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}
Pub Date : 2025-04-14DOI: 10.1109/TMRB.2025.3560331
Reese R. Peterson;Jennifer K. Leestma;Inseung Kang;Aaron J. Young
Fall incidents due to slips are some of the most common causes of injuries for industry workers and older adults, motivating research to assist balance recovery following slips. To assist balance recovery during a slip, a detection algorithm that can work with an assistive device, such as an exoskeleton, needs to be able to detect slips rapidly after onset, which remains a critical gap in the field. Here, we compared the ability of linear discriminant analysis (LDA), extreme gradient boosting (XGBoost), and convolutional neural networks (CNN) to detect slip using only native sensors on a hip exoskeleton. We trained and evaluated user-independent models on early-stance (ES) and late-stance (LS) slips of various magnitudes collected through treadmill-based slips. All models, except LDA with LS slips, detected slips with ¿90% accuracy. Overall, the best model was XGBoost, with its fastest results achieving average detection times and median accuracies of 155.06 ms at 96.25% for ES slips and 228.88 ms at 93.75% for LS slips, while also achieving 100% sensitivity at 195.64 ms (ES) and 266.24 ms (LS). Our results indicate a promising direction for further research into designing a generalizable model for balance recovery during slip perturbations using robotic hip exoskeletons.
由于滑倒导致的跌倒事故是工业工人和老年人受伤的最常见原因之一，这促使研究人员在滑倒后帮助平衡恢复。为了在打滑过程中帮助平衡恢复，一种能够与辅助设备（如外骨骼）一起工作的检测算法需要能够在打滑发生后快速检测到打滑，这在该领域仍然是一个关键的空白。在这里，我们比较了线性判别分析（LDA）、极端梯度增强（XGBoost）和卷积神经网络（CNN）仅使用髋关节外骨骼上的本机传感器检测滑移的能力。我们训练并评估了通过跑步机收集的不同震级的早站（ES）和晚站（LS）滑动的用户独立模型。除LS滑动的LDA外，所有模型检测滑动的准确率均为90%。总的来说，最好的模型是XGBoost，其最快的结果实现了平均检测时间和中位数精度155.06 ms（96.25%的ES滑动）和228.88 ms（93.75%的LS滑动），同时也实现了100%的灵敏度195.64 ms （ES）和266.24 ms （LS）。我们的研究结果表明了一个有希望的方向，为进一步研究设计一个可推广的模型，用于利用机器人髋关节外骨骼在滑移扰动下的平衡恢复。
{"title":"Machine Learning Enables Rapid Detection of Slips Using a Robotic Hip Exoskeleton","authors":"Reese R. Peterson;Jennifer K. Leestma;Inseung Kang;Aaron J. Young","doi":"10.1109/TMRB.2025.3560331","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3560331","url":null,"abstract":"Fall incidents due to slips are some of the most common causes of injuries for industry workers and older adults, motivating research to assist balance recovery following slips. To assist balance recovery during a slip, a detection algorithm that can work with an assistive device, such as an exoskeleton, needs to be able to detect slips rapidly after onset, which remains a critical gap in the field. Here, we compared the ability of linear discriminant analysis (LDA), extreme gradient boosting (XGBoost), and convolutional neural networks (CNN) to detect slip using only native sensors on a hip exoskeleton. We trained and evaluated user-independent models on early-stance (ES) and late-stance (LS) slips of various magnitudes collected through treadmill-based slips. All models, except LDA with LS slips, detected slips with ¿90% accuracy. Overall, the best model was XGBoost, with its fastest results achieving average detection times and median accuracies of 155.06 ms at 96.25% for ES slips and 228.88 ms at 93.75% for LS slips, while also achieving 100% sensitivity at 195.64 ms (ES) and 266.24 ms (LS). Our results indicate a promising direction for further research into designing a generalizable model for balance recovery during slip perturbations using robotic hip exoskeletons.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"666-677"},"PeriodicalIF":3.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084779","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.2025.3560399
Jiaqi Chen;Guochen Ning;Longfei Ma;Hongen Liao
Deformable tissue retraction is a common but time-consuming task in robotic surgery. An autonomous robotic deformable tissue retraction system has the potential to help surgeons reduce cognitive burdens and focus more on critical aspects of the surgery. However, the uncertain deformation and complex constraints of deformable tissues pose significant challenges. We propose an autonomous deformable tissue retraction framework that incorporates visual representation and learning models, along with a 7-degree-of-freedom robotic system. For extracting deformation representations and learning to manipulate deformable tissues based on 2D images, we introduce a Sequential-information-based Contrastive State Representation Learning (SC-SRL) algorithm and a reinforcement learning model with asymmetric inputs and auxiliary losses. Experimental results show that the proposed framework achieved a 93.0% success rate of tissue retraction task in a simulated environment. Furthermore, our method demonstrates a safe retraction trajectory proportion of 92.5% based on a novel evaluation method using the histogram of feature angles of the tissue particles. The proposed framework can also be deployed on a real robotic system through a sim-to-real transfer pipeline, acquire policies for nearby tasks and perform resistance to visual dynamic disturbance. This study paves a new path for the application of vision-based intelligent systems in surgical robotics.
{"title":"Autonomous Deformable Tissue Retraction System Based on 2-D Visual Representation and Asymmetric Reinforcement Learning for Robotic Surgery","authors":"Jiaqi Chen;Guochen Ning;Longfei Ma;Hongen Liao","doi":"10.1109/TMRB.2025.3560399","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3560399","url":null,"abstract":"Deformable tissue retraction is a common but time-consuming task in robotic surgery. An autonomous robotic deformable tissue retraction system has the potential to help surgeons reduce cognitive burdens and focus more on critical aspects of the surgery. However, the uncertain deformation and complex constraints of deformable tissues pose significant challenges. We propose an autonomous deformable tissue retraction framework that incorporates visual representation and learning models, along with a 7-degree-of-freedom robotic system. For extracting deformation representations and learning to manipulate deformable tissues based on 2D images, we introduce a Sequential-information-based Contrastive State Representation Learning (SC-SRL) algorithm and a reinforcement learning model with asymmetric inputs and auxiliary losses. Experimental results show that the proposed framework achieved a 93.0% success rate of tissue retraction task in a simulated environment. Furthermore, our method demonstrates a safe retraction trajectory proportion of 92.5% based on a novel evaluation method using the histogram of feature angles of the tissue particles. The proposed framework can also be deployed on a real robotic system through a sim-to-real transfer pipeline, acquire policies for nearby tasks and perform resistance to visual dynamic disturbance. This study paves a new path for the application of vision-based intelligent systems in surgical robotics.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"595-606"},"PeriodicalIF":3.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084776","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.2025.3560383
Hamid Ladjal;Michael Beuve;Behzad Shariat
Respiratory-induced organ motion is a technical challenge to radiation therapy for lung cancer. Breathing is controlled by two independent muscles: the thorax and diaphragm muscles. The modeling of their action constitutes an important step for the respiratory motion model. The amplitude of the diaphragm forces and ribs displacement are patient-specific and depends on geometrical and physiological characteristics of the patient. This article presents a patient-specific bio-mechanical model (PSBM) of the diaphragm, as well as ribs kinematics. To determine the appropriate values of specific diaphragm forces for each patient, during a whole respiratory cycle, inverse finite element (FE) analysis methodology has been implemented to match the experimental results to the FE simulation results. Ribs kinematics extracted and calculated directly from 4D Computed Tomography (CT) scan images. We have investigated the effect of element type, finite deformation and elasticity on the accuracy and computation time. The results demonstrate that the proposed FE model including ribs kinematics can accurately predict the diaphragm motion with an average surface error in diaphragm/lungs contact region less than $2.2pm 2.1mm$ . This constitutes first steps for biomechanical patient-specific of the respiratory system modeling to pilot lungs and lung tumor motion for External Beam Radiation Therapy (EBRT).
{"title":"Patient-Specific Biomechanical Diaphragm-Ribs Respiratory Motion Model for Radiation Therapy","authors":"Hamid Ladjal;Michael Beuve;Behzad Shariat","doi":"10.1109/TMRB.2025.3560383","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3560383","url":null,"abstract":"Respiratory-induced organ motion is a technical challenge to radiation therapy for lung cancer. Breathing is controlled by two independent muscles: the thorax and diaphragm muscles. The modeling of their action constitutes an important step for the respiratory motion model. The amplitude of the diaphragm forces and ribs displacement are patient-specific and depends on geometrical and physiological characteristics of the patient. This article presents a patient-specific bio-mechanical model (PSBM) of the diaphragm, as well as ribs kinematics. To determine the appropriate values of specific diaphragm forces for each patient, during a whole respiratory cycle, inverse finite element (FE) analysis methodology has been implemented to match the experimental results to the FE simulation results. Ribs kinematics extracted and calculated directly from 4D Computed Tomography (CT) scan images. We have investigated the effect of element type, finite deformation and elasticity on the accuracy and computation time. The results demonstrate that the proposed FE model including ribs kinematics can accurately predict the diaphragm motion with an average surface error in diaphragm/lungs contact region less than <inline-formula> <tex-math>$2.2pm 2.1mm$ </tex-math></inline-formula>. This constitutes first steps for biomechanical patient-specific of the respiratory system modeling to pilot lungs and lung tumor motion for External Beam Radiation Therapy (EBRT).","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"802-813"},"PeriodicalIF":3.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949203","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.2025.3560385
Mahdi Nabipour;Gregory S. Sawicki;Massimo Sartori
Recent advancements in wearable exoskeletons for human lower extremities have primarily focused on augmenting walking capacity by either reducing metabolic costs or providing joint torque support based on measured electromyography or predicted joint torques. However, less attention has been given to the use of robotic exoskeletons for controlling the mechanics of specific biological tissues, such as elastic tendons. Achieving closed-loop control over in-vivo musculotendon mechanics during movement could revolutionize injury prevention and personalized rehabilitation. Here, we introduce a framework utilizing musculoskeletal modeling and nonlinear model predictive control (NMPC) to close the loop around tendon force in a simulation of cyclic force production of the human ankle plantarflexors in parallel with a powered exoskeleton. The proposed framework integrates a computationally efficient model comprising explicit closed-form ordinary differential equations governing musculotendon and ankle joint with parallel actuation dynamics. The model’s computational time, in the microsecond range, allows prediction of future states in real-time closed-loop control. Compared to a predictive proportional-derivative controller, the NMPC-based framework more effectively maintained Achilles tendon force within a predetermined threshold across varying levels of muscle excitation amplitude and frequency. Remarkably, the NMPC framework demonstrates robustness to muscle excitation variations during cyclic motions, making it suitable for real-world applications.
{"title":"Predictive Control of Achilles Tendon Force During Cyclic Motions in a Simulated Musculoskeletal System With Parallel Actuation","authors":"Mahdi Nabipour;Gregory S. Sawicki;Massimo Sartori","doi":"10.1109/TMRB.2025.3560385","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3560385","url":null,"abstract":"Recent advancements in wearable exoskeletons for human lower extremities have primarily focused on augmenting walking capacity by either reducing metabolic costs or providing joint torque support based on measured electromyography or predicted joint torques. However, less attention has been given to the use of robotic exoskeletons for controlling the mechanics of specific biological tissues, such as elastic tendons. Achieving closed-loop control over in-vivo musculotendon mechanics during movement could revolutionize injury prevention and personalized rehabilitation. Here, we introduce a framework utilizing musculoskeletal modeling and nonlinear model predictive control (NMPC) to close the loop around tendon force in a simulation of cyclic force production of the human ankle plantarflexors in parallel with a powered exoskeleton. The proposed framework integrates a computationally efficient model comprising explicit closed-form ordinary differential equations governing musculotendon and ankle joint with parallel actuation dynamics. The model’s computational time, in the microsecond range, allows prediction of future states in real-time closed-loop control. Compared to a predictive proportional-derivative controller, the NMPC-based framework more effectively maintained Achilles tendon force within a predetermined threshold across varying levels of muscle excitation amplitude and frequency. Remarkably, the NMPC framework demonstrates robustness to muscle excitation variations during cyclic motions, making it suitable for real-world applications.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"814-825"},"PeriodicalIF":3.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949215","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.2025.3560400
A. Meza-Pantoja;A. C. Lawson;C. C. Caputo;J. Ge;D. J. Cohen;A. Krieger;H. Saeidi
Robotic-assisted surgery (RAS) systems take advantage of dexterous tools, enhanced vision, and motion filtering to improve patient outcomes. Whereas most RAS systems are directly controlled by surgeons, the development and application of autonomous RAS are growing owing to their repeatability and precision. Although full autonomy is a long-term goal, human intervention in RAS is still essential. In this work, we develop and test a shared control strategy for robotic electrosurgery in which autonomous robot controllers and human operators collaborate. We designed and implemented identification tests that assessed the effectiveness of autonomous and manual control strategies and the cost of switching between the control modes. Based on the results, we propose a control mode switching strategy and examine it via an experiment on precision cutting on porcine tongue samples. The results indicate that by combining the best elements of autonomous and manual control, we can achieve more accurate soft-tissue incisions as compared to single-mode control strategies. Furthermore, the proposed strategy reduces the required human-in-the-loop time by 69.29%.
{"title":"A Confidence-Based Shared Control Strategy for Robotic Electrosurgery","authors":"A. Meza-Pantoja;A. C. Lawson;C. C. Caputo;J. Ge;D. J. Cohen;A. Krieger;H. Saeidi","doi":"10.1109/TMRB.2025.3560400","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3560400","url":null,"abstract":"Robotic-assisted surgery (RAS) systems take advantage of dexterous tools, enhanced vision, and motion filtering to improve patient outcomes. Whereas most RAS systems are directly controlled by surgeons, the development and application of autonomous RAS are growing owing to their repeatability and precision. Although full autonomy is a long-term goal, human intervention in RAS is still essential. In this work, we develop and test a shared control strategy for robotic electrosurgery in which autonomous robot controllers and human operators collaborate. We designed and implemented identification tests that assessed the effectiveness of autonomous and manual control strategies and the cost of switching between the control modes. Based on the results, we propose a control mode switching strategy and examine it via an experiment on precision cutting on porcine tongue samples. The results indicate that by combining the best elements of autonomous and manual control, we can achieve more accurate soft-tissue incisions as compared to single-mode control strategies. Furthermore, the proposed strategy reduces the required human-in-the-loop time by 69.29%.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"583-594"},"PeriodicalIF":3.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084794","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 : 2025-04-02DOI: 10.1109/TMRB.2025.3557197
Chengyu Lin;Kong Hoi Cheng;Wei Pan;Jinxin Sun;Guotao Gou;Junyun Fu;Yuquan Leng;Chenglong Fu
Grasping is a critical motor skill essential for daily activities, but it is often compromised in individuals with neural impairments. Functional Electrical Stimulation (FES) has emerged as a promising intervention, utilizing electrical pulses to stimulate muscles and thereby restore impaired motor functions. However, existing closed-loop FES systems depend on pre-calibrated angles or forces specific to individual objects, which limits their practicality in dynamic, real-world environments with varying object properties.This paper presents a novel closed-loop FES (CLFES) system with visual feedback, designed to dynamically adjust stimulation parameters based on real-time interaction states without requiring object-specific calibration. The system employs a finite state machine to manage sequential grasp-release tasks and integrates a visual perception module for slip detection and intent recognition. The system was tested with two individuals with disabilities on five common household objects. Experimental results demonstrate significant improvements, including a 42.6% increase in success rate and a 45.9% reduction in task completion time compared to tasks performed without the system. These results underscore the system’s potential to improve daily task performance for individuals with neural impairments.
{"title":"Adaptive Closed-Loop Functional Electrical Stimulation System With Visual Feedback for Enhanced Grasping in Neurological Impairments","authors":"Chengyu Lin;Kong Hoi Cheng;Wei Pan;Jinxin Sun;Guotao Gou;Junyun Fu;Yuquan Leng;Chenglong Fu","doi":"10.1109/TMRB.2025.3557197","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3557197","url":null,"abstract":"Grasping is a critical motor skill essential for daily activities, but it is often compromised in individuals with neural impairments. Functional Electrical Stimulation (FES) has emerged as a promising intervention, utilizing electrical pulses to stimulate muscles and thereby restore impaired motor functions. However, existing closed-loop FES systems depend on pre-calibrated angles or forces specific to individual objects, which limits their practicality in dynamic, real-world environments with varying object properties.This paper presents a novel closed-loop FES (CLFES) system with visual feedback, designed to dynamically adjust stimulation parameters based on real-time interaction states without requiring object-specific calibration. The system employs a finite state machine to manage sequential grasp-release tasks and integrates a visual perception module for slip detection and intent recognition. The system was tested with two individuals with disabilities on five common household objects. Experimental results demonstrate significant improvements, including a 42.6% increase in success rate and a 45.9% reduction in task completion time compared to tasks performed without the system. These results underscore the system’s potential to improve daily task performance for individuals with neural impairments.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"678-686"},"PeriodicalIF":3.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084805","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 : 2025-04-01DOI: 10.1109/TMRB.2025.3556556
Rachael L’Orsa;Kourosh Zareinia;Garnette R. Sutherland;David Westwick;Katherine J. Kuchenbecker
Tube thoracostomy (chest tube insertion) is a surgical procedure that treats pneumothorax, a potentially life-threatening condition where air accumulates between the chest wall and the lungs. The literature reports high complication rates for this procedure, including accidental fatality due to poor manual depth control during tool insertion. We hypothesize that an instrumented needle-holder could help operators recognize pleural puncture and improve depth control, and we present a puncture-detection experiment that contributes toward this goal. An operator manually inserted a bevel-tip needle into ex vivo porcine ribs and through the parietal pleura via a sensorized percutaneous device that records position, force, and videos. We use this rich dataset of 63 insertions to thoroughly test four previously published data-driven puncture-detection (DDPD) algorithms against two new real-time algorithms: a custom recursive digital filter with coefficients optimized for our application, and a difference equation that compares standard deviations between adjacent sliding windows. Our algorithms achieve a precision (true positives over total identified punctures) of 23% and 22%, respectively, while the precision of existing DDPD algorithms ranges from 0% to 21%. Despite these performance improvements, our results show the limitations of DDPD algorithms and motivate new methods for detecting pleural membrane punctures in thoracostomy.
{"title":"Comparing Puncture-Detection Approaches for Manual Needle Insertions Through the Parietal Pleura","authors":"Rachael L’Orsa;Kourosh Zareinia;Garnette R. Sutherland;David Westwick;Katherine J. Kuchenbecker","doi":"10.1109/TMRB.2025.3556556","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3556556","url":null,"abstract":"Tube thoracostomy (chest tube insertion) is a surgical procedure that treats pneumothorax, a potentially life-threatening condition where air accumulates between the chest wall and the lungs. The literature reports high complication rates for this procedure, including accidental fatality due to poor manual depth control during tool insertion. We hypothesize that an instrumented needle-holder could help operators recognize pleural puncture and improve depth control, and we present a puncture-detection experiment that contributes toward this goal. An operator manually inserted a bevel-tip needle into ex vivo porcine ribs and through the parietal pleura via a sensorized percutaneous device that records position, force, and videos. We use this rich dataset of 63 insertions to thoroughly test four previously published data-driven puncture-detection (DDPD) algorithms against two new real-time algorithms: a custom recursive digital filter with coefficients optimized for our application, and a difference equation that compares standard deviations between adjacent sliding windows. Our algorithms achieve a precision (true positives over total identified punctures) of 23% and 22%, respectively, while the precision of existing DDPD algorithms ranges from 0% to 21%. Despite these performance improvements, our results show the limitations of DDPD algorithms and motivate new methods for detecting pleural membrane punctures in thoracostomy.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"455-468"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084739","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 : 2025-04-01DOI: 10.1109/TMRB.2025.3556539
Mingrui Hao;Pengcheng Zhang;Xilong Hou;Xiaolin Gu;Xiao-Hu Zhou;Zeng-Guang Hou;Chen Chen;Shuangyi Wang
Echocardiography serves as a prevalent modality for both heart disease diagnosis and procedural guidance in medical applications. Nevertheless, the conventional echocardiography examination heavily relies on the manual dexterity of the sonographer, leading to the suboptimal repeatability. Despite the extensive exploration of robot-assisted ultrasound systems, achieving a heightened level of automation in examinations and enhancing the practicality of these robotic platforms for primary utilization remain formidable challenges within the field. In this study, we introduce an innovative automatic acquisition method for cardiac views using a novel ultrasound robot. The method is designed to autonomously traverse and scan target positions and angular ranges to search and identify the target cardiac views. First, the target positions and angular ranges were derived from a professional sonographer’s practice on 14 cases. Then, an automatic traversal scanning method is designed integrating visual guidance, human-machine collaboration, and path planning within the framework of a novel parallel mechanism-based ultrasound robot. Finally, we explore deep metric learning to search for target ultrasound images in the traversed ultrasound video. Experiments on the test set to evaluate the target ultrasound view searching algorithm achieved a mAP of 98.8% and a Rank-1 accuracy of 98.23%. Our method has been successfully validated by data from five subjects, achieving the acquisition of standard parasternal long-axis and short-axis cardiac views essential for diagnosis, demonstrating the effectiveness of the proposed method.
{"title":"Towards Autonomous Cardiac Ultrasound Scanning: Combining Physician Expertise and Machine Intelligence","authors":"Mingrui Hao;Pengcheng Zhang;Xilong Hou;Xiaolin Gu;Xiao-Hu Zhou;Zeng-Guang Hou;Chen Chen;Shuangyi Wang","doi":"10.1109/TMRB.2025.3556539","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3556539","url":null,"abstract":"Echocardiography serves as a prevalent modality for both heart disease diagnosis and procedural guidance in medical applications. Nevertheless, the conventional echocardiography examination heavily relies on the manual dexterity of the sonographer, leading to the suboptimal repeatability. Despite the extensive exploration of robot-assisted ultrasound systems, achieving a heightened level of automation in examinations and enhancing the practicality of these robotic platforms for primary utilization remain formidable challenges within the field. In this study, we introduce an innovative automatic acquisition method for cardiac views using a novel ultrasound robot. The method is designed to autonomously traverse and scan target positions and angular ranges to search and identify the target cardiac views. First, the target positions and angular ranges were derived from a professional sonographer’s practice on 14 cases. Then, an automatic traversal scanning method is designed integrating visual guidance, human-machine collaboration, and path planning within the framework of a novel parallel mechanism-based ultrasound robot. Finally, we explore deep metric learning to search for target ultrasound images in the traversed ultrasound video. Experiments on the test set to evaluate the target ultrasound view searching algorithm achieved a mAP of 98.8% and a Rank-1 accuracy of 98.23%. Our method has been successfully validated by data from five subjects, achieving the acquisition of standard parasternal long-axis and short-axis cardiac views essential for diagnosis, demonstrating the effectiveness of the proposed method.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"782-792"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949178","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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