IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Biophysical Models With Adaptive Online Learning for Direct Neural Control of Prostheses","authors":"Joris Gentinetta;Michael F. Fernandez;Junqing Qiao;Maria Ramos Gonzalez;Hugh M. Herr","doi":"10.1109/TNSRE.2025.3599114","DOIUrl":"10.1109/TNSRE.2025.3599114","url":null,"abstract":"Direct neural control of multi-articulating prosthetic hands is critical for achieving dexterous manipulation in unstructured environments. However, such control — predicting continuous movements over independent degrees of freedom — remains confined to research settings. In contrast, pattern recognition systems are widely employed for their simple, user-friendly training procedures, though their limitation to a set of discrete whole-hand poses restricts functionality. To bridge this gap, we designed a direct neural controller and a training procedure to support adaptive retraining, enabling users to improve controller predictions or incorporate new movements using a single RGB camera. It explicitly models musculoskeletal dynamics and employs a neural network-based method for motor intent disambiguation, which we term “synergy inversion”. The defined dynamics constrain the predicted kinetics and kinematics to a physiologically realizable manifold, while synergy inversion can capture nonlinear patterns of muscle coactivation missing from traditional musculoskeletal models. In experiments with eight biologically intact participants and two individuals with unilateral transradial amputation, the proposed paradigm predicted trajectories for seven degrees of freedom and improved performance through online learning, achieving lower error than both purely neural and purely biophysical baseline models. This work represents a step toward the adoption of direct neural control of upper extremity prostheses in real-world settings, offering the flexibility of pattern recognition training within a more performant control framework.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3201-3211"},"PeriodicalIF":5.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11124939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855131","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}

{"title":"Simultaneous and Proportional Myoelectric Control of Multiple Degrees of Freedom in Individuals With Chronic Hemiparesis","authors":"Caleb J. Thomson;W. Caden Hamrick;Jakob W. Travis;Michael D. Adkins;Patrick P. Maitre;Steven R. Edgely;Jacob A. George","doi":"10.1109/TNSRE.2025.3599062","DOIUrl":"10.1109/TNSRE.2025.3599062","url":null,"abstract":"Stroke is a leading cause of disability worldwide, with most survivors experiencing chronic motor deficits. Myoelectric orthoses, controlled by residual muscle activity from the paretic limb, can restore upper-limb function to patients. However, existing commercial myoelectric orthoses are limited to only a single hand motion with fixed force output. In the adjacent field of myoelectric prostheses, regression algorithms have enabled simultaneous and proportional position control over multiple degrees of freedom (DOFs), which in turn has improved user dexterity. Here, we explore, for the first time, the ability to regress the kinematic position of multiple DOFs in parallel from paretic muscle activity using a Kalman filter. We collected data from seven hemiparetic patients and systematically explored the root mean squared error (RMSE) of kinematic predictions for various degrees of freedom. We show that proportional position control is possible for multiple hand and wrist motions and that unidirectional DOFs perform better than bidirectional DOFs. Using previously reported RMSEs from healthy participants as a benchmark, we found that 86% of hemiparetic patients achieved functional 2-DOF control, 57% achieved functional 3-DOF control, and 29% achieved functional 4-DOF control. Performance was similar across patient characteristics and different combinations of DOFs. This work demonstrates that multi-DOF regression is readily achievable for some hemiparetic patients. Restoring wrist motion, in addition to grasping, could have a substantial impact on the dexterity and independence of hemiparetic patients. As such, this work serves as an important first step towards multi-DOF assistive upper-limb exoskeletons.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3246-3258"},"PeriodicalIF":5.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11124928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855135","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}

{"title":"Motor Neuron Loss Detection Based on EMG Probability Density Function Shape Descriptors","authors":"Javier Navallas;Lucia Biurrun;Cristina Mariscal;Silvia Recalde-Villamayor;Armando Malanda;Javier Rodríguez-Falces","doi":"10.1109/TNSRE.2025.3599103","DOIUrl":"10.1109/TNSRE.2025.3599103","url":null,"abstract":"EMG interference pattern analysis is routinely used in the assessment of motor neuron loss. We propose systematizing interference pattern analysis by recording an isometric ramp contraction of a muscle, from minimum to maximum activation level. Three EMG probability density function (PDF) shape descriptors are then employed to quantify the PDF evolution assessing EMG filling through contraction: filling factor, negentropy, and kurtosis. The three filling curves are fitted with an exponential model, and the decay constant parameters are employed to obtain a feature vector that characterizes the EMG filling behavior of the muscle. Results show a tendency of the filling curves to shorten and not reach saturation when neuropathy is simulated, and a subsequent dependency of the decay constant parameters with neuropathy progression. We demonstrate, with a set of real signals and through simulation experiments, the ability of the features to be used by a classification system to detect motor neuron loss. With the set of real signals (from 40 subjects with L5 radiculopathy and 40 healthy controls), results show a 0.86 sensibility and 0.84 specificity, indicating a promising performance when incorporated into clinical decision support systems.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3189-3200"},"PeriodicalIF":5.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11124927","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855134","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}

{"title":"Brain Network Analysis Reveals Age-Related Differences in Topological Reorganization During Vigilance Decline","authors":"Lingyun Gao;Rui Zhang;Mengru Xu;Yi Sun;Gang Li;Yu Sun","doi":"10.1109/TNSRE.2025.3598197","DOIUrl":"10.1109/TNSRE.2025.3598197","url":null,"abstract":"To mitigate the economic losses and safety risks caused by reduced alertness of individuals in the context of an aging workforce, mental fatigue among the elderly is an issue worthy of in-depth exploration. Despite convergent studies on cognitive aging, the differential alterations in brain network topology between the elderly and young individuals during vigilance decline remain unclear. Here, a prolonged 30-min psychomotor vigilance task (PVT) was employed to induce mental fatigue, where both behavioral performance and electroencephalography (EEG) data were collected from healthy elderly (n =30) and young participants (n =40). Subsequently, EEG functional connectivity was constructed and the differences in network topological properties between the two groups were quantitatively evaluated based on global and nodal metrics. Both groups an exhibited age-independent significant decline in behavioral performance with time on task. Moreover, age-related dysconnectivity pattern was revealed over a wide frequency range (<inline-formula> <tex-math>$1-45$ </tex-math></inline-formula> Hz) in the elderly group, which further developed toward less optimal network architecture. Specifically, significant deficits in nodal efficiency were revealed in most of the brain regions, and the frontal area exhibited significant age-by-time interaction effect, which was attributed to a significant decline in the elderly group. Statistically significant correlation between behavioral and network metrics was also found. Overall, our results provide some of the first quantitative insights for revealing the neural mechanisms of age differences during mental fatigue, which may contribute to the rational arrangement of personnel in real-world scenarios with high alertness demands.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3160-3170"},"PeriodicalIF":5.2,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11123522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144835002","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}

{"title":"Foot Pressure-Based Abnormal Gait Recognition With Multi-Scale Cross-Attention Fusion","authors":"Menghao Yuan;Yan Wang;Xiaohu Zhou;Meijiang Gui;Aihui Wang;Chen Wang;Guotao Li;Hongnian Yu;Lin Meng;Zengguang Hou","doi":"10.1109/TNSRE.2025.3597639","DOIUrl":"10.1109/TNSRE.2025.3597639","url":null,"abstract":"Abnormal gait recognition plays a critical role in healthcare, particularly for the early diagnosis and continuous monitoring of neurological and musculoskeletal disorders, such as Parkinson’s disease and orthopedic injuries. This study proposes MSCAF-Gait, a Multi-Scale Cross-Attention Fusion Network designed specifically for abnormal gait recognition using foot pressure sensors. MSCAF-Gait incorporates multi-scale convolutional modules with channel and spatial attention mechanisms to effectively capture features across temporal, channel, and spatial dimensions. A novel cross-attention fusion module further enhances feature representation, enabling precise recognition of diverse abnormal gait patterns. To facilitate this research, we introduce the Pressure-Insole Abnormal Gait (PIAG) dataset, comprising gait data associated with common neurological and musculoskeletal abnormalities. Extensive experiments on the publicly available Gait in Parkinson’s Disease (GaitinPD) dataset and our self-constructed PIAG dataset validate the effectiveness of MSCAF-Gait. Specifically, the model achieves 99.61% accuracy in Parkinsonian gait recognition and 98.88% accuracy in Parkinson’s severity classification. On the PIAG dataset, which includes multiple abnormal gait patterns, MSCAF-Gait attains a high accuracy of 99.42%. Notably, these results are obtained with a lightweight architecture characterized by reduced FLOPs and parameter count, demonstrating that MSCAF-Gait offers both high accuracy and computational efficiency, making it well-suited for real-time deployment on wearable platforms.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3146-3159"},"PeriodicalIF":5.2,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11122534","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144821372","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}

{"title":"Simultaneous Estimation of Wrist Joint Angle and Torque During Isokinetic Contraction Based on HD-sEMG","authors":"Mingjie Yan;Zhe Chen;Jianmin Li;Jinhua Li;Lizhi Pan","doi":"10.1109/TNSRE.2025.3596839","DOIUrl":"10.1109/TNSRE.2025.3596839","url":null,"abstract":"The establishment of a natural and smooth human-computer interface is crucial for myoelectric control, which requires an effective decoding method for movement intention. Based on high-density surface electromyography (HD-sEMG), this study explored a method to simultaneously estimate wrist joint angle and torque during isokinetic contraction. Ten able-bodied individuals were instructed to complete wrist isokinetic flexion and extension tasks with different movement patterns, and the HD-sEMG signals were collected. To decode these signals, a convolutional neural network (CNN) incorporating the global attention mechanism was established, named global attention convolutional neural network (GACNN). Six other decoding models were also used to continuously estimate the wrist joint angle and torque, including support vector machine (SVM), residual network (ResNet), long short-term memory (LSTM), transformer-based model (TBM), muscle synergy-based graph attention networks (MSGAT-LSTM), and spatio-temporal feature extraction network (STFEN). Evaluation metrics including normalized root mean square error (NRMSE) and Pearson’s correlation coefficient (PCC) were applied to evaluate the estimation performance of the seven models. The GACNN showed significantly better estimation performance than SVM, LSTM, ResNet, STFEN and it also demonstrated superior performance over TBM and MSGAT-LSTM in some estimation cases. On average, for all subjects, NRMSE and PCC of the GACNN were <inline-formula> <tex-math>$0.080~pm ~0.013$ </tex-math></inline-formula> and <inline-formula> <tex-math>$0.955~pm ~0.016$ </tex-math></inline-formula>. The result shows the superiority of the neural network incorporating global attention mechanism, which is of great significance for the application of human-computer interaction.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3137-3145"},"PeriodicalIF":5.2,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11119647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798973","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}

{"title":"A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance","authors":"Delaney E. Miller;Ashley E. Brown;Nicholas A. Bianco;Scott L. Delp;Steven H. Collins","doi":"10.1109/TNSRE.2025.3596261","DOIUrl":"10.1109/TNSRE.2025.3596261","url":null,"abstract":"Lower-limb exoskeletons could benefit individuals with knee osteoarthritis by reducing knee loading. Real-time estimation of knee loads could accelerate the development of load-reducing exoskeletons. However, measuring or estimating internal knee forces remains challenging due to the rarity of force-sensing knee implants and complexity of simulation-based methods. We developed two data-driven models to separately estimate the peaks in knee contact force during early and late stance using a limited set of features from electromyography (EMG), ground reaction force (GRF), and knee angle recordings. These models were trained on experimental data from healthy young adults (N = 6) walking with a wide range of knee-ankle exoskeleton torque assistance conditions. Peak knee contact forces were obtained from EMG-informed musculoskeletal simulations in OpenSim Moco. The data-driven models were evaluated using leave-one-subject-out cross validation on their ability to accurately compare exoskeleton assistance conditions. The data-driven models identified directional changes in peak knee contact force larger than 0.1 body weights (BW) with 90% accuracy for early-stance peak and 79% accuracy for late-stance peak. Both models included GRF and knee angle features, but EMG features reflected phase-specific muscle activity: quadriceps appeared in the early-stance model, plantar flexors in late stance, and hamstrings in both. We developed a simple method to rapidly estimate changes in peak knee contact force. This approach is suitable for systematic interventions that aim to reduce knee load, such as human-in-the-loop optimization of exoskeleton assistance.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3116-3128"},"PeriodicalIF":5.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11115116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794331","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}

{"title":"Multivideo Models for Classifying Hand Impairment After Stroke Using Egocentric Video","authors":"Anne Mei;Meng-Fen Tsai;José Zariffa","doi":"10.1109/TNSRE.2025.3596488","DOIUrl":"10.1109/TNSRE.2025.3596488","url":null,"abstract":"Objectives: After stroke, hand function assessments are used as outcome measures to evaluate new rehabilitation therapies, but do not reflect true performance in natural environments. Wearable (egocentric) cameras provide a way to capture hand function information during activities of daily living (ADLs). However, while clinical assessments involve observing multiple functional tasks, existing deep learning methods developed to analyze hands in egocentric video are only capable of considering single ADLs. This study presents a novel multi-video architecture that processes multiple task videos to make improved estimations about hand impairment. Methods: An egocentric video dataset of ADLs performed by stroke survivors in a home simulation lab was used to develop single and multi-input video models for binary impairment classification. Using SlowFast as a base feature extractor, late fusion (majority voting, fully-connected network) and intermediate fusion (concatenation, Markov chain) were investigated for building multi-video architectures. Results: Through evaluation with Leave-One-Participant-Out-Cross-Validation, using intermediate concatenation fusion to build multi-video models was found to achieve the best performance out of the fusion techniques. The resulting multi-video model for cropped inputs achieved an F1-score of <inline-formula> <tex-math>$0.778pm 0.129$ </tex-math></inline-formula> and significantly outperformed its single-video counterpart (F1-score of <inline-formula> <tex-math>$0.696pm 0.102$ </tex-math></inline-formula>). Similarly, the multi-video model for full-frame inputs (F1-score of <inline-formula> <tex-math>$0.796pm 0.102$ </tex-math></inline-formula>) significantly outperformed its single-video counterpart (F1-score of <inline-formula> <tex-math>$0.708pm 0.099$ </tex-math></inline-formula>). Conclusion: Multi-video architectures are beneficial for estimating hand impairment from egocentric video after stroke. Significance: The proposed deep learning solution is the first of its kind in multi-video analysis, and opens the door to further applications in automating other multi-observation assessments for clinical use.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3303-3313"},"PeriodicalIF":5.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11115139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794335","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}

{"title":"Enhancing Postural Monitoring in Wheelchair Users Through Context Classification","authors":"Nerea Perez;Aitziber Mancisidor;Itziar Cabanes;Patrick Vermander","doi":"10.1109/TNSRE.2025.3596472","DOIUrl":"10.1109/TNSRE.2025.3596472","url":null,"abstract":"Globally, the number of wheelchair users is steadily increasing. These people often adopt sitting patterns that reflect their functional status. Monitoring the user’s postural status can help users and healthcare professionals to treat them. However, this posture is sometimes influenced by the environment in which the chairs move, and not necessarily by changes in their functional status. To address this problem, this study presents a model designed to classify wheelchair movement contexts, enabling the identification of what is happening in the user’s environment. To do this, data has been collected using a robust and non-intrusive combined monitoring system, which records both the wheelchair’s movement and the user’s posture. These data have been used to train classifier models capable of distinguishing between seven categories of environments that are common in the daily lives of wheelchair users: flat surface, ramp up, ramp down, right turn, left turn, obstacles, and abrupt braking. These models have been developed using machine learning techniques, such as K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results show an accuracy of 90% in free-running tests and more than 99% in controlled runs. These results remained consistent despite variations in training subjects, validated by leave 2 out cross-validation. This innovative approach has the potential to improve the quality of life of wheelchair users by providing an accurate and effective tool to understand and address complex interactions between the environment and the users’ posture.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3129-3136"},"PeriodicalIF":5.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11115105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794333","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}

{"title":"Benchmarking of IMU-Based Gait Event Detection Algorithms Across Diverse Terrain Conditions","authors":"Carlota Trigo;Pietro Della Vecchia;Francesco Crenna;Adriana Torres-Pardo;Jorge Andrés Gómez;Geronimo Ratto;Teresa Rodríguez Millán;Rubén Martínez Sánchez de la Torre;Jesús Tornero;Juan Moreno;Diego Torricelli","doi":"10.1109/TNSRE.2025.3596319","DOIUrl":"10.1109/TNSRE.2025.3596319","url":null,"abstract":"Objective: Walking on irregular terrains is a common situation in everyday life. The accurate detection of gait events is of paramount importance for characterizing and analyzing gait. While several algorithms have been proposed for gait timing estimation on flat terrains, an assessment of their performance on ecological-like terrains is still lacking. The purpose of the present study is to evaluate the performance of several gait event detection algorithms, as proposed in the literature, in the temporal segmentation of gait across different terrains. Methods: Nine healthy volunteers, each mounted with 17 tri-axial inertial sensors, walked on 12 different terrains with varying slopes. Gait events, identified from a marker-based optoelectronic system, were used as the reference. Nine different algorithms were applied to the data, and their performance was analyzed in terms of precision, recall, F1-score, and detection error. The performance scores of the different algorithms were compared across conditions. Results: In general, the results show a decline in performance when transitioning from flat to other terrains, which aligns with expectations as most algorithms are optimized for regular horizontal ground. However, one method (Paraschiv-Ionescu) showed superior performance, achieving near-perfect F1-scores (close to 1) across most conditions. Conclusion: This study compares IMU-based gait event detection algorithms on irregular terrains, revealing that performance degrades as terrain complexity increases. Additionally, this study showcases the ability of the standardized EUROBENCH benchmarking framework to test locomotion in real-like terrain conditions.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3181-3188"},"PeriodicalIF":5.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11115090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794332","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}