approach, compared with state-of-the-art semantic segmentation ones.
{"title":"Unsupervised Domain-Adaptive Semantic Segmentation for Surgical Instruments Leveraging Dropout-Enhanced Dual Heads and Coarse-Grained Classification Branch","authors":"Ziqian Li;Zhengyu Wang;Xinzhou Xu;Yongfa Chen;Björn W. Schuller","doi":"10.1109/TMRB.2025.3561865","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3561865","url":null,"abstract":"Accurate semantic segmentation for surgical instruments is crucial in robot-assisted minimally invasive surgery, mainly regarded as a core module in surgical-instrument tracking and operation guidance. Nevertheless, it is usually difficult for existing semantic surgical-instrument segmentation approaches to adapt to unknown surgical scenes, particularly due to their insufficient consideration for reducing the domain gaps across different scenes. To address this issue, we propose an unsupervised domain-adaptive semantic segmentation approach for surgical instruments, leveraging Dropout-enhanced Dual Heads and Coarse-Grained classification branch (D2HCG). The proposed approach comprises dropout-enhanced dual heads for diverse feature representation, and a coarse-grained classification branch for capturing complexities across varying granularities. This incorporates consistency loss functions targeting fine-grained features and coarse-grained granularities, aiming to reduce cross-scene domain gaps. Afterwards, we perform experiments in cross-scene surgical-instrument semantic segmentation cases, with the experimental results reporting the effectiveness for the proposed approach, compared with state-of-the-art semantic segmentation ones.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"950-961"},"PeriodicalIF":3.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887824","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}
The optimized hardware implementation of neurons and biological cells in the neuromorphic domain is of significant importance. In this paper, a novel method is presented that reduces any number of nonlinear terms in the differential equations describing the behavior of neurons or biological cells with a common variable to a single nonlinear term with high precision. This approach significantly improves implementation efficiency by reducing hardware resource consumption while maintaining high frequency and accuracy. The proposed method was applied to Cardiac Purkinje Fiber Cells, and its validity was demonstrated through time-domain analysis, noise condition analysis, Lyapunov stability analysis, and bifurcation analysis to validate the model under various conditions. These validations ensure the accuracy and stability of the proposed approach across different operating conditions. To assess large-scale applicability, the model was tested in a 300-cell Purkinje fiber network, demonstrating accurate synchronization, equilibrium states, and cross-spectral consistency while maintaining computational efficiency. The digital hardware implementation on a Virtex-7 FPGA board demonstrated a frequency improvement of 3.49 times compared to the original model and 1.79 times compared to the best implementation of this model to date. We also simulated a network of 4500 cells to analyze correlation and implemented it on hardware to demonstrate that the proposed model, based on the method presented in this paper, can efficiently and accurately scale to large-scale applications. This efficient and scalable approach paves the way for applications in medical research, bioengineering, and neuromorphic hardware development, including the creation of hardware-accelerated tools for simulating biological systems, designing bio-inspired devices, and enabling large-scale real-time simulations for understanding and treating cardiac or neurological conditions.
{"title":"FPGA-Optimized Neuromorphic Modeling of Cardiac Purkinje Fibers for Next-Generation Bionic Implants","authors":"Gilda Ghanbarpour;Muhammad Akmal Chaudhary;Maher Assaad;Milad Ghanbarpour","doi":"10.1109/TMRB.2025.3561836","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3561836","url":null,"abstract":"The optimized hardware implementation of neurons and biological cells in the neuromorphic domain is of significant importance. In this paper, a novel method is presented that reduces any number of nonlinear terms in the differential equations describing the behavior of neurons or biological cells with a common variable to a single nonlinear term with high precision. This approach significantly improves implementation efficiency by reducing hardware resource consumption while maintaining high frequency and accuracy. The proposed method was applied to Cardiac Purkinje Fiber Cells, and its validity was demonstrated through time-domain analysis, noise condition analysis, Lyapunov stability analysis, and bifurcation analysis to validate the model under various conditions. These validations ensure the accuracy and stability of the proposed approach across different operating conditions. To assess large-scale applicability, the model was tested in a 300-cell Purkinje fiber network, demonstrating accurate synchronization, equilibrium states, and cross-spectral consistency while maintaining computational efficiency. The digital hardware implementation on a Virtex-7 FPGA board demonstrated a frequency improvement of 3.49 times compared to the original model and 1.79 times compared to the best implementation of this model to date. We also simulated a network of 4500 cells to analyze correlation and implemented it on hardware to demonstrate that the proposed model, based on the method presented in this paper, can efficiently and accurately scale to large-scale applications. This efficient and scalable approach paves the way for applications in medical research, bioengineering, and neuromorphic hardware development, including the creation of hardware-accelerated tools for simulating biological systems, designing bio-inspired devices, and enabling large-scale real-time simulations for understanding and treating cardiac or neurological conditions.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"926-937"},"PeriodicalIF":3.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887778","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}
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