IEEE ... International Conference on Rehabilitation Robotics : [proceedings]最新文献

{"title":"Implementation and Validation of a Data-Driven Variable Impedance Controller on the Össur Power Knee.","authors":"T Kevin Best, C Andrew Seelhoff, Robert D Gregg","doi":"10.1109/ICORR66766.2025.11063151","DOIUrl":"10.1109/ICORR66766.2025.11063151","url":null,"abstract":"<p><p>While prototype prostheses and control algorithms have demonstrated compelling clinical benefits in research laboratories, studies with commercially-available robotic prostheses have often failed to demonstrate similar benefits for users, limiting their adoption into mainstream clinical practice. This work is a step towards addressing this shortcoming by presenting the implementation of a phase-based variable impedance controller on the commercially-available Össur Power Knee ${ }^{text {TM }}$ for walking and sit/stand tasks. We show that, through preliminary experiments with $mathrm{N}=4$ high-mobility above-knee prosthesis users, the Power Knee under our controller can produce clear clinical benefits compared to the users' prescribed prostheses. In sitting and standing, users demonstrated generally increased leg-loading symmetry and speed with the Power Knee, indicating easier motions with less over-use of the sound limb. In walking, users demonstrated improved gait with the Power Knee, including increases in toe clearance and early-stance knee flexion. These positive results are similar to our previous work on prototype hardware, demonstrating our controller's hardware generalization and its potential for generating clinical benefits with commercial prostheses. These results are a step towards a promising future in which commercially-available robotic prostheses provide users with concrete clinical benefits.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"7-14"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12258919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612612","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}

{"title":"Optimization of an Exosuit Design for Elbow and Shoulder Joints Considering Comfort and Safety.","authors":"Pierre Garnault, Yannick Aoustin, Vigen Arakelian","doi":"10.1109/ICORR66766.2025.11062959","DOIUrl":"10.1109/ICORR66766.2025.11062959","url":null,"abstract":"<p><p>To ensure comfort and safety in the design of wearable robots, a multi-objective optimization method is defined for shoulder and elbow assistance. This optimization method is evaluated with a simulation platform implemented on Matlab integrating biomechanical features from the software OpenSim. Thus, the interaction forces between skin and cuffs and the metabolic cost could be estimated during design phase. Finally a drinking task trajectory is carried out to validate the efficiency of wearing an exosuit reducing the fatigue by 32%.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"30-35"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612661","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}

{"title":"Development of Multimodal EEG-EMG Human Machine Interface for Hand-Wrist Rehabilitation: A Preliminary Study.","authors":"Minki Kim, SeongHyeon Jo, Hyeonseung Cho, Seungmin Ye, Yeongtae Kim, Hyung-Soon Park","doi":"10.1109/ICORR66766.2025.11063079","DOIUrl":"https://doi.org/10.1109/ICORR66766.2025.11063079","url":null,"abstract":"<p><p>Patients with neurological disorders, such as stroke, often undergo upper limb motor impairments, severely limiting their ability to perform activities of daily living (ADL). Wearable robots have been developed to provide intensive and precise repetitive training for upper limb rehabilitation. Effective rehabilitation requires aligning robotic assistance with patient movement intention to promote brain plasticity. Additionally, robotic assistance must accommodate the complex, coordinated upper limb motions required for ADL tasks, including not only isolated hand movements but also integrated hand and wrist actions. This paper presents a multimodal human-machine interface (HMI) for integrated hand-wrist rehabilitation using both EEG and EMG signals. A three-degrees-of-freedom (3-DOF) soft wearable robot, combining a robotic hand glove and forearm skin brace, was designed to assist coordinated hand and wrist movements during reaching and grasping. EEG signals classified rest and grasp states using a Riemannian geometry approach, while EMG signals from three forearm muscles detected reaching onset to trigger the wrist adjustment. Preliminary tests with four healthy participants demonstrated 85% accuracy in EEG-based classification and sufficient EMG amplitude for motion onset detection. Future studies will expand participant testing to improve system robustness and evaluate its effectiveness for stroke rehabilitation.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"1564-1569"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612483","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}

{"title":"Digital Design Workflow for Individualized 2-DOF Ankle Exoskeletons.","authors":"Hunter M Schmidt, Andy Li, Aytac Teker, Mariana H Rocha, Biruk A Gebre, Karen J Nolan, Kishore Pochiraju, Damiano Zanotto","doi":"10.1109/ICORR66766.2025.11062994","DOIUrl":"https://doi.org/10.1109/ICORR66766.2025.11062994","url":null,"abstract":"<p><p>Gait rehabilitation programs aid individuals recovering from brain injury or severe lower-leg trauma. While robotic exoskeletons may offer advantages over traditional exercise-based interventions, their high cost and lack of personalized fit limit their clinical utility. In this paper, we present a new efficient design workflow for individualized 2-DOF ankle exoskeletons. The anatomical orientations of the talocrural and subtalar joints are estimated by utilizing a functional calibration procedure and then embedded and implemented into the ankle exoskeleton. The exoskeleton is fabricated using affordable additive manufacturing processes to conform to the user's leg morphology. This creates a personalized design that encapsulates the envelope of the ankle joint complex motion. By achieving this without the need for kinematic redundancy, we aim at maintaining a lightweight design with reduced mechanical complexity. Early tests with two healthy individuals indicate the feasibility of the proposed approach.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612486","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}

{"title":"Assessing Repeatability of CLEVERArm Exoskeleton Using Healthy Subjects: A Pilot Study.","authors":"Adib H Laskar, Reza Tafreshi, Muhammad Bin Mughees, Khaled Al-Halabi, Reza Langari, Md Ferdous Wahid","doi":"10.1109/ICORR66766.2025.11062963","DOIUrl":"https://doi.org/10.1109/ICORR66766.2025.11062963","url":null,"abstract":"<p><p>Upper extremity (UE) impairments resulting from non-communicable diseases continue to rise annually across the globe. Robotic devices offer promising solutions for mitigating the long-term logistical challenges and limited recovery outcomes associated with short-term, one-to-one rehabilitation sessions. This study presents a repeatability analysis of CLEVERArm (compact, lightweight, ergonomic, VR/AR-enhanced rehabilitation arm), an eight-degrees-of-freedom (DOF) robotic exoskeleton for treating patients with UE impairments, focusing on validating both single-DOF (sDOF) and multi-DOF (mDOF) trajectories produced by the device. Eighteen healthy subjects performed tasks ranging from simple to complex UE movements associated with activities of daily living. The device then autonomously repeated the movements made by the participants. Across all tasks, CLEVERArm demonstrated low root mean square deviation (<3.42°), and high correlations (>0.99) between reference and repetition trajectories recorded by absolute encoders. High intra-class coefficient values (>0.9) further constitute the system's consistency and accuracy in UE movement over time. These results suggest that CLEVERArm can reliably replicate input trajectories, providing consistent and positive outcomes in rehabilitation settings. Future work will utilize the device's ability to accurately replicate trajectories for designing personalized rehabilitation regimens, monitoring patient progress, and tailoring exercises to individual needs, ultimately enhancing long-term recovery for patients with UE impairments.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"160-165"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612507","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}

{"title":"Assessing Stylistic Differences in the Underlying Biomechanical Objectives of Walking Using Simulation-Based Observational Gait Analysis.","authors":"McKenna C Everett, Wentao Li, Ann Majewicz Fey, Nicholas P Fey","doi":"10.1109/ICORR66766.2025.11063203","DOIUrl":"https://doi.org/10.1109/ICORR66766.2025.11063203","url":null,"abstract":"<p><p>Observational gait analysis and categorical ratings are commonly used by clinicians to assess pathologies. The purpose of this study was to determine the capacity of novice observers to characterize the gait behavior underlying biomechanical performance objectives using stylistic labels. We hypothesized that visual characterization of physics-based musculoskeletal predictive simulations of walking would be sensitive to the biomechanical objective employed by individuals, as well as the visual perspective. We developed 75 full-body muscle-driven predictive gait simulation videos, corresponding to five subject models, five biomechanical objectives, and three visual perspectives. Subject models were constructed for five individuals performing straight line walking, with optimal tracking simulations generated for each using computed muscle control. Direct collocation was used to apply five different objectives to each individual's nominal behavior including metabolic cost, summed and squared muscle activations, time-integrated whole-body angular momentum, time-integrated bilateral ground reaction forces, and an equally weighted multi-objective cost function summing the individual objectives. 100 crowd workers characterized each simulation on a 1-5 scale using stylistic labels corresponding to each objective. Multinomial logistic regression analysis revealed that loading and activation ratings were significant predictors of muscle activation-optimized movements, while activation ratings were significant predictors of movement perspective. Balance ratings were significant for the frontal view alone, suggesting that balance indicators are more easily distinguished in the frontal plane. Collectively, the wisdom of crowds could distinguish motion associated with some biomechanical objectives, but due to the redundancy of motor control strategies used by individuals, the resolution of this observational approach is limited.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612508","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}

{"title":"Assessment Scores for Patients with Central Neurological Conditions in Rehabilitation: A Systematic Review.","authors":"Silvia Campagnini, Kaan Borekci, Kyle Embry, Arun Jayaraman","doi":"10.1109/ICORR66766.2025.11062944","DOIUrl":"https://doi.org/10.1109/ICORR66766.2025.11062944","url":null,"abstract":"<p><p>In rehabilitation, evidence-based practice relies on evaluating patients' standardized outcomes to manage care plans and resources; however, the selection of tools to use is globally inconsistent. Alternatively, emerging technologies can measure high-resolution biomarkers, but their implementation remains a challenge. Given the amount of measures, interest in new scoring systems of patient health synthesizing multisource data is rising. Emerging research in rehabilitation lacks standardized guidelines and psychometric validation of these scores. Hence, this review systematically examines the methods used for developing and validating patient assessment scores in rehabilitation. A systematic search included studies on the development and validation of scores to assess adult patients with central neurological disorders in rehabilitation. Nineteen studies were synthesized. In the score development, simpler methods and input data were more common (11 studies used statistics, 12 clinical data). In the validation, the heterogeneity in reference standards and tests in scores' psychometric analysis highlights the need for evidence and standardized processes in this field. Clinically relevant information is crucial for implementable scores able to guide clinical decision-making and care strategies. Future studies should adopt exploratory approaches, analyzing various input data, methods, and psychometric properties' impact on the score capability to assess patient health.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"675-680"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612511","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}

{"title":"Automated Selectivity-Driven Algorithm for Transcutaneous Spinal Cord Stimulation.","authors":"Mouhamed Zorkot, Riccardo Carpineto, Solaiman Shokur, Silvestro Micera, Mohamed Bouri","doi":"10.1109/ICORR66766.2025.11063011","DOIUrl":"https://doi.org/10.1109/ICORR66766.2025.11063011","url":null,"abstract":"<p><p>Spinal cord injury (SCI) impairs motor function and quality of life. Transcutaneous Spinal Cord Stimulation (tSCS) is a non-invasive method to restore motor function by activating spinal circuits below the lesion. However, its effectiveness is limited by individual variability, reliance on manual electrode placement, and offline muscle analysis. To address these challenges, we developed an online method for detecting spinal reflexes and muscle responses using two automated algorithms: the Ranking-Based Approach (binary method) and the Automated Selectivity-Driven Approach (leveraging a selectivity index), both designed to optimize electrode placement and stimulation amplitude for precise activation of proximal or distal muscles. In a study with six healthy participants in the supine position, the posterior root muscle test was performed automatically using tSCS across three rostrocaudal spinal electrodes. Our findings reinforce the evidence of the rostrocaudal tSCS selectivity, with rostral electrodes activating proximal muscles and caudal ones targeting distal muscles. Both algorithms identified optimal electrode positions and stimulation amplitudes, enhancing tSCS selectivity for lower-limb muscles. Our results suggest that the Automated SelectivityDriven Approach is more appropriate for increasing selectivity for targeted muscle recruitment. These results highlight the potential of automated methods to improve tSCS selectivity for SCI rehabilitation and other conditions.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"989-994"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612513","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}

{"title":"Comparative Analysis of Temporal Difference Learning Methods to Learn General Value Functions of Lower-Limb Signals.","authors":"Sonny T Jones, Grange M Simpson, Wyatt M J Young, Kylee North, Patrick M Pilarski, Ashley N Dalrymple","doi":"10.1109/ICORR66766.2025.11063114","DOIUrl":"https://doi.org/10.1109/ICORR66766.2025.11063114","url":null,"abstract":"<p><p>Millions of people in the United States suffer from paralysis, resulting in significant deficits in motor function. Restricted mobility due to these deficits and the lack of adaptive rehabilitative solutions make traversing complex and challenging terrains unsafe. Exoskeletons offer a promising solution, but their effectiveness could be greatly enhanced by incorporating reinforcement learning algorithms for real-time adaptation to changing environments and the user's unique gait biomechanics. This study explored different temporal difference learning methods for predicting signals recorded from sensors on the lower-limbs, including muscle activation from electromyography, underfoot pressure, and joint angles from goniometers. Specifically, the performance of the temporal difference learning methods TD $(lambda)$, TOTD, and SwiftTD to quickly and accurately predict these signals was examined. From initial findings, SwiftTD generally converged faster, while TOTD typically achieved lower convergence errors. These outcomes varied depending on the specific signal that was being predicted, highlighting the need for careful consideration of algorithm choice depending on the signal, accuracy, and speed. The results, therefore, support the informed selection of specific algorithms for providing predictive knowledge to adaptive, machine learning-controlled assistive rehabilitative technologies. These findings will enable the selection of appropriate predictive algorithms, leading to the development of better exoskeletons and other assistive devices to enhance the mobility and quality of life of individuals with motor paralysis.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"1209-1214"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612527","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}

{"title":"Mapping the Neural Control of Force and Impedance of Wrist Movements Using Robotics and fMRI.","authors":"Kristin Schmidt, Fabrizio Sergi","doi":"10.1109/ICORR66766.2025.11062940","DOIUrl":"https://doi.org/10.1109/ICORR66766.2025.11062940","url":null,"abstract":"<p><p>While robots are becoming increasingly valuable tools in neurorehabilitation, our limited understanding of the brain's response during human-robot interaction tasks restricts advancements in training programs to restore neural pathways after injury. Co-contraction is characteristic of several neuromuscular disorders, such as stroke and cerebral palsy, and it is often targeted by assessments or rehabilitation programs. Despite its importance, the neural mechanisms underlying cocontraction remain poorly understood. To address this gap, this study investigates the neural substrates of muscle co-contraction via functional magnetic resonance imaging (fMRI) during a dynamic motor task with an MR-compatible wrist robot. To establish suitable fMRI experimental conditions, we first conducted a behavioral study assessing muscle activity during a wrist-pointing task with four participants. Participants reached toward a target while experiencing four main perturbation conditions (no force, divergent force, constant force up, and constant force down), designed to elicit distinct force and impedance responses. Following this behavioral validation, five additional participants performed the wrist-pointing task during fMRI. Our results suggest localization of force and impedance control within the cortico-thalamic-cerebellar network. These findings provide new insights into the neural mechanisms of co-contraction, supporting the development of neurorehabilitation paradigms.</p>","PeriodicalId":73276,"journal":{"name":"IEEE ... International Conference on Rehabilitation Robotics : [proceedings]","volume":"2025 ","pages":"1154-1159"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612536","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}