Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3653182
Lijiang Luan;Roger Adams;Evangelos Pappas;Adrian Pranata;Gordon Waddington;Jie Lyu;Jia Han
Individual differences in the biomechanical characteristics of chronic ankle instability (CAI) and the heterogeneity in treatment responses suggest that CAI may have distinguishable subtypes. However, the existing selection criteria for CAI are limited, and the current CAI model groups various types of ankle instability without any precise differentiation of subtypes. This study aimed to apply clustering analysis to identify distinct CAI subtypes. An ordered dataset representing three CAI types (perceived ankle instability (PAI), functional ankle instability (FAI), and mechanical ankle instability (MAI)) was designed, and the K-means clustering algorithm was then applied to clinical data from 210 participants, including individuals with CAI, copers, and healthy people. Clustering analysis was performed using the Cumberland Ankle Instability Tool (CAIT), Identification of Functional Ankle Instability (IdFAI), and anterior drawer test (ADT) scores as indicators, followed by dimensionality reduction and cluster validation. The K-Means clustering algorithm identified five distinct CAI subtypes: PAI, FAI, PAI+FAI, PAI+FAI+MAI, and Sub-coper. The clustering model based on clinical data confirmed the absence of pure MAI and showed that CAI patients could present with varying levels of instability. The most prevalent subtype might be a combination of PAI and FAI. This study demonstrates that, by using clustering analysis, CAI can be categorized into distinct subtypes, offering a more precise diagnostic framework. This approach supports the development of subgroup-based management strategies for CAI and highlights the need for updated selection criteria for CAI.
慢性踝关节不稳定（CAI）的生物力学特征的个体差异和治疗反应的异质性表明，CAI可能有可区分的亚型。然而，现有的CAI选择标准有限，目前的CAI模型将各种类型的踝关节不稳定进行分组，没有精确的亚型区分。本研究旨在应用聚类分析来识别不同的CAI亚型。设计了一个代表三种CAI类型（感知性踝关节不稳定（PAI）、功能性踝关节不稳定（FAI）和机械性踝关节不稳定（MAI））的有序数据集，然后将K-means聚类算法应用于来自210名参与者的临床数据，包括患有CAI的个体、患者和健康人。以Cumberland Ankle Instability Tool （CAIT）、Identification of Functional Ankle Instability （IdFAI）和前抽屉测试（ADT）评分为指标进行聚类分析，然后进行降维和聚类验证。K-Means聚类算法确定了5种不同的CAI亚型：PAI、FAI、PAI+FAI、PAI+FAI+MAI和Sub-coper。基于临床资料的聚类模型证实了单纯MAI的不存在，表明CAI患者可能存在不同程度的不稳定性。最常见的亚型可能是PAI和FAI的组合。本研究表明，通过聚类分析，CAI可以分为不同的亚型，提供了更精确的诊断框架。这种方法支持基于子组的CAI管理策略的开发，并强调需要更新CAI的选择标准。
{"title":"Unraveling Chronic Ankle Instability: A Data-Driven Clustering Approach to Redefine Subtypes and Improve Diagnosis","authors":"Lijiang Luan;Roger Adams;Evangelos Pappas;Adrian Pranata;Gordon Waddington;Jie Lyu;Jia Han","doi":"10.1109/TNSRE.2026.3653182","DOIUrl":"10.1109/TNSRE.2026.3653182","url":null,"abstract":"Individual differences in the biomechanical characteristics of chronic ankle instability (CAI) and the heterogeneity in treatment responses suggest that CAI may have distinguishable subtypes. However, the existing selection criteria for CAI are limited, and the current CAI model groups various types of ankle instability without any precise differentiation of subtypes. This study aimed to apply clustering analysis to identify distinct CAI subtypes. An ordered dataset representing three CAI types (perceived ankle instability (PAI), functional ankle instability (FAI), and mechanical ankle instability (MAI)) was designed, and the K-means clustering algorithm was then applied to clinical data from 210 participants, including individuals with CAI, copers, and healthy people. Clustering analysis was performed using the Cumberland Ankle Instability Tool (CAIT), Identification of Functional Ankle Instability (IdFAI), and anterior drawer test (ADT) scores as indicators, followed by dimensionality reduction and cluster validation. The K-Means clustering algorithm identified five distinct CAI subtypes: PAI, FAI, PAI+FAI, PAI+FAI+MAI, and Sub-coper. The clustering model based on clinical data confirmed the absence of pure MAI and showed that CAI patients could present with varying levels of instability. The most prevalent subtype might be a combination of PAI and FAI. This study demonstrates that, by using clustering analysis, CAI can be categorized into distinct subtypes, offering a more precise diagnostic framework. This approach supports the development of subgroup-based management strategies for CAI and highlights the need for updated selection criteria for CAI.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"894-905"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11346862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145958763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3652858
Imad Eddine Tibermacine;Samuele Russo;Christian Napoli
Electroencephalographic (EEG) decoding relies heavily on second-order (covariance) structure that lives on the manifold of symmetric positive-definite (SPD) matrices. Conventional deep networks in Euclidean space ignore this geometry, distorting geodesic relations between covariances; classical Riemannian pipelines respect SPD metrics but typically use fixed projections and a single global tangent embedding, which limits task adaptivity and incurs cubic costs in the channel dimension. We propose a fully geometry-consistent architecture that preserves manifold structure end-to-end while remaining trainable at scale. A compact depthwise-separable convolutional neural network (CNN) produces features whose regularized covariances lie on the SPD manifold. A learnable orthonormal projection, optimized on the Stiefel manifold via Riemannian stochastic gradient descent (SGD) with QR-factorization (QR) retraction, reduces dimensionality without breaking positive-definiteness and preserves an eigenvalue floor. We then perform tangent space graph-SPD aggregation on a scalp $k$ -nearest-neighbor graph—neighbor covariances are transported to the reference tangent space, attention-averaged, and mapped back via the exponential—followed by a log-Euclidean mapping and linear softmax classification. This Stiefel$!to $ Graph-SPD$!to log $ chain explains why full geometric consistency matters: it avoids Euclidean shortcuts, keeps all intermediates SPD, and makes log/exp costs cubic in the reduced rank $d$ . In cross-subject evaluation on three public datasets, the model attains ${83}.{2}%!/!{81}.{5}%!/!{79}.{7}%$ accuracy with improved macro-${F}_{{1}}$ , strong separability (macro-AUROC $approx {0}.{90}$ ), and well-calibrated probabilities (ECE $le {0}.{04}$ ), outperforming strong Euclidean CNNs and Riemannian baselines while remaining computationally pragmatic.
{"title":"Stiefel-SPD Manifold Graph Convolution for End-to-End EEG Learning","authors":"Imad Eddine Tibermacine;Samuele Russo;Christian Napoli","doi":"10.1109/TNSRE.2026.3652858","DOIUrl":"10.1109/TNSRE.2026.3652858","url":null,"abstract":"Electroencephalographic (EEG) decoding relies heavily on second-order (covariance) structure that lives on the manifold of symmetric positive-definite (SPD) matrices. Conventional deep networks in Euclidean space ignore this geometry, distorting geodesic relations between covariances; classical Riemannian pipelines respect SPD metrics but typically use fixed projections and a single global tangent embedding, which limits task adaptivity and incurs cubic costs in the channel dimension. We propose a fully geometry-consistent architecture that preserves manifold structure end-to-end while remaining trainable at scale. A compact depthwise-separable convolutional neural network (CNN) produces features whose regularized covariances lie on the SPD manifold. A learnable orthonormal projection, optimized on the Stiefel manifold via Riemannian stochastic gradient descent (SGD) with QR-factorization (QR) retraction, reduces dimensionality without breaking positive-definiteness and preserves an eigenvalue floor. We then perform tangent space graph-SPD aggregation on a scalp <inline-formula> <tex-math>$k$ </tex-math></inline-formula>-nearest-neighbor graph—neighbor covariances are transported to the reference tangent space, attention-averaged, and mapped back via the exponential—followed by a log-Euclidean mapping and linear softmax classification. This Stiefel<inline-formula> <tex-math>$!to $ </tex-math></inline-formula>Graph-SPD<inline-formula> <tex-math>$!to log $ </tex-math></inline-formula> chain explains why full geometric consistency matters: it avoids Euclidean shortcuts, keeps all intermediates SPD, and makes log/exp costs cubic in the reduced rank <inline-formula> <tex-math>$d$ </tex-math></inline-formula>. In cross-subject evaluation on three public datasets, the model attains <inline-formula> <tex-math>${83}.{2}%!/!{81}.{5}%!/!{79}.{7}%$ </tex-math></inline-formula> accuracy with improved macro-<inline-formula> <tex-math>${F}_{{1}}$ </tex-math></inline-formula>, strong separability (macro-AUROC <inline-formula> <tex-math>$approx {0}.{90}$ </tex-math></inline-formula>), and well-calibrated probabilities (ECE <inline-formula> <tex-math>$le {0}.{04}$ </tex-math></inline-formula>), outperforming strong Euclidean CNNs and Riemannian baselines while remaining computationally pragmatic.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"595-606"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Non-invasive neural interfaces (NIs) are increasingly investigated in upper limb neurorehabilitation, where they exploit biosignals, such as electroencephalography (EEG) and electromyography (EMG), to decode motor intentions using artificial intelligence (AI). Yet, traditional systems are complex and difficult to use outside the clinic. Wearable devices have the potential for innovative neurorehabilitation solutions thanks to their comfort, easy-to-use and long-term monitoring. However, current AI approaches require adaptation to the technical constraints of wearable devices, and the related state-of-the-art is not clearly explained and summarized. In this work, a systematic literature review on 51 studies was conducted analyzing them according to five important concepts: biosignals, wearable devices, AI-driven methods, upper limb, and clinical applications. The review highlights methodological heterogeneity, a variety of wearable sensor configurations, and open challenges related to accuracy, robustness, and clinical validation. Finally, we discuss how explainable AI (XAI) and generative AI (GenAI) may contribute to improve the interpretability and personalization of future neurorehabilitation systems.
{"title":"Artificial Intelligence and Wearable Technologies for Upper Limb Neurorehabilitation","authors":"Ilaria Siviero;Nicola Valè;Gloria Menegaz;Ander Ramos-Murguialday;Silvia Francesca Storti","doi":"10.1109/TNSRE.2026.3651949","DOIUrl":"10.1109/TNSRE.2026.3651949","url":null,"abstract":"Non-invasive neural interfaces (NIs) are increasingly investigated in upper limb neurorehabilitation, where they exploit biosignals, such as electroencephalography (EEG) and electromyography (EMG), to decode motor intentions using artificial intelligence (AI). Yet, traditional systems are complex and difficult to use outside the clinic. Wearable devices have the potential for innovative neurorehabilitation solutions thanks to their comfort, easy-to-use and long-term monitoring. However, current AI approaches require adaptation to the technical constraints of wearable devices, and the related state-of-the-art is not clearly explained and summarized. In this work, a systematic literature review on 51 studies was conducted analyzing them according to five important concepts: biosignals, wearable devices, AI-driven methods, upper limb, and clinical applications. The review highlights methodological heterogeneity, a variety of wearable sensor configurations, and open challenges related to accuracy, robustness, and clinical validation. Finally, we discuss how explainable AI (XAI) and generative AI (GenAI) may contribute to improve the interpretability and personalization of future neurorehabilitation systems.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"732-749"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11342313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3653297
Susan K. Coltman;Luis Vargas;Xiaogang Hu
Restoring dexterous hand function after neurological injury requires precise control over distinct muscles with different neuromuscular architectures. The extrinsic and intrinsic muscles of the human hand differ in anatomy, motor unit structure, and neural input; however, their relative efficiency in converting evoked reflex activity into functional muscle forces, essential for the development of neural stimulation strategies, remains unclear. H-reflex-based stimulation preferentially recruits small and fatigue-resistant motor units through spinal pathways, offering advantages over direct M-wave activation for fine control of finger forces and sustained muscle activation. Here, we quantified reflex-mediated force transmission in these muscle groups using transcutaneous nerve stimulation, high-density electromyography, and finger-specific force measurements in 12 neurologically intact adults. We found that intrinsic muscles produced significantly greater normalized H-reflex-evoked force than extrinsic muscles, a muscle effect consistent across all fingers. This difference was strongly predicted by spinal excitability, as indexed by the ratio between the maximum amplitudes of H-reflex and M-wave, which emerged as a key mechanistic determinant of reflex force efficiency. Notably, greater force selectivity, as measured by a lower finger coactivation index, was associated with enhanced reflex output, suggesting that improved spatial targeting amplifies functional benefits. Higher trial-to-trial variability in intrinsic muscles likely reflects greater cortical modulation, suggesting the need for adaptive stimulation strategies. These results reveal fundamental differences in reflex transmission efficiency between intrinsic and extrinsic hand muscles, providing physiological evidence for optimizing fatigue-resistant neural stimulation protocols in assistive and rehabilitation technologies.
{"title":"Spinal Excitability Mediates Reflex-Evoked Force in Intrinsic Versus Extrinsic Finger Muscles","authors":"Susan K. Coltman;Luis Vargas;Xiaogang Hu","doi":"10.1109/TNSRE.2026.3653297","DOIUrl":"10.1109/TNSRE.2026.3653297","url":null,"abstract":"Restoring dexterous hand function after neurological injury requires precise control over distinct muscles with different neuromuscular architectures. The extrinsic and intrinsic muscles of the human hand differ in anatomy, motor unit structure, and neural input; however, their relative efficiency in converting evoked reflex activity into functional muscle forces, essential for the development of neural stimulation strategies, remains unclear. H-reflex-based stimulation preferentially recruits small and fatigue-resistant motor units through spinal pathways, offering advantages over direct M-wave activation for fine control of finger forces and sustained muscle activation. Here, we quantified reflex-mediated force transmission in these muscle groups using transcutaneous nerve stimulation, high-density electromyography, and finger-specific force measurements in 12 neurologically intact adults. We found that intrinsic muscles produced significantly greater normalized H-reflex-evoked force than extrinsic muscles, a muscle effect consistent across all fingers. This difference was strongly predicted by spinal excitability, as indexed by the ratio between the maximum amplitudes of H-reflex and M-wave, which emerged as a key mechanistic determinant of reflex force efficiency. Notably, greater force selectivity, as measured by a lower finger coactivation index, was associated with enhanced reflex output, suggesting that improved spatial targeting amplifies functional benefits. Higher trial-to-trial variability in intrinsic muscles likely reflects greater cortical modulation, suggesting the need for adaptive stimulation strategies. These results reveal fundamental differences in reflex transmission efficiency between intrinsic and extrinsic hand muscles, providing physiological evidence for optimizing fatigue-resistant neural stimulation protocols in assistive and rehabilitation technologies.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"607-616"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11346860","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigated the potential of using virtual reality (VR) as a platform for early-stage design of upper-limb prostheses as well as evaluation with a focus on performance, cognitive workload and usability. Three prosthetic device control modes (Direct Control, DC; Pattern Recognition, PR; and Continuous Control, CC) were compared across physical device (PD) and VR settings. Results indicated that task performance was generally lower in VR than in PD for DC and CC modes, likely due to the reduction of haptic cues and stricter spatial-alignment requirements in the VR interaction setting. PR mode, however, showed consistent performance across settings, highlighting its resilience to sensory limitations in VR. Cognitive workload differed by mode, with DC showing reduced workload in VR due to visual task performance aids (e.g., automatic counting of successful clothespin relocations or door-handle turns), while the PR and CC modes produced higher perceived workload, likely due to the VR simulation control demands. Usability scores were consistent across settings and control modes, highlighting the reliability of VR as a platform for early-stage prosthetic evaluation. These findings highlight the potential of VR as a cost-effective, accessible platform to refine prosthetic control algorithms and facilitate user adaptation, while also emphasizing the need for enhancements, such as haptic feedback to improve VR applicability for advanced design and development.
{"title":"Virtual Reality as a Platform for Upper-Limb Prosthetic Control Modes Evaluation and Early-Stage Design","authors":"Yunmei Liu;Junho Park;Daniel Delgado;Austin Music;Joseph Berman;Jaime Ruiz;David Kaber;He Huang;Maryam Zahabi","doi":"10.1109/TNSRE.2026.3652083","DOIUrl":"10.1109/TNSRE.2026.3652083","url":null,"abstract":"This study investigated the potential of using virtual reality (VR) as a platform for early-stage design of upper-limb prostheses as well as evaluation with a focus on performance, cognitive workload and usability. Three prosthetic device control modes (Direct Control, DC; Pattern Recognition, PR; and Continuous Control, CC) were compared across physical device (PD) and VR settings. Results indicated that task performance was generally lower in VR than in PD for DC and CC modes, likely due to the reduction of haptic cues and stricter spatial-alignment requirements in the VR interaction setting. PR mode, however, showed consistent performance across settings, highlighting its resilience to sensory limitations in VR. Cognitive workload differed by mode, with DC showing reduced workload in VR due to visual task performance aids (e.g., automatic counting of successful clothespin relocations or door-handle turns), while the PR and CC modes produced higher perceived workload, likely due to the VR simulation control demands. Usability scores were consistent across settings and control modes, highlighting the reliability of VR as a platform for early-stage prosthetic evaluation. These findings highlight the potential of VR as a cost-effective, accessible platform to refine prosthetic control algorithms and facilitate user adaptation, while also emphasizing the need for enhancements, such as haptic feedback to improve VR applicability for advanced design and development.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"583-594"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11340725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145958906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3653477
Chenquan Xu;Yuanshuo Tan;Zach Strout;Guoxing Liu;Kezhe Zhu;Hong Wang;Peter B. Shull
Despite the growing demand for healthcare services due to an aging population, patients often avoid traditional rehabilitation centers due to high costs, time constraints, and discomfort experienced in laboratory or hospital settings. Home-based rehabilitation offers a promising alternative, but real-time kinematic monitoring and assessment remain challenging. We thus propose a real-time, wireless, portable approach for computing full-body kinematics through OpenSim. Twenty-two subjects performed walking, running, squatting, boxing, yoga, dance, badminton, stair climbing, and seated extremity exercise movements, while wearing 12 SageMotion inertial measurement units (IMUs). Real-time IMU kinematics were computed at 20 Hz and offline kinematics at 100 Hz and were compared with reference optical motion capture kinematics to determine accuracy. Real-time walking and stair climbing were most accurate, both with median RMSE of 5.3 deg. The most accurate joint angle was lumber rotation with median RMSE of 2.7 deg, and the overall median RMSE for all activities across all joints was 7.4 deg. Overall mean RMSE between real-time and offline IMU estimation was 0.7 deg, and mean latency from IMU data reception at the processing hub to kinematics generation was 31.7 ms. This approach could dramatically improve clinical and remote care by enabling rapid assessment and real-time biofeedback for rehabilitation, with potential to significantly enhance patient assessment and treatment outcomes.
{"title":"Real-Time OpenSim via IMUs for Full Body Kinematics During Gait, Sports, Exercise, and Dance Movements","authors":"Chenquan Xu;Yuanshuo Tan;Zach Strout;Guoxing Liu;Kezhe Zhu;Hong Wang;Peter B. Shull","doi":"10.1109/TNSRE.2026.3653477","DOIUrl":"10.1109/TNSRE.2026.3653477","url":null,"abstract":"Despite the growing demand for healthcare services due to an aging population, patients often avoid traditional rehabilitation centers due to high costs, time constraints, and discomfort experienced in laboratory or hospital settings. Home-based rehabilitation offers a promising alternative, but real-time kinematic monitoring and assessment remain challenging. We thus propose a real-time, wireless, portable approach for computing full-body kinematics through OpenSim. Twenty-two subjects performed walking, running, squatting, boxing, yoga, dance, badminton, stair climbing, and seated extremity exercise movements, while wearing 12 SageMotion inertial measurement units (IMUs). Real-time IMU kinematics were computed at 20 Hz and offline kinematics at 100 Hz and were compared with reference optical motion capture kinematics to determine accuracy. Real-time walking and stair climbing were most accurate, both with median RMSE of 5.3 deg. The most accurate joint angle was lumber rotation with median RMSE of 2.7 deg, and the overall median RMSE for all activities across all joints was 7.4 deg. Overall mean RMSE between real-time and offline IMU estimation was 0.7 deg, and mean latency from IMU data reception at the processing hub to kinematics generation was 31.7 ms. This approach could dramatically improve clinical and remote care by enabling rapid assessment and real-time biofeedback for rehabilitation, with potential to significantly enhance patient assessment and treatment outcomes.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"650-662"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11347027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigated the effects of knee braces with differing stiffness on in vivo knee kinematics and neuromuscular control during single-leg lateral landings. 14 healthy males performed landings under three conditions: no brace (Control), low-stiffness (Type-1), and high-stiffness (Type-2). Kinematics were quantified via dual fluoroscopic imaging, and sEMG recorded seven lower-limb muscles. Brace mechanics were assessed via three-point bending. Statistical analysis used repeated-measures ANOVA (α = 0.05). Kinematically, neither brace restricted knee flexion. Both significantly reduced varus angle (Type-1: 27–100% stance, ${p} = 0.043$ ; Type-2: 60–100% stance, ${p} = 0.033$ ), and Type-2 also lowered peak sagittal flexion acceleration (5.0 rad/s2, ${p} = 0.013$ ). Neuromuscularly, Type-1 enhanced multiplanar control, advancing rectus femoris (154.7 ms vs. Type-2, ${p} = 0.005$ ) and vastus lateralis (35.6 ms vs. Control, ${p} = 0.046$ ) activation without increasing rotational instability. Conversely, Type-2 demonstrated a trade-off: despite earlier vastus medialis activation (43.6 ms vs. Control, ${p} = 0.011$ ), it significantly delayed gluteus medius activation (23.9 ms vs. Type-1, ${p} = 0.037$ ) and, critically, exacerbated compensatory internal-rotation acceleration (3.3 rad/s2 vs. Type-1, ${p} = 0.006$ ) at peak flexion. The low-stiffness brace leveraged neuromuscular coordination for multiplanar stability, whereas the high-stiffness brace improved frontal-plane protection at the cost of rotational instability. These findings provide biomechanical evidence for the synergistic optimization of mechanical support and neuromuscular adaptation in knee brace design for populations with similar characteristics to the young male athletes studied herein.
{"title":"Biomechanical Trade-Offs in Knee Brace Stiffness: Dynamic Stability During Single-Leg Lateral Landings in Young Males","authors":"Dongxu Wang;Yang Song;Dong Sun;Fengping Li;Diwei Chen;Zhanyi Zhou;Qiaolin Zhang;Xuanzhen Cen;Bálint Kovács;Zixiang Gao;Liangliang Xiang;Yaodong Gu","doi":"10.1109/TNSRE.2026.3653016","DOIUrl":"10.1109/TNSRE.2026.3653016","url":null,"abstract":"This study investigated the effects of knee braces with differing stiffness on in vivo knee kinematics and neuromuscular control during single-leg lateral landings. 14 healthy males performed landings under three conditions: no brace (Control), low-stiffness (Type-1), and high-stiffness (Type-2). Kinematics were quantified via dual fluoroscopic imaging, and sEMG recorded seven lower-limb muscles. Brace mechanics were assessed via three-point bending. Statistical analysis used repeated-measures ANOVA (α = 0.05). Kinematically, neither brace restricted knee flexion. Both significantly reduced varus angle (Type-1: 27–100% stance, <inline-formula> <tex-math>${p} = 0.043$ </tex-math></inline-formula>; Type-2: 60–100% stance, <inline-formula> <tex-math>${p} = 0.033$ </tex-math></inline-formula>), and Type-2 also lowered peak sagittal flexion acceleration (5.0 rad/s2, <inline-formula> <tex-math>${p} = 0.013$ </tex-math></inline-formula>). Neuromuscularly, Type-1 enhanced multiplanar control, advancing rectus femoris (154.7 ms vs. Type-2, <inline-formula> <tex-math>${p} = 0.005$ </tex-math></inline-formula>) and vastus lateralis (35.6 ms vs. Control, <inline-formula> <tex-math>${p} = 0.046$ </tex-math></inline-formula>) activation without increasing rotational instability. Conversely, Type-2 demonstrated a trade-off: despite earlier vastus medialis activation (43.6 ms vs. Control, <inline-formula> <tex-math>${p} = 0.011$ </tex-math></inline-formula>), it significantly delayed gluteus medius activation (23.9 ms vs. Type-1, <inline-formula> <tex-math>${p} = 0.037$ </tex-math></inline-formula>) and, critically, exacerbated compensatory internal-rotation acceleration (3.3 rad/s2 vs. Type-1, <inline-formula> <tex-math>${p} = 0.006$ </tex-math></inline-formula>) at peak flexion. The low-stiffness brace leveraged neuromuscular coordination for multiplanar stability, whereas the high-stiffness brace improved frontal-plane protection at the cost of rotational instability. These findings provide biomechanical evidence for the synergistic optimization of mechanical support and neuromuscular adaptation in knee brace design for populations with similar characteristics to the young male athletes studied herein.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"626-637"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11346811","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3653049
Gokul Krishna Raja Padmaja;Nikunj Arunkumar Bhagat;Pragathi Priyadharsini Balasubramani
Brain-machine interfaces (BMIs) have the potential to improve stroke rehabilitation by actively facilitating sensory-cognitive-motor connections to restore movement. However, individuals with cognitive impairments are often excluded from BMI-based neurorehabilitation due to concerns about impaired cognition, specifically reduced attention and executive control. We propose leveraging the trial-wise dynamics of large-scale cognitive control networks—specifically, the frontoparietal (FPN) and cingulo-opercular (CON) networks—to build neural markers of cognitive control. Using existing BMI datasets, we demonstrate that trial-wise activity within these networks predicts motor task performance, suggesting that cognitive control signals in these networks could serve as adaptive modulations for BMI-based rehabilitation. Our system is able to predict unsuccessful BMI trials at the population level about 84.2% of the time on average, with an overall mean accuracy of 72.2% in a 3-fold cross-validation. Additionally, in a leave-one-subject-out validation, our system achieved 71% specificity on average, with an overall mean accuracy of 68.3%. Notably, model performance varies across subjects, with some individuals showing up to 92% specificity and 100% sensitivity. Unlike previous studies that primarily focus on resting-state data, our findings point toward the untapped potential of incorporating cognitive network state monitoring into BMI systems to optimize online performance through trials. Specifically, we suggest that our pre-trained models can be fine-tuned with subject-specific information to design more targeted rehabilitation programs that enhance motor performance by identifying precise attention and learning tasks to improve the successful response of the network model in patients with significant cognitive impairment.
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Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3653653
E. L. Spieker;C. Otto;K. Ruprecht;T. Schauer;C. Salchow-Hömmen;N. Wenger
Leg spasticity and gait impairments are common symptoms in Primary and Secondary Progressive Multiple Sclerosis (PPMS, SPMS). Transcutaneous Spinal Cord Stimulation (tSCS) has been shown to alleviate these symptoms in individuals with spinal cord injury. Here, we present the first case series (German Clinical Trials Register DRKS00032742) that determines the effect of repeatedly applied tSCS in progressive MS. Nine participants, experiencing spasticity and gait impairments, received 30 min of tSCS (biphasic pulses, 50 Hz) twice a week for four weeks. Before, during, and one week after termination of the treatment, we monitored spasticity with the Modified Ashworth Scale (MAS) and a reduced version of the Tardieu Scale, as well as gait performance, and gait kinematics. Additionally, patient-reported outcome measures were determined. We observed moderate and large effect sizes after seven tSCS treatments in the bilateral MAS (p = 0.34) and bilateral Tardieu sum score, (p = 0.11) respectively. These effects persisted on a moderate level at follow-up. The performance in clinical gait tests showed mixed results with negligible, small and moderate effects at the end of the treatment. Subjective questionnaires revealed a large effect on fatigue and no effect on patient-reported gait deficits. We observed small effects on the range of motion of the hip and knee at the end of the treatment period. This case series suggests that repeated application of tSCS may help reduce spasticity in individuals with progressive MS. These findings highlight the need for further investigation in controlled study designs beyond a single-arm approach, such as randomized controlled trials.
{"title":"Investigating the Effects of Repeated Transcutaneous Spinal Cord Stimulation on Spasticity and Gait in Multiple Sclerosis: A Case Series","authors":"E. L. Spieker;C. Otto;K. Ruprecht;T. Schauer;C. Salchow-Hömmen;N. Wenger","doi":"10.1109/TNSRE.2026.3653653","DOIUrl":"10.1109/TNSRE.2026.3653653","url":null,"abstract":"Leg spasticity and gait impairments are common symptoms in Primary and Secondary Progressive Multiple Sclerosis (PPMS, SPMS). Transcutaneous Spinal Cord Stimulation (tSCS) has been shown to alleviate these symptoms in individuals with spinal cord injury. Here, we present the first case series (German Clinical Trials Register DRKS00032742) that determines the effect of repeatedly applied tSCS in progressive MS. Nine participants, experiencing spasticity and gait impairments, received 30 min of tSCS (biphasic pulses, 50 Hz) twice a week for four weeks. Before, during, and one week after termination of the treatment, we monitored spasticity with the Modified Ashworth Scale (MAS) and a reduced version of the Tardieu Scale, as well as gait performance, and gait kinematics. Additionally, patient-reported outcome measures were determined. We observed moderate and large effect sizes after seven tSCS treatments in the bilateral MAS (p = 0.34) and bilateral Tardieu sum score, (p = 0.11) respectively. These effects persisted on a moderate level at follow-up. The performance in clinical gait tests showed mixed results with negligible, small and moderate effects at the end of the treatment. Subjective questionnaires revealed a large effect on fatigue and no effect on patient-reported gait deficits. We observed small effects on the range of motion of the hip and knee at the end of the treatment period. This case series suggests that repeated application of tSCS may help reduce spasticity in individuals with progressive MS. These findings highlight the need for further investigation in controlled study designs beyond a single-arm approach, such as randomized controlled trials.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"663-673"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11347029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-06DOI: 10.1109/TNSRE.2026.3651294
Lu Yu;Linjing Peng;Jingyi Jia;Yu Wei;Yiping Du;Yaokai Gan;Zhe Xu;Yifei Yao
This study assessed location-dependent inhomogeneity in the intracarpal median nerve of healthy subjects using a multimodal framework integrating subject-specific finite element analysis (FEA), ultrasound, and diffusion tensor imaging (DTI). Dynamic B-mode ultrasonography tracked segmental nerve displacement during finger flexion in subjects, with axial strain quantified via speckle cross-correlation. Cross-sectional ultrasound measured nerve cross-sectional area and flattening ratio. Twelve subject-specific FEA models analyzed stress distributions in the nerve, while DTI evaluated diffusion tensor of the median nerve indicating microstructural properties. Correlations between biomechanical and microstructural parameters were examined. Results showed that cross-sectional area, axial strain, von Mises stress, maximum principal stress, and frictional anisotropy of the median nerve decreased from the carpal tunnel inlet to outlet. Strong and significant correlations (r>0.8, P<0.05) were found among these parameters. Our findings in healthy individuals suggest that segmental nerve displacement creates localized strain, particularly at the carpal tunnel inlet. These potential biomechanical vulnerabilities could contribute to the initiation or progression of Carpal Tunnel Syndrome, a hypothesis requiring further clinical investigation.
本研究采用多模态框架，结合受试者特异性有限元分析（FEA）、超声和弥散张量成像（DTI），评估了健康受试者腕内正中神经的位置依赖性不均匀性。动态b型超声追踪受试者手指屈曲时的节段性神经位移，轴向应变通过散斑互相关量化。横断超声测量神经横截面积和压平比。12个受试者特定的FEA模型分析了神经中的应力分布，而DTI评估了正中神经的扩散张量，表明微观结构特性。研究了生物力学和微观结构参数之间的相关性。结果表明，从腕管入口到出口，正中神经的横截面积、轴向应变、von Mises应力、最大主应力和摩擦各向异性均减小。相关性强且显著（P < 0.05, P < 0.05）
{"title":"Multimodal Location-Dependent Biomechanical Characterization and Numerical Modeling of Inhomogeneous Median Nerve in Carpal Tunnel","authors":"Lu Yu;Linjing Peng;Jingyi Jia;Yu Wei;Yiping Du;Yaokai Gan;Zhe Xu;Yifei Yao","doi":"10.1109/TNSRE.2026.3651294","DOIUrl":"10.1109/TNSRE.2026.3651294","url":null,"abstract":"This study assessed location-dependent inhomogeneity in the intracarpal median nerve of healthy subjects using a multimodal framework integrating subject-specific finite element analysis (FEA), ultrasound, and diffusion tensor imaging (DTI). Dynamic B-mode ultrasonography tracked segmental nerve displacement during finger flexion in subjects, with axial strain quantified via speckle cross-correlation. Cross-sectional ultrasound measured nerve cross-sectional area and flattening ratio. Twelve subject-specific FEA models analyzed stress distributions in the nerve, while DTI evaluated diffusion tensor of the median nerve indicating microstructural properties. Correlations between biomechanical and microstructural parameters were examined. Results showed that cross-sectional area, axial strain, von Mises stress, maximum principal stress, and frictional anisotropy of the median nerve decreased from the carpal tunnel inlet to outlet. Strong and significant correlations (r>0.8, P<0.05) were found among these parameters. Our findings in healthy individuals suggest that segmental nerve displacement creates localized strain, particularly at the carpal tunnel inlet. These potential biomechanical vulnerabilities could contribute to the initiation or progression of Carpal Tunnel Syndrome, a hypothesis requiring further clinical investigation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"563-572"},"PeriodicalIF":5.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11333895","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145911119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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