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}
Pub Date : 2025-04-15DOI: 10.1109/TMRB.2025.3560394
Daniel Rodríguez-Jorge;Sainan Zhang;Jin Sen Huang;Ivan Lopez-Sanchez;Nitin Srinivasan;Qiang Zhang;Xianlian Zhou;Hao Su
Exoskeletons can improve human mobility, but discomfort remains a significant barrier to their widespread adoption. This paper presents a comfort-centered mechatronics design of portable hip exoskeletons, comprising of three factors: (i) actuation, (ii) wearable interface, (iii) and assistive controller. We introduced an analytical multibody model to predict the human-exoskeleton contact forces during gait. Informed by this model, we designed a wearable interface that significantly improved the three considered objective metrics: (i) undesired contact forces at the wearable interface, (ii) wobbling, and (iii) metabolic reduction, and also the post-test evaluation via a System Usability Scale questionnaire as a subjective metric. Our experiments with two exoskeleton controllers (gait-based and reinforcement learning-based) demonstrated that the design of the wearable physical interface has a greater impact on reducing metabolic rate and minimizing wobbling than the choice of controller. Our actuation design method leads to highly backdrivable, lightweight quasi-direct drive actuators with high torque tracking performance. By leveraging this wearable design, we achieved up to 60% reduction in undesired contact forces, and a 74% reduction in exoskeleton wobbling in the frontal axis compared to a traditional configuration. Additionally, the net metabolic cost reduction was 18% compared to the no exoskeleton condition.
{"title":"Biomechanics-Informed Mechatronics Design of Comfort-Centered Portable Hip Exoskeleton: Actuator, Wearable Interface, Controller","authors":"Daniel Rodríguez-Jorge;Sainan Zhang;Jin Sen Huang;Ivan Lopez-Sanchez;Nitin Srinivasan;Qiang Zhang;Xianlian Zhou;Hao Su","doi":"10.1109/TMRB.2025.3560394","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3560394","url":null,"abstract":"Exoskeletons can improve human mobility, but discomfort remains a significant barrier to their widespread adoption. This paper presents a comfort-centered mechatronics design of portable hip exoskeletons, comprising of three factors: (i) actuation, (ii) wearable interface, (iii) and assistive controller. We introduced an analytical multibody model to predict the human-exoskeleton contact forces during gait. Informed by this model, we designed a wearable interface that significantly improved the three considered objective metrics: (i) undesired contact forces at the wearable interface, (ii) wobbling, and (iii) metabolic reduction, and also the post-test evaluation via a System Usability Scale questionnaire as a subjective metric. Our experiments with two exoskeleton controllers (gait-based and reinforcement learning-based) demonstrated that the design of the wearable physical interface has a greater impact on reducing metabolic rate and minimizing wobbling than the choice of controller. Our actuation design method leads to highly backdrivable, lightweight quasi-direct drive actuators with high torque tracking performance. By leveraging this wearable design, we achieved up to 60% reduction in undesired contact forces, and a 74% reduction in exoskeleton wobbling in the frontal axis compared to a traditional configuration. Additionally, the net metabolic cost reduction was 18% compared to the no exoskeleton condition.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"687-698"},"PeriodicalIF":3.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084773","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 : 2025-04-14DOI: 10.1109/TMRB.2025.3560390
V. Mainardi;M. Dal Canto;T. Melillo;N. Lorenzini;G. Bagnoni;S. Moccia;G. Ciuti
Skin cancer affects over 2 million people worldwide each year. Although dermoscopy is the gold standard screening technique, it only assesses the superficial features of skin lesions. Novel approaches based on thermal investigation have revealed a correlation between thermal recovery and vascular pattern alterations, which is an important factor in discriminating malignant and benign lesions. In this study, a dynamic thermal-imaging system was designed, developed, and validated in a real clinical scenario. The system is non-invasive, compact, and cost-effective, comprising a cooling probe and an image acquisition system equipped with RGB and thermal cameras. The system incorporates a machine-learning classification algorithm for skin cancer screening. The system showed an accuracy of 89.7% in distinguishing between malignant and benign lesions in a case study involving 58 patients and classified sub-classes of lesions (i.e., melanoma and nevi) with an accuracy of 95.5%. These findings underscore the potential benefit of the proposed dynamic thermal-imaging system as a support tool for non-invasive screening and early detection of malignant skin lesions.
{"title":"A Thermal-Imaging System and Machine-Learning Classification Algorithm for Skin Cancer Screening","authors":"V. Mainardi;M. Dal Canto;T. Melillo;N. Lorenzini;G. Bagnoni;S. Moccia;G. Ciuti","doi":"10.1109/TMRB.2025.3560390","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3560390","url":null,"abstract":"Skin cancer affects over 2 million people worldwide each year. Although dermoscopy is the gold standard screening technique, it only assesses the superficial features of skin lesions. Novel approaches based on thermal investigation have revealed a correlation between thermal recovery and vascular pattern alterations, which is an important factor in discriminating malignant and benign lesions. In this study, a dynamic thermal-imaging system was designed, developed, and validated in a real clinical scenario. The system is non-invasive, compact, and cost-effective, comprising a cooling probe and an image acquisition system equipped with RGB and thermal cameras. The system incorporates a machine-learning classification algorithm for skin cancer screening. The system showed an accuracy of 89.7% in distinguishing between malignant and benign lesions in a case study involving 58 patients and classified sub-classes of lesions (i.e., melanoma and nevi) with an accuracy of 95.5%. These findings underscore the potential benefit of the proposed dynamic thermal-imaging system as a support tool for non-invasive screening and early detection of malignant skin lesions.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"938-949"},"PeriodicalIF":3.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887665","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 advent of robotic surgery has brought about a paradigm shift in the medical field, necessitating the development of corresponding surgical skills training and assessment methods. These methods aim to enable surgeons to acquire the requisite skills for robotic surgery in the most efficient manner. Despite the progression from a master-apprentice system to manual objective assessment and then automated performance assessment methods, certain limitations have been observed. Our research aims to address these limitations by exploring muscle activity and state information during training via surface electromyography (sEMG) signals. This approach is intended to eventually provide interpretable information that can enhance the trainee’s understanding of assessment feedback and facilitate skill improvement. Building on our first study that validated the feasibility of motion primitive recognition based on sEMG signals, this work compares the performance of various machine learning (ML) methods for motion primitive recognition. It also investigates the effect of different parameters of the sliding window on recognition accuracy. Our findings indicate that the deep neural network (DNN) when paired with optimal sliding window parameters, can achieve the best average accuracy of 61.76% in this study. The discoveries also provide a reference of parameter settings for variable-length sliding window approach and ML methods in recognition of robotic surgery motion based on sEMG data. By demonstrating the feasibility and exploring the most effective analysis method, this work lays down the first stone to address the research topic of integrating muscle information into multimodal surgical skill training and assessment.
{"title":"sEMG-Based Motion Recognition for Robotic Surgery Training Using Machine Learning and Variable-Length Sliding Windows—A Preliminary Study","authors":"Chenji Li;Chao Liu;Arnaud Huaulmé;Nabil Zemiti;Pierre Jannin;Philippe Poignet","doi":"10.1109/TMRB.2025.3560389","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3560389","url":null,"abstract":"The advent of robotic surgery has brought about a paradigm shift in the medical field, necessitating the development of corresponding surgical skills training and assessment methods. These methods aim to enable surgeons to acquire the requisite skills for robotic surgery in the most efficient manner. Despite the progression from a master-apprentice system to manual objective assessment and then automated performance assessment methods, certain limitations have been observed. Our research aims to address these limitations by exploring muscle activity and state information during training via surface electromyography (sEMG) signals. This approach is intended to eventually provide interpretable information that can enhance the trainee’s understanding of assessment feedback and facilitate skill improvement. Building on our first study that validated the feasibility of motion primitive recognition based on sEMG signals, this work compares the performance of various machine learning (ML) methods for motion primitive recognition. It also investigates the effect of different parameters of the sliding window on recognition accuracy. Our findings indicate that the deep neural network (DNN) when paired with optimal sliding window parameters, can achieve the best average accuracy of 61.76% in this study. The discoveries also provide a reference of parameter settings for variable-length sliding window approach and ML methods in recognition of robotic surgery motion based on sEMG data. By demonstrating the feasibility and exploring the most effective analysis method, this work lays down the first stone to address the research topic of integrating muscle information into multimodal surgical skill training and assessment.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"572-582"},"PeriodicalIF":3.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084780","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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