Pub Date : 2025-08-29DOI: 10.1109/TMRB.2025.3604104
Miguel Nobre Castro;Strahinja Dosen
Semi-autonomous prosthesis controllers based on computer vision improve performance while reducing cognitive effort. However, approaches relying on full-depth data face challenges in being deployed as embedded prosthesis controllers due to the computational demands of processing point clouds. To address this, the present study proposes a method to reconstruct the shape of various daily objects from minimal depth data. This is achieved using four concurrent laser scanner lines instead of a full point cloud. These lines represent the partial contours of an object’s cross-section, enabling its dimensions and orientation to be reconstructed using simple geometry. A control prototype was implemented using a depth sensor with four laser scanners. Vibrotactile feedback was also designed to help users to correctly aim the sensor at target objects. Ten able-bodied volunteers used a prosthesis equipped with the novel controller to grasp ten objects of varying shapes, sizes, and orientations. For comparison, they also tested an existing benchmark system that used full-depth information. The results showed that the novel controller successfully handled all objects and, while performance improved with training, it remained slightly below that of the benchmark. This study marks an important step towards a compact vision-based system for embedded depth sensing in prosthesis grasping.
{"title":"Semi-Autonomous Prosthesis Control Using Minimal Depth Information and Vibrotactile Feedback","authors":"Miguel Nobre Castro;Strahinja Dosen","doi":"10.1109/TMRB.2025.3604104","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3604104","url":null,"abstract":"Semi-autonomous prosthesis controllers based on computer vision improve performance while reducing cognitive effort. However, approaches relying on full-depth data face challenges in being deployed as embedded prosthesis controllers due to the computational demands of processing point clouds. To address this, the present study proposes a method to reconstruct the shape of various daily objects from minimal depth data. This is achieved using four concurrent laser scanner lines instead of a full point cloud. These lines represent the partial contours of an object’s cross-section, enabling its dimensions and orientation to be reconstructed using simple geometry. A control prototype was implemented using a depth sensor with four laser scanners. Vibrotactile feedback was also designed to help users to correctly aim the sensor at target objects. Ten able-bodied volunteers used a prosthesis equipped with the novel controller to grasp ten objects of varying shapes, sizes, and orientations. For comparison, they also tested an existing benchmark system that used full-depth information. The results showed that the novel controller successfully handled all objects and, while performance improved with training, it remained slightly below that of the benchmark. This study marks an important step towards a compact vision-based system for embedded depth sensing in prosthesis grasping.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 4","pages":"1646-1657"},"PeriodicalIF":3.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659199","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 : 2025-08-21DOI: 10.1109/TMRB.2025.3593798
{"title":"IEEE Transactions on Medical Robotics and Bionics Society Information","authors":"","doi":"10.1109/TMRB.2025.3593798","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3593798","url":null,"abstract":"","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"C3-C3"},"PeriodicalIF":3.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11133444","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887733","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 : 2025-08-21DOI: 10.1109/TMRB.2025.3593800
{"title":"IEEE Transactions on Medical Robotics and Bionics Information for Authors","authors":"","doi":"10.1109/TMRB.2025.3593800","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3593800","url":null,"abstract":"","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"C4-C4"},"PeriodicalIF":3.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11133595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887686","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 : 2025-08-21DOI: 10.1109/TMRB.2025.3593796
{"title":"IEEE Transactions on Medical Robotics and Bionics Publication Information","authors":"","doi":"10.1109/TMRB.2025.3593796","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3593796","url":null,"abstract":"","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"C2-C2"},"PeriodicalIF":3.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11133577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887790","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 : 2025-08-08DOI: 10.1109/TMRB.2025.3597014
Cole B. Johnson;Jairo Maldonado-Contreras;Kinsey R. Herrin;Aaron J. Young
A primary challenge in continual learning (CL) for wearable robotics, especially prosthetics, is balancing the need to retain learned knowledge (stability) with the necessity to adapt to new information (plasticity). This balance is crucial for online adaptation, enabling systems to transition between tasks without losing prior knowledge. In this paper, we introduce a novel online optimizer-based framework designed to manage the stability-plasticity balance through strategic datapoint replay and learning-rate adjustments of a deep neural network. We applied this framework to speed estimation systems for transfemoral prostheses (TFA users), conducting offline validation tests using data from 10 individuals with TFA, and online tests with three TFA and six able-bodied (AB) participants. Our results demonstrate statistically significant improvements: in offline settings, our method showed a 39.2% increase in stability and a 35.2% boost in plasticity over traditional CL approaches during leave-one-subject-out validation. Similarly, in real-time trials with AB participants, we observed statistically significant gains in handling both previously encountered and new walking speeds. Finally, trials with individuals with TFA showed that the system improved the plasticity of the baseline model by 67.45% and the stability of the traditional CL approach by 31.36%; reducing overall average walking speed estimation error by 19.47%.
{"title":"Real-Time Balancing of Stability and Plasticity in Continual Learning Enables Adaptive Speed Estimation for Lower-Limb Prostheses","authors":"Cole B. Johnson;Jairo Maldonado-Contreras;Kinsey R. Herrin;Aaron J. Young","doi":"10.1109/TMRB.2025.3597014","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3597014","url":null,"abstract":"A primary challenge in continual learning (CL) for wearable robotics, especially prosthetics, is balancing the need to retain learned knowledge (stability) with the necessity to adapt to new information (plasticity). This balance is crucial for online adaptation, enabling systems to transition between tasks without losing prior knowledge. In this paper, we introduce a novel online optimizer-based framework designed to manage the stability-plasticity balance through strategic datapoint replay and learning-rate adjustments of a deep neural network. We applied this framework to speed estimation systems for transfemoral prostheses (TFA users), conducting offline validation tests using data from 10 individuals with TFA, and online tests with three TFA and six able-bodied (AB) participants. Our results demonstrate statistically significant improvements: in offline settings, our method showed a 39.2% increase in stability and a 35.2% boost in plasticity over traditional CL approaches during leave-one-subject-out validation. Similarly, in real-time trials with AB participants, we observed statistically significant gains in handling both previously encountered and new walking speeds. Finally, trials with individuals with TFA showed that the system improved the plasticity of the baseline model by 67.45% and the stability of the traditional CL approach by 31.36%; reducing overall average walking speed estimation error by 19.47%.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 4","pages":"1634-1645"},"PeriodicalIF":3.8,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659154","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}
Current MR-conditional neurosurgical robotic systems face challenges in bulky navigation setup and limited distal dexterity. To address these, we introduce navigation frames consisting of a compact spherical stereotactic frame and a fixture frame, and a system for MRI-guided laser interstitial thermal therapy (LITT) of brain tumors integrating the navigation frames and a steerable robotic laser manipulator. The novel navigation frames enable in-bore stereotactic targeting through multiple burr holes with a single registration procedure and 3-degree-of-freedom pivoting about each burr hole, while simultaneously support the actuator-equipped manipulator across a large workspace. Kinematic modeling and design optimization were performed for the navigation components to achieve Pareto balance between large laser coverage and sufficient stiffness for the manipulator support. The unique setting enables in the clinical workflow intuitive navigation frames adjustment for stereotactic targeting and iterative laser tip steering to adapt to targeting error and various tumor geometries. Experiments demonstrate high tip positioning accuracy (RMSE 1.45 mm), minimal MR imaging interference ($lt 1.71%~Delta $ SNR, <0.71 mm distortion), and precise MRI-guided tip steering (RMSE 2.1 mm). Compared to existing MRI-guided neurosurgical systems, our system offers practical and accurate navigation, distal robot dexterity, and minimal MR image disruption, potentially improving clinical LITT outcomes and facilitating autonomous MRI-guided ablation strategies.
{"title":"Integrating In-Bore Navigation Frames With Continuum Robot for MRI-Guided Steerable Laser Ablation of Brain Tumor","authors":"Qingpeng Ding;Yongjun Yan;Xin Tong;Kim Yan;Wu Yuan;George Kwok Chu Wong;Shing Shin Cheng","doi":"10.1109/TMRB.2025.3590494","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3590494","url":null,"abstract":"Current MR-conditional neurosurgical robotic systems face challenges in bulky navigation setup and limited distal dexterity. To address these, we introduce navigation frames consisting of a compact spherical stereotactic frame and a fixture frame, and a system for MRI-guided laser interstitial thermal therapy (LITT) of brain tumors integrating the navigation frames and a steerable robotic laser manipulator. The novel navigation frames enable in-bore stereotactic targeting through multiple burr holes with a single registration procedure and 3-degree-of-freedom pivoting about each burr hole, while simultaneously support the actuator-equipped manipulator across a large workspace. Kinematic modeling and design optimization were performed for the navigation components to achieve Pareto balance between large laser coverage and sufficient stiffness for the manipulator support. The unique setting enables in the clinical workflow intuitive navigation frames adjustment for stereotactic targeting and iterative laser tip steering to adapt to targeting error and various tumor geometries. Experiments demonstrate high tip positioning accuracy (RMSE 1.45 mm), minimal MR imaging interference (<inline-formula> <tex-math>$lt 1.71%~Delta $ </tex-math></inline-formula>SNR, <0.71 mm distortion), and precise MRI-guided tip steering (RMSE 2.1 mm). Compared to existing MRI-guided neurosurgical systems, our system offers practical and accurate navigation, distal robot dexterity, and minimal MR image disruption, potentially improving clinical LITT outcomes and facilitating autonomous MRI-guided ablation strategies.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"1099-1110"},"PeriodicalIF":3.8,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887777","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 : 2025-07-21DOI: 10.1109/TMRB.2025.3590483
Sérgio G. Pereira;Pedro Morais;Estevão Lima;João L. Vilaça
Minimally invasive surgeries (MIS) replaced traditional open surgeries to reduce scarring and blood loss. This procedure involves making small abdominal incisions for the placement of instruments and a laparoscope that provides visual access for the surgeon. Initially, an assistant held the laparoscope throughout the procedure, however, with technological advances, teleoperated systems have emerged where surgeons controlled it with bodily impulses. More recently, automated systems have been introduced where the camera is repositioned using image processing and artificial intelligence. This study systematically reviews the advancements in laparoscope camera control over the past decade. It involved searching PubMed and Web of Science until 1st September 2023, using keywords such as “MIS control”, “MIS holders” and “robotics in MIS”, yielding 905 publications. After reviewing abstracts, 71 studies were selected for full reading and classified into manual, teleoperated, automated, or hybrid control systems. This results found diverse innovative approaches to laparoscope camera control, however, few automatic methods have yet been validated in clinics. In the last five years, automatic and hybrid systems have increased significantly, approaching the number of teleoperated solutions. In the future, it is expected that these systems will improve precision and reduce the workload of medical teams.
微创手术（MIS）取代了传统的开放手术，以减少疤痕和失血。这个过程包括在腹部做一个小切口，以便放置器械和腹腔镜，为外科医生提供视觉通道。最初，一名助手在整个手术过程中握住腹腔镜，然而，随着技术的进步，远程操作系统已经出现，外科医生可以通过身体冲动来控制它。最近，引入了自动化系统，其中使用图像处理和人工智能重新定位相机。本研究系统回顾了近十年来腹腔镜摄像机控制的进展。它包括搜索PubMed和Web of Science，直到2023年9月1日，使用关键词如“MIS控制”，“MIS持有者”和“MIS中的机器人”，产生905篇出版物。在回顾摘要后，选择71篇研究进行全文阅读，并将其分为手动、远程操作、自动或混合控制系统。该结果发现了多种创新的方法来控制腹腔镜相机，然而，很少有自动方法尚未在临床验证。在过去的五年中，自动和混合系统显著增加，接近远程操作解决方案的数量。在未来，预计这些系统将提高精度，减少医疗团队的工作量。
{"title":"Innovative Medical Navigation Interfaces in Laparoscopic Control: A Systematic Review","authors":"Sérgio G. Pereira;Pedro Morais;Estevão Lima;João L. Vilaça","doi":"10.1109/TMRB.2025.3590483","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3590483","url":null,"abstract":"Minimally invasive surgeries (MIS) replaced traditional open surgeries to reduce scarring and blood loss. This procedure involves making small abdominal incisions for the placement of instruments and a laparoscope that provides visual access for the surgeon. Initially, an assistant held the laparoscope throughout the procedure, however, with technological advances, teleoperated systems have emerged where surgeons controlled it with bodily impulses. More recently, automated systems have been introduced where the camera is repositioned using image processing and artificial intelligence. This study systematically reviews the advancements in laparoscope camera control over the past decade. It involved searching PubMed and Web of Science until 1st September 2023, using keywords such as “MIS control”, “MIS holders” and “robotics in MIS”, yielding 905 publications. After reviewing abstracts, 71 studies were selected for full reading and classified into manual, teleoperated, automated, or hybrid control systems. This results found diverse innovative approaches to laparoscope camera control, however, few automatic methods have yet been validated in clinics. In the last five years, automatic and hybrid systems have increased significantly, approaching the number of teleoperated solutions. In the future, it is expected that these systems will improve precision and reduce the workload of medical teams.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"881-897"},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887684","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}
Echocardiography is a critical tool for diagnosing cardiovascular diseases, offering detailed insights into heart functions. However, its accessibility is currently limited by a shortage of trained sonographers, specific skill requirements, and the physical strain imposed on professionals during repetitive procedures. This article introduces a new robotic system designed to automate the acquisition of transthoracic echocardiography (TTE) images. The system autonomously adjusts the position and orientation of the ultrasound transducer based on analysing real-time ultrasound images, without relying on tomographic data or depth sensors. Initially, the transducer is manually placed on the subject’s skin, and the system uses a deep learning approach to grade the quality of ultrasound images captured at each position. The robot then adjusts its position by spiralling outwards from the starting point, moving to the location with the highest image quality score. Next, the system fine-tunes the transducer’s orientation in 5-degree increments along all three axes of rotation, informed by another deep learning module to identify the field of view. The robotic system was tested using a cardiac simulator, achieving approximately 80% accuracy in acquiring the A4Ch view when the probe was initially positioned randomly in a 6 by 6 cm area beneath the left nipple. The impact of this work would be rapid diagnostics in the Emergency Departments to reduce the length of stay in hospitals, a reduction of hospital admissions related to heart disease by accessing local healthcare communities, acceleration of clearing the post-Covid backlog, and improved quality of life and longevity of patients.
{"title":"Echo-Robot: Semi-Autonomous Cardiac Ultrasound Image Acquisition Using AI and Robotics","authors":"Eliott Laurent;Raska Soemantoro;Kathryn Jenner;Attila Kardos;Gilbert Tang;Yifan Zhao","doi":"10.1109/TMRB.2025.3590471","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3590471","url":null,"abstract":"Echocardiography is a critical tool for diagnosing cardiovascular diseases, offering detailed insights into heart functions. However, its accessibility is currently limited by a shortage of trained sonographers, specific skill requirements, and the physical strain imposed on professionals during repetitive procedures. This article introduces a new robotic system designed to automate the acquisition of transthoracic echocardiography (TTE) images. The system autonomously adjusts the position and orientation of the ultrasound transducer based on analysing real-time ultrasound images, without relying on tomographic data or depth sensors. Initially, the transducer is manually placed on the subject’s skin, and the system uses a deep learning approach to grade the quality of ultrasound images captured at each position. The robot then adjusts its position by spiralling outwards from the starting point, moving to the location with the highest image quality score. Next, the system fine-tunes the transducer’s orientation in 5-degree increments along all three axes of rotation, informed by another deep learning module to identify the field of view. The robotic system was tested using a cardiac simulator, achieving approximately 80% accuracy in acquiring the A4Ch view when the probe was initially positioned randomly in a 6 by 6 cm area beneath the left nipple. The impact of this work would be rapid diagnostics in the Emergency Departments to reduce the length of stay in hospitals, a reduction of hospital admissions related to heart disease by accessing local healthcare communities, acceleration of clearing the post-Covid backlog, and improved quality of life and longevity of patients.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"1307-1316"},"PeriodicalIF":3.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887735","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 : 2025-07-18DOI: 10.1109/TMRB.2025.3590488
Sixu Zhou;Hanjun Kim;Jairo Y. Maldonado-Contreras;Atli Örn Sverrisson;David Langlois;Kinsey R. Herrin;Aaron J. Young
Traditional tuning methods of assistance parameters rely on the experience of human experts but often fail to achieve optimal performance. Human-in-the-loop optimization improves parameter selection but requires extensive in-lab testing. In this study, we rigorously tested two control parameters, early stance knee flexion angle (5° to 12°) and swing initiation timing (55% to 65% of the gait cycle), with ten individuals with transfemoral amputation using a commercially available robotic prosthetic knee, Össur Power Knee, and a passive foot, Pro-Flex LP. We measured energy expenditure, joint work, and user preferences during treadmill walking. Results showed a 15.6% reduction in metabolic rate with stance flexion decreasing from 12° to 5° (p<0.05). User preferences favored lower stance flexion and personalized swing initiation. Personalized-best settings reduced the metabolic rate by 4.1% (stance flexion) and 9.8% (swing initiation) compared to the best-on-average settings (p<0.05). These reductions were also significant when compared to the device default and clinically tuned settings (p<0.05). We proposed an offline learning approach using anthropometric, gait, and prosthesis-related data to estimate optimal settings, yielding a 7.1% reduction in metabolic rate (p<0.05). Our results suggest that this approach achieves comparable energy efficiency without lengthy experiments, enabling automatic parameter tuning with initial measurements.
传统的辅助参数调优方法依赖于人类专家的经验，但往往无法达到最优的性能。人在环优化改进了参数选择，但需要大量的实验室测试。在这项研究中，我们严格测试了两个控制参数，早期站立膝关节弯曲角度（5°至12°）和摆动起始时间（55%至65%的步态周期），10例经股截肢患者使用市售机器人假膝Össur Power knee和被动足Pro-Flex LP。我们测量了在跑步机上行走时的能量消耗、关节工作和用户偏好。结果显示，当体位屈曲从12°减少到5°时，代谢率降低了15.6% （p<0.05）。用户偏好较低的姿态弯曲和个性化的挥拍开始。与平均最佳设置相比，个性化最佳设置可使代谢率降低4.1%（姿态弯曲）和9.8%（摇摆开始）（p<0.05）。与设备默认设置和临床调整设置相比，这些降低也很显著（p<0.05）。我们提出了一种离线学习方法，使用人体测量学、步态和假体相关数据来估计最佳设置，代谢率降低7.1% （p<0.05）。我们的研究结果表明，这种方法无需冗长的实验即可实现相当的能源效率，并且可以通过初始测量自动调整参数。
{"title":"An Anthropometry-Based Personalization of Powered Knee Prosthesis for Metabolic Efficiency","authors":"Sixu Zhou;Hanjun Kim;Jairo Y. Maldonado-Contreras;Atli Örn Sverrisson;David Langlois;Kinsey R. Herrin;Aaron J. Young","doi":"10.1109/TMRB.2025.3590488","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3590488","url":null,"abstract":"Traditional tuning methods of assistance parameters rely on the experience of human experts but often fail to achieve optimal performance. Human-in-the-loop optimization improves parameter selection but requires extensive in-lab testing. In this study, we rigorously tested two control parameters, early stance knee flexion angle (5° to 12°) and swing initiation timing (55% to 65% of the gait cycle), with ten individuals with transfemoral amputation using a commercially available robotic prosthetic knee, Össur Power Knee, and a passive foot, Pro-Flex LP. We measured energy expenditure, joint work, and user preferences during treadmill walking. Results showed a 15.6% reduction in metabolic rate with stance flexion decreasing from 12° to 5° (p<0.05). User preferences favored lower stance flexion and personalized swing initiation. Personalized-best settings reduced the metabolic rate by 4.1% (stance flexion) and 9.8% (swing initiation) compared to the best-on-average settings (p<0.05). These reductions were also significant when compared to the device default and clinically tuned settings (p<0.05). We proposed an offline learning approach using anthropometric, gait, and prosthesis-related data to estimate optimal settings, yielding a 7.1% reduction in metabolic rate (p<0.05). Our results suggest that this approach achieves comparable energy efficiency without lengthy experiments, enabling automatic parameter tuning with initial measurements.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"1263-1274"},"PeriodicalIF":3.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887791","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 paper, an existing physiological muscle model that predicts muscular force in response to electrical stimulation is adapted to be compatible with gradient-based optimization, in particular with numerical optimal control/estimation problems. The objective is to integrate biomechanical models with those that correlate muscle force generation with electrical pulses from a physiological perspective, with the aim of achieving optimal stimulation patterns in activities assisted by functional electrical stimulation. To this end, the activation dynamics of the original model, initially constrained to a stimulation train of predefined and constant length, is reformulated to account for stimulation sequences that dynamically change over time. This is typically necessary to simulate complex motions, which would otherwise be impossible to achieve with the earliest formulation. To identify the model parameters, experimental torque data of 3 participants with spinal cord injury performing electrically evoked isometric quadriceps contractions at different knee angles are used. We then employ an optimal control framework to demonstrate the model’s ability to predict knee torques and the possibility of achieving optimized stimulation patterns in simulation for controlling muscle force and knee extension. Our results reveal that the identified model allows accurate prediction of knee torque and optimization of stimulation patterns while satisfying the system’s dynamics at the skeletal and physiological muscle levels. This proof of concept is a first step towards physiological muscle model-based control of functional electrical stimulation to achieve movements that best exploit an individual’s physiological and biomechanical characteristics.
{"title":"Numerical-Optimal-Control-Compliant Muscle Model for Electrically Evoked Contractions","authors":"Tiago Coelho-Magalhães;Christine Azevedo-Coste;François Bailly","doi":"10.1109/TMRB.2025.3590453","DOIUrl":"https://doi.org/10.1109/TMRB.2025.3590453","url":null,"abstract":"In this paper, an existing physiological muscle model that predicts muscular force in response to electrical stimulation is adapted to be compatible with gradient-based optimization, in particular with numerical optimal control/estimation problems. The objective is to integrate biomechanical models with those that correlate muscle force generation with electrical pulses from a physiological perspective, with the aim of achieving optimal stimulation patterns in activities assisted by functional electrical stimulation. To this end, the activation dynamics of the original model, initially constrained to a stimulation train of predefined and constant length, is reformulated to account for stimulation sequences that dynamically change over time. This is typically necessary to simulate complex motions, which would otherwise be impossible to achieve with the earliest formulation. To identify the model parameters, experimental torque data of 3 participants with spinal cord injury performing electrically evoked isometric quadriceps contractions at different knee angles are used. We then employ an optimal control framework to demonstrate the model’s ability to predict knee torques and the possibility of achieving optimized stimulation patterns in simulation for controlling muscle force and knee extension. Our results reveal that the identified model allows accurate prediction of knee torque and optimization of stimulation patterns while satisfying the system’s dynamics at the skeletal and physiological muscle levels. This proof of concept is a first step towards physiological muscle model-based control of functional electrical stimulation to achieve movements that best exploit an individual’s physiological and biomechanical characteristics.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 3","pages":"1297-1306"},"PeriodicalIF":3.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887841","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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