Pub Date : 2025-03-12DOI: 10.1109/TMRB.2025.3550655
Andrea Berettoni;Josephus J. M. Driessen;Marco Puliti;Giacinto Barresi;Carlo De Benedictis;Carlo Ferraresi;Matteo Laffranchi
For many decades, developments of knee prostheses have shown a dichotomy regarding fundamental working principles. The industry has mainly emphasized on quasi-passive hydraulic solutions, whereas most research works have focused on powered devices, employing electric actuation. The former have an energetically passive effect at the knee joint, for which they often lack in providing versatility and movement robustness for the wearer. Powered prostheses can address these deficiencies, but are often rejected as they struggle to fulfill other user needs (e.g., weight and acoustic noise). Correspondingly, recent studies have emerged that attempt to significantly attenuate the deficiencies of fully powered prosthesis knees, partially sacrificing on device versatility. Recognizing the state-of-the art difficulties in balancing active assistance and user needs fulfilment, this work analyses human-centered design perspectives and their prospects for prosthetic development, in light of the often diverging user needs. We conclude that various types of both explored and yet unexplored semi-powered solutions may have the potential to provide the better trade-off between quasi-passive and fully powered prosthetic devices.
{"title":"Human-Centered Design Trade-Offs for Semi-Powered Knee Prostheses: A Review","authors":"Andrea Berettoni;Josephus J. M. Driessen;Marco Puliti;Giacinto Barresi;Carlo De Benedictis;Carlo Ferraresi;Matteo Laffranchi","doi":"10.1109/TMRB.2025.3550655","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550655","url":null,"abstract":"For many decades, developments of knee prostheses have shown a dichotomy regarding fundamental working principles. The industry has mainly emphasized on quasi-passive hydraulic solutions, whereas most research works have focused on powered devices, employing electric actuation. The former have an energetically passive effect at the knee joint, for which they often lack in providing versatility and movement robustness for the wearer. Powered prostheses can address these deficiencies, but are often rejected as they struggle to fulfill other user needs (e.g., weight and acoustic noise). Correspondingly, recent studies have emerged that attempt to significantly attenuate the deficiencies of fully powered prosthesis knees, partially sacrificing on device versatility. Recognizing the state-of-the art difficulties in balancing active assistance and user needs fulfilment, this work analyses human-centered design perspectives and their prospects for prosthetic development, in light of the often diverging user needs. We conclude that various types of both explored and yet unexplored semi-powered solutions may have the potential to provide the better trade-off between quasi-passive and fully powered prosthetic devices.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"429-442"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084799","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 : 2025-03-12DOI: 10.1109/TMRB.2025.3550643
Zhi Li;Dunfa Long;Feiyang Chen;Kaifeng Wang;Chaoyang Shi
This paper presents a leader-follower robotic system featuring a novel 4-degree-of-freedom (4-DOF) Remote Center of Motion (RCM) mechanism, tailored to address the limitations associated with traditional posterior segment ocular microsurgery. The proposed 4-DOF mechanism employs parallelogram motion replication to relocate the bulky instrument insertion drive from the end-effector to the proximal linkage on the base, minimizing obstruction to the microscope’s field of view and the surgical environment. The mechanism’s orthogonal or coincident arranged degrees of freedom, paired with independent drive units, facilitate calibration and enhance control accuracy. A minimized internal constraint force optimization method was proposed to improve RCM point stability and tip positioning accuracy. The mechanism’s configuration and parameters were optimized through an analytical mechanics model, effectively reducing the constraint force on the internal components under the same external loads, thereby minimizing deformation and maintaining accuracy. To enhance robot compatibility, modular surgical instruments with a quick-change coupler were developed based on the analysis of traditional instruments’ characteristics. The prototype was built and kinematically calibrated to reduce manufacturing and assembly errors and thus improve accuracy. Following kinematic calibration to minimize manufacturing and assembly errors, experimental validation revealed positioning accuracies of $49~pm ~23~mu $ m and $22~pm ~13~mu $ m, repeatabilities of $25~pm ~10~mu $ m and $9~pm ~4~mu $ m, and RCM deviations of $13~pm ~10~mu $ m and $18~pm ~11~mu $ m on the X-Z and Y-Z planes, respectively. The cannulation experiment further demonstrates the prototype’s potential for robot-assisted vitreoretinal microsurgery.
{"title":"A Posterior Segment Ocular Microsurgical Robot With a Decoupling RCM Mechanism Based on a Minimized Internal Constraint Force Optimization Method","authors":"Zhi Li;Dunfa Long;Feiyang Chen;Kaifeng Wang;Chaoyang Shi","doi":"10.1109/TMRB.2025.3550643","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550643","url":null,"abstract":"This paper presents a leader-follower robotic system featuring a novel 4-degree-of-freedom (4-DOF) Remote Center of Motion (RCM) mechanism, tailored to address the limitations associated with traditional posterior segment ocular microsurgery. The proposed 4-DOF mechanism employs parallelogram motion replication to relocate the bulky instrument insertion drive from the end-effector to the proximal linkage on the base, minimizing obstruction to the microscope’s field of view and the surgical environment. The mechanism’s orthogonal or coincident arranged degrees of freedom, paired with independent drive units, facilitate calibration and enhance control accuracy. A minimized internal constraint force optimization method was proposed to improve RCM point stability and tip positioning accuracy. The mechanism’s configuration and parameters were optimized through an analytical mechanics model, effectively reducing the constraint force on the internal components under the same external loads, thereby minimizing deformation and maintaining accuracy. To enhance robot compatibility, modular surgical instruments with a quick-change coupler were developed based on the analysis of traditional instruments’ characteristics. The prototype was built and kinematically calibrated to reduce manufacturing and assembly errors and thus improve accuracy. Following kinematic calibration to minimize manufacturing and assembly errors, experimental validation revealed positioning accuracies of <inline-formula> <tex-math>$49~pm ~23~mu $ </tex-math></inline-formula>m and <inline-formula> <tex-math>$22~pm ~13~mu $ </tex-math></inline-formula>m, repeatabilities of <inline-formula> <tex-math>$25~pm ~10~mu $ </tex-math></inline-formula>m and <inline-formula> <tex-math>$9~pm ~4~mu $ </tex-math></inline-formula>m, and RCM deviations of <inline-formula> <tex-math>$13~pm ~10~mu $ </tex-math></inline-formula>m and <inline-formula> <tex-math>$18~pm ~11~mu $ </tex-math></inline-formula>m on the X-Z and Y-Z planes, respectively. The cannulation experiment further demonstrates the prototype’s potential for robot-assisted vitreoretinal microsurgery.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"528-541"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084731","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 : 2025-03-12DOI: 10.1109/TMRB.2025.3550673
Xiangyu Wang;Chong Liu;Yongchun Fang;Ningbo Yu;Yanding Qin;Hongpeng Wang;Jianda Han
In natural orifice transluminal endoscopic surgery (NOTES), the twist steering of the flexible endoscope plays an important role in tracking the preoperative path during cavity intervention. However, the flexible endoscope’s twisting motion has high nonlinearity and uncertainty, which bring challenges for accurate modeling and controller design. In this study, a novel active modeling-based improved control (AMIC) scheme is proposed, which achieves precise control of the robotic flexible endoscope’s twisting motion. First, the Coleman-Hodgdon (C-H) model is modified to serve as the reference model to describe the twist steering. Then, the model error in the C-H model is introduced as an extended state. Upon this, an active modeling algorithm is developed by using the unscented Kalman filter. The proposed model estimates both the twisting angle and the model error in real time. Based on the proposed model, the AMIC strategy is developed to enhance the tracking performance of a proportional-integral-derivative (PID) controller for a reference trajectory. Finally, comparative experiments were conducted on a self-built robotic flexible endoscope under various insertion depths and tip-part configurations. Compared to the PID controller, the experimental results demonstrate that the proposed AMIC scheme achieves a 63.1% reduction in tracking error under a sinusoidal trajectory.
{"title":"Active-Model-Based Precise Twist Steering for Autonomous Robotic Flexible Endoscope","authors":"Xiangyu Wang;Chong Liu;Yongchun Fang;Ningbo Yu;Yanding Qin;Hongpeng Wang;Jianda Han","doi":"10.1109/TMRB.2025.3550673","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550673","url":null,"abstract":"In natural orifice transluminal endoscopic surgery (NOTES), the twist steering of the flexible endoscope plays an important role in tracking the preoperative path during cavity intervention. However, the flexible endoscope’s twisting motion has high nonlinearity and uncertainty, which bring challenges for accurate modeling and controller design. In this study, a novel active modeling-based improved control (AMIC) scheme is proposed, which achieves precise control of the robotic flexible endoscope’s twisting motion. First, the Coleman-Hodgdon (C-H) model is modified to serve as the reference model to describe the twist steering. Then, the model error in the C-H model is introduced as an extended state. Upon this, an active modeling algorithm is developed by using the unscented Kalman filter. The proposed model estimates both the twisting angle and the model error in real time. Based on the proposed model, the AMIC strategy is developed to enhance the tracking performance of a proportional-integral-derivative (PID) controller for a reference trajectory. Finally, comparative experiments were conducted on a self-built robotic flexible endoscope under various insertion depths and tip-part configurations. Compared to the PID controller, the experimental results demonstrate that the proposed AMIC scheme achieves a 63.1% reduction in tracking error under a sinusoidal trajectory.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"481-491"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084774","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 : 2025-03-12DOI: 10.1109/TMRB.2025.3550667
Peter Q. Lee;John S. Zelek;Katja Mombaur
The nasopharyngeal (NP) swab test is a method for collecting cultures to diagnose for different types of respiratory illnesses, including COVID-19. Delegating this task to robots would be beneficial in terms of reducing infection risks and bolstering the healthcare system, but a critical component of the NP swab test is having the swab aligned properly with the nasal cavity so that it does not cause excessive discomfort or injury by traveling down the wrong passage. Existing research towards robotic NP swabbing typically assumes the patient’s head is held within a fixture. This simplifies the alignment problem, but is also dissimilar to clinical scenarios where patients are typically free-standing. Consequently, our work creates a vision-guided pipeline to allow an instrumented robot arm to properly position and orient NP swabs with respect to the nostrils of free-standing patients. The first component of the pipeline is a precomputed joint lookup table to allow the arm to meet the patient’s arbitrary position in the designated workspace, while avoiding joint limits. Our pipeline leverages semantic face models from computer vision to estimate the Euclidean pose of the face with respect to a monocular RGB-D camera placed on the end-effector. These estimates are passed into an unscented Kalman filter on manifolds state estimator and a pose based visual servo control loop to move the swab to the designated pose in front of the nostril. Our pipeline was validated with human trials, featuring a cohort of 25 participants. The system is effective, reaching the nostril for 84% of participants, and our statistical analysis did not find significant demographic biases within the cohort.
{"title":"Robotic Eye-in-Hand Visual Servo Axially Aligning Nasopharyngeal Swabs With the Nasal Cavity","authors":"Peter Q. Lee;John S. Zelek;Katja Mombaur","doi":"10.1109/TMRB.2025.3550667","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550667","url":null,"abstract":"The nasopharyngeal (NP) swab test is a method for collecting cultures to diagnose for different types of respiratory illnesses, including COVID-19. Delegating this task to robots would be beneficial in terms of reducing infection risks and bolstering the healthcare system, but a critical component of the NP swab test is having the swab aligned properly with the nasal cavity so that it does not cause excessive discomfort or injury by traveling down the wrong passage. Existing research towards robotic NP swabbing typically assumes the patient’s head is held within a fixture. This simplifies the alignment problem, but is also dissimilar to clinical scenarios where patients are typically free-standing. Consequently, our work creates a vision-guided pipeline to allow an instrumented robot arm to properly position and orient NP swabs with respect to the nostrils of free-standing patients. The first component of the pipeline is a precomputed joint lookup table to allow the arm to meet the patient’s arbitrary position in the designated workspace, while avoiding joint limits. Our pipeline leverages semantic face models from computer vision to estimate the Euclidean pose of the face with respect to a monocular RGB-D camera placed on the end-effector. These estimates are passed into an unscented Kalman filter on manifolds state estimator and a pose based visual servo control loop to move the swab to the designated pose in front of the nostril. Our pipeline was validated with human trials, featuring a cohort of 25 participants. The system is effective, reaching the nostril for 84% of participants, and our statistical analysis did not find significant demographic biases within the cohort.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"469-480"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084732","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 : 2025-03-12DOI: 10.1109/TMRB.2025.3550652
Pengxiu Geng;Mengde Luo;Jianpeng Liu;Tianyao Li;Hongpeng Wang;Yanding Qin;Jianda Han
Optical navigation is the mainstream technology used in intraoperative navigation for the orthopedic surgical robot. The information loss problem in optical navigation system (OTS) seriously affects the surgical process. Different from the conventional orthopedic surgical robot systems, we propose to use an additional navigation robot to adjust the viewpoint of the OTS. In this navigation-operation dual-robot collaborative robotic system for orthopedic surgery (NOR2CORS), a comprehensive range of functions are realized, including CT image segmentation, path planning, intraoperative operation, and active navigation. In this paper, pedicle screw placement is adopted as an example to assess the effectiveness and performance of the NOR2CORS. An assessment criterion is also proposed to assess the accuracy of pedicle screw placement based on the widely-used Gertzbein and Robbins classification. Experiments are conducted on a human spine model (24 pedicles) and ex-vivo porcine spine (10 pedicles). According to the proposed assessment criterion, 96% pedicles on human spine model can be classified as grade A and 4% grade B, with 67% of them as grade A2 and above. In the ex-vivo porcine spine experiment, all pedicle screw placements can be classified as grade A, with 80% of them as grade A2 and above. In the surgical process, all positioning targets can be observed without any interruption in navigation. These results demonstrated the efficiency and accuracy of the NOR2CORS.
{"title":"Design and Accuracy Assessment of NOR²CORS: Navigation-Operation Dual-Robot Collaborative Robotic System for Orthopedic Surgery","authors":"Pengxiu Geng;Mengde Luo;Jianpeng Liu;Tianyao Li;Hongpeng Wang;Yanding Qin;Jianda Han","doi":"10.1109/TMRB.2025.3550652","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550652","url":null,"abstract":"Optical navigation is the mainstream technology used in intraoperative navigation for the orthopedic surgical robot. The information loss problem in optical navigation system (OTS) seriously affects the surgical process. Different from the conventional orthopedic surgical robot systems, we propose to use an additional navigation robot to adjust the viewpoint of the OTS. In this navigation-operation dual-robot collaborative robotic system for orthopedic surgery (NOR2CORS), a comprehensive range of functions are realized, including CT image segmentation, path planning, intraoperative operation, and active navigation. In this paper, pedicle screw placement is adopted as an example to assess the effectiveness and performance of the NOR2CORS. An assessment criterion is also proposed to assess the accuracy of pedicle screw placement based on the widely-used Gertzbein and Robbins classification. Experiments are conducted on a human spine model (24 pedicles) and ex-vivo porcine spine (10 pedicles). According to the proposed assessment criterion, 96% pedicles on human spine model can be classified as grade A and 4% grade B, with 67% of them as grade A2 and above. In the ex-vivo porcine spine experiment, all pedicle screw placements can be classified as grade A, with 80% of them as grade A2 and above. In the surgical process, all positioning targets can be observed without any interruption in navigation. These results demonstrated the efficiency and accuracy of the NOR2CORS.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"502-513"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084778","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 : 2025-03-12DOI: 10.1109/TMRB.2025.3550649
Andreas Christou;Daniel F. N. Gordon;Theodoros Stouraitis;Juan C. Moreno;Sethu Vijayakumar
Personalised rehabilitation can be key to promoting gait independence and quality of life. Robots can enhance therapy by systematically delivering support in gait training, but often use one-size-fits-all control methods, which can be suboptimal. Here, we describe a model-based optimisation method for designing and fine-tuning personalised robotic controllers. As a case study, we formulate the objective of providing assistance as needed as an optimisation problem, and we demonstrate how musculoskeletal modelling can be used to develop personalised interventions. Eighteen healthy participants (age $ = 26~pm ~4$ ) were recruited and the personalised control parameters for each were obtained to provide assistance as needed during a unilateral tracking task. A comparison was carried out between the personalised controller and the non-personalised controller. In simulation, a significant improvement was predicted when the personalised parameters were used. Experimentally, responses varied: six subjects showed significant improvements with the personalised parameters, eight subjects showed no obvious change, while four subjects performed worse. High interpersonal and intra-personal variability was observed with both controllers. This study highlights the importance of personalised control in robot-assisted gait training, and the need for a better estimation of human-robot interaction and human behaviour to realise the benefits of model-based optimisation.
{"title":"Model-Based Optimization for the Personalization of Robot-Assisted Gait Training","authors":"Andreas Christou;Daniel F. N. Gordon;Theodoros Stouraitis;Juan C. Moreno;Sethu Vijayakumar","doi":"10.1109/TMRB.2025.3550649","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550649","url":null,"abstract":"Personalised rehabilitation can be key to promoting gait independence and quality of life. Robots can enhance therapy by systematically delivering support in gait training, but often use one-size-fits-all control methods, which can be suboptimal. Here, we describe a model-based optimisation method for designing and fine-tuning personalised robotic controllers. As a case study, we formulate the objective of providing assistance as needed as an optimisation problem, and we demonstrate how musculoskeletal modelling can be used to develop personalised interventions. Eighteen healthy participants (age <inline-formula> <tex-math>$ = 26~pm ~4$ </tex-math></inline-formula>) were recruited and the personalised control parameters for each were obtained to provide assistance as needed during a unilateral tracking task. A comparison was carried out between the personalised controller and the non-personalised controller. In simulation, a significant improvement was predicted when the personalised parameters were used. Experimentally, responses varied: six subjects showed significant improvements with the personalised parameters, eight subjects showed no obvious change, while four subjects performed worse. High interpersonal and intra-personal variability was observed with both controllers. This study highlights the importance of personalised control in robot-assisted gait training, and the need for a better estimation of human-robot interaction and human behaviour to realise the benefits of model-based optimisation.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"642-654"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084775","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 : 2025-03-12DOI: 10.1109/TMRB.2025.3550664
Léa Boillereaux;Simon Le Floc’h;Franck Jourdan;Gille Camp;Arnaud Tanguy;Abderrahmane Kheddar
We design and evaluate a new knee distraction unloader brace. The proposed device conforms to the nonlinear behavior of the tibiofemoral contact force during squat motions, by means of patient-custom cams. Using pneumatic cylinders as springs, the unloading assistance provided by the brace is tailored to the patient’s pathology and adjusted during the rehabilitation process. To assess the performance of our orthosis, various tests are conducted to evaluate its efficiency in terms of tibiofemoral contact load reduction. For this purpose, a robotic test-bench, equipped with a robotic arm, emulates upper leg motion under applied forces (hybrid force-motion control). A pseudo-leg is attached to the robot end-effector, and the orthosis is mounted onto it. The test bench is instrumented with two six degrees of freedom force-torque sensors. Using these force sensors as ground truth, tibiofemoral contact force measurements are obtained with and without our orthosis and compared. A pair of cams is fabricated based on data from a patient whose information is retrieved from the Orthoload database. Experimental results demonstrate a contact force reduction of up to 100% within the force range corresponding to the robot’s maximum capacity.
{"title":"A Distraction Knee-Brace and a Robotic Testbed for Tibiofemoral Load Reduction During Squatting","authors":"Léa Boillereaux;Simon Le Floc’h;Franck Jourdan;Gille Camp;Arnaud Tanguy;Abderrahmane Kheddar","doi":"10.1109/TMRB.2025.3550664","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550664","url":null,"abstract":"We design and evaluate a new knee distraction unloader brace. The proposed device conforms to the nonlinear behavior of the tibiofemoral contact force during squat motions, by means of patient-custom cams. Using pneumatic cylinders as springs, the unloading assistance provided by the brace is tailored to the patient’s pathology and adjusted during the rehabilitation process. To assess the performance of our orthosis, various tests are conducted to evaluate its efficiency in terms of tibiofemoral contact load reduction. For this purpose, a robotic test-bench, equipped with a robotic arm, emulates upper leg motion under applied forces (hybrid force-motion control). A pseudo-leg is attached to the robot end-effector, and the orthosis is mounted onto it. The test bench is instrumented with two six degrees of freedom force-torque sensors. Using these force sensors as ground truth, tibiofemoral contact force measurements are obtained with and without our orthosis and compared. A pair of cams is fabricated based on data from a patient whose information is retrieved from the Orthoload database. Experimental results demonstrate a contact force reduction of up to 100% within the force range corresponding to the robot’s maximum capacity.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"621-632"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084781","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}
Tissue retraction, one of the basic steps in orthopedic surgery, is related to the smooth entry of surgical instruments into the surgical area and provides a clear surgical perspective for surgeons. This work introduces the first robot-assisted retraction system (RARS) specifically designed for orthopedic surgery. The RARS allows surgeons to set a safe retraction force and roughly set the posture of the retraction device at the beginning. To ensure that the RARS automatically completes the task in a safe manner, we propose a safety control framework that employs iterative enhanced control based on interaction model to handle the retraction interaction problem, and avoids interference with the operation of surgeons through null-space optimization. First, we performed a performance test of the RARS on a phantom model, and the results showed that the maximum tracking error of the retraction force was 0.51N, demonstrating satisfactory tracking performance. Second, we validated the effectiveness of null-space optimization by observing the evolution of joint positions. Finally, we conducted experiments on in-vivo animal and the results showed that the proposed RARS exhibited superior performance in safe retraction force tracking accuracy and tissue damage compared to traditional manual retractions.
{"title":"Development and Validation of a Robot-Assisted Retraction System for Orthopedic Surgery","authors":"Xiaolong Zhu;Yuzhen Jiang;Rui He;Changsheng Li;Xingguang Duan","doi":"10.1109/TMRB.2025.3550648","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550648","url":null,"abstract":"Tissue retraction, one of the basic steps in orthopedic surgery, is related to the smooth entry of surgical instruments into the surgical area and provides a clear surgical perspective for surgeons. This work introduces the first robot-assisted retraction system (RARS) specifically designed for orthopedic surgery. The RARS allows surgeons to set a safe retraction force and roughly set the posture of the retraction device at the beginning. To ensure that the RARS automatically completes the task in a safe manner, we propose a safety control framework that employs iterative enhanced control based on interaction model to handle the retraction interaction problem, and avoids interference with the operation of surgeons through null-space optimization. First, we performed a performance test of the RARS on a phantom model, and the results showed that the maximum tracking error of the retraction force was 0.51N, demonstrating satisfactory tracking performance. Second, we validated the effectiveness of null-space optimization by observing the evolution of joint positions. Finally, we conducted experiments on in-vivo animal and the results showed that the proposed RARS exhibited superior performance in safe retraction force tracking accuracy and tissue damage compared to traditional manual retractions.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"492-501"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084735","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}
Laparoscopic surgery has become a common surgical technique owing to its minimal invasiveness. However, surgeons require advanced techniques, and several studies have evaluated surgical skills through motion measurements to improve skill proficiency. However, existing measurement systems have a low tolerance for occlusion and are difficult to use in operating rooms with many obstacles. Therefore, a hybrid measurement system was developed for laparoscopic surgery. This system consists of an inertial measurement unit (IMU), a distance sensor, and an optical motion capture (MoCap). When MoCap data are unavailable, surgical instrument motion is calculated using the IMU and distance sensor data, and when it is available, the IMU drift is corrected using MoCap data. The MoCap markers were arranged individually, thus facilitating the measurement of multiple instruments simultaneously. The validation experiment in the wet-lab training confirmed that the error was smaller than that measured using MoCap alone, and the subjects expressed that the subjective disturbance caused by the sensors was very small during the procedure. The measurement experiment was conducted in cadaver surgical training, and 15 cases of nephrectomy were successfully recorded. This system facilitated highly accurate measurements during practical surgical training and surgical skills analysis.
{"title":"Development of a Hybrid Measurement System for Surgical Instrument Motion of Laparoscopic Surgery","authors":"Koki Ebina;Takashige Abe;Lingbo Yan;Kiyohiko Hotta;Chihiro Kamijo;Madoka Higuchi;Masafumi Kon;Hiroshi Kikuchi;Haruka Miyata;Ryuji Matsumoto;Takahiro Osawa;Sachiyo Murai;Yo Kurashima;Toshiaki Shichinohe;Masahiko Watanabe;Shunsuke Komizunai;Teppei Tsujita;Kazuya Sase;Xiaoshuai Chen;Taku Senoo;Nobuo Shinohara;Atsushi Konno","doi":"10.1109/TMRB.2025.3550666","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550666","url":null,"abstract":"Laparoscopic surgery has become a common surgical technique owing to its minimal invasiveness. However, surgeons require advanced techniques, and several studies have evaluated surgical skills through motion measurements to improve skill proficiency. However, existing measurement systems have a low tolerance for occlusion and are difficult to use in operating rooms with many obstacles. Therefore, a hybrid measurement system was developed for laparoscopic surgery. This system consists of an inertial measurement unit (IMU), a distance sensor, and an optical motion capture (MoCap). When MoCap data are unavailable, surgical instrument motion is calculated using the IMU and distance sensor data, and when it is available, the IMU drift is corrected using MoCap data. The MoCap markers were arranged individually, thus facilitating the measurement of multiple instruments simultaneously. The validation experiment in the wet-lab training confirmed that the error was smaller than that measured using MoCap alone, and the subjects expressed that the subjective disturbance caused by the sensors was very small during the procedure. The measurement experiment was conducted in cadaver surgical training, and 15 cases of nephrectomy were successfully recorded. This system facilitated highly accurate measurements during practical surgical training and surgical skills analysis.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"550-561"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084738","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}
In this study, we propose a robust myoelectric intention recognition framework to recognize human locomotion mode and detect irrelevant locomotion intention. The framework is integrated into the control system of the lower limb exoskeleton robot for experimental validation. Most conventional electromyography (EMG) intention detection methods aim to accurately detect the target motion intentions but ignore the possible effects of irrelevant intentions. In traditional action intention recognition strategies, most researchers did not consider entering irrelevant action intentions into the model during training. Therefore, when using a classification model, if irrelevant action intentions are input, the model will still recognize it as a type of target action intention. That can lead to incorrect recognition results, which will cause the robot to perform wrong movements and pose a safety risk to the wearer. To detect and reject irrelevant motion intentions, we first used the dual-purpose autoencoder-guided temporal convolution network (DA-TCN) to obtain discriminative features of the surface EMG signal. Autoencoders (AE)/Variable Autoencoders (VAE) are then trained for each of the seven deep features of the target motion intention. In addition, irrelevant motion intentions are detected according to the value of their reconstruction error. The recall rate of this method for the detection of irrelevant motion intentions exceeds 99% and the accuracy rate exceeds 99%.At the same time, we replaced the TCN with the LSTM model and compared the performance of the two after adding irrelevant motion discrimination. We collected data on seven goals and three unrelated motor intentions from seven experimenters for testing and completed an online experimental validation. The motion recognition accuracy of all the experimenters can be maintained above 86%.
{"title":"Irrelevant Locomotion Intention Detection for Myoelectric Assistive Lower Limb Robot Control","authors":"Xiaoyu Song;Jiaqing Liu;Heng Pan;Haotian Rao;Can Wang;Xinyu Wu","doi":"10.1109/TMRB.2025.3550736","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3550736","url":null,"abstract":"In this study, we propose a robust myoelectric intention recognition framework to recognize human locomotion mode and detect irrelevant locomotion intention. The framework is integrated into the control system of the lower limb exoskeleton robot for experimental validation. Most conventional electromyography (EMG) intention detection methods aim to accurately detect the target motion intentions but ignore the possible effects of irrelevant intentions. In traditional action intention recognition strategies, most researchers did not consider entering irrelevant action intentions into the model during training. Therefore, when using a classification model, if irrelevant action intentions are input, the model will still recognize it as a type of target action intention. That can lead to incorrect recognition results, which will cause the robot to perform wrong movements and pose a safety risk to the wearer. To detect and reject irrelevant motion intentions, we first used the dual-purpose autoencoder-guided temporal convolution network (DA-TCN) to obtain discriminative features of the surface EMG signal. Autoencoders (AE)/Variable Autoencoders (VAE) are then trained for each of the seven deep features of the target motion intention. In addition, irrelevant motion intentions are detected according to the value of their reconstruction error. The recall rate of this method for the detection of irrelevant motion intentions exceeds 99% and the accuracy rate exceeds 99%.At the same time, we replaced the TCN with the LSTM model and compared the performance of the two after adding irrelevant motion discrimination. We collected data on seven goals and three unrelated motor intentions from seven experimenters for testing and completed an online experimental validation. The motion recognition accuracy of all the experimenters can be maintained above 86%.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 2","pages":"655-665"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084801","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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