IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Exploring Residual Limb Muscle Activation and Structure in Transtibial Amputees for Improved Prosthetic Control","authors":"Faranak Rostamjoud;Haraldur Björn Sigurðsson;Atli Örn Sverrisson;Sigurður Brynjólfsson;Kristín Briem","doi":"10.1109/TNSRE.2025.3604380","DOIUrl":"10.1109/TNSRE.2025.3604380","url":null,"abstract":"This study investigates the structural and functional characteristics of residual muscles in transtibial amputees (TTAs) to improve electromyography (EMG)-based prosthetic control. Using ultrasonography, we measured the thickness of the Tibialis Anterior (TA), Peroneus Longus (PL), Gastrocnemius Medialis (GM), and Lateralis (GL) at rest and during contraction. Surface EMG was employed to assess muscle activation patterns, co-contraction levels, and accuracy in modulating submaximal contractions at 25%, 50%, and 75% of maximum voluntary contraction (MVC). Results revealed that muscle thickness on the amputated side was significantly lower than on the sound side (p <0.0001), with the TA and PL exhibiting the greatest atrophy. Despite this, all muscles demonstrated significant increases in thickness during contraction (p<0.0001), indicating preserved neuromuscular activity. GL showed the highest percentage increase in thickness (23.7%), followed by PL (20.5%) and GM (15.4%). EMG analysis demonstrated high co-contraction, particularly between TA and PL, which may complicate selective muscle activation for prosthetic control. During dorsiflexion, PL activation was nearly as high as TA, while TA also exhibited unintended activation during eversion, suggesting poor muscle differentiation. During plantarflexion, GM and GL exhibited dominant activation, while the PL showed substantial co-contraction. Accuracy in controlling submaximal contractions was inconsistent, with TA showing the lowest absolute error (0.17), while GM and GL exhibited the highest errors (0.26 and 0.27, respectively). These findings suggest that TTAs retain the ability to activate residual muscles but struggle with selective activation and intensity modulation, emphasizing the need for targeted training and prosthetic control strategies to optimize functional outcomes.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3466-3475"},"PeriodicalIF":5.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952448","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 Plug-and-Play P300-Based BCI With Zero-Training Application","authors":"Jongsu Kim;Sung-Phil Kim","doi":"10.1109/TNSRE.2025.3603979","DOIUrl":"10.1109/TNSRE.2025.3603979","url":null,"abstract":"The practical deployment of P300-based brain–computer interfaces (BCIs) has long been hindered by the need for user-specific calibration and multiple stimulus repetitions. In this study, we build and validate a plug-and-play, zero-training P300 BCI system that operates in a single-trial setting using a pre-trained xDAWN spatial filter and a deep convolutional neural network. Without any subject-specific adaptation, participants could control an IoT device via the BCI system in real time, with decoding accuracy reaching 85.2% comparable to the offline benchmark of 87.8%, demonstrating the feasibility of realizing a plug-and-play BCI. Offline analyses revealed that a small set of parietal and occipital electrodes contributed most to decoding performance, supporting the viability of low-density, high-accuracy BCI configurations. A data sufficiency simulation provided quantitative guidelines for pre-training dataset size, and an error trial analysis showed that both stimulus timing and preparatory attentional state influenced real-time decoding performance. Together, these results demonstrate the real-time validation of a fully pre-trained, zero-training P300 BCI operating on a single-trial basis, without stimulus repetition or user-specific calibration, and offer practical insights for developing scalable, robust, and user-friendly BCI systems.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3443-3454"},"PeriodicalIF":5.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952603","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":"Low-Cost IMU-Based System for Automated Parkinson’s Subtype and Stage Classification to Support Precision Rehabilitation","authors":"Xiangzhi Liu;Hanyi Huang;Yu Gu;Jiaxing Li;Xiangliang Zhang;Tao Liu","doi":"10.1109/TNSRE.2025.3603555","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3603555","url":null,"abstract":"Parkinson’s disease (PD) is one of the most common progressive neurodegenerative disorder, for which early detection and precise rehabilitation planning are essential to alleviate its impact on quality of life and reduce societal burden. Accurate, automated PD subtype classification and staging play a key role in designing effective rehabilitation strategies while minimizing reliance on intensive expert assessments. Unlike existing automated methods that typically depend on high–cost medical imaging (e.g., MRI) or extensive sensor networks, we introduce a low–cost motion measurement system employing only two inertial measurement units (IMUs) placed on the lower legs. We propose a Symbiotic Graph Attention Network (SGAT)–based algorithm that fuses node features and whole-body features for automated PD subtype and stage detection. By establishing a symbiotic mechanism between the subtype and staging tasks and using adaptive fusion weights, our method achieves outstanding performance—subtype accuracy of 0.91 and staging accuracy of 0.85—validated on data from 46 participants. Notably, the entire detection and recognition process requires merely a simple walking task and incurs minimal time cost. The system’s affordability, ease of use, and scalability underscore its substantial potential for large-scale clinical deployment.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3421-3431"},"PeriodicalIF":5.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11143586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929187","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":"Decoding the Variable Velocity of Lower-Limb Stepping Movements From EEG","authors":"Attila Korik;Naomi Du Bois;Jose Sanchez Bornot;Niall McShane;Christoph Guger;Alessandra Del Felice;Olive Lennon;Damien Coyle","doi":"10.1109/TNSRE.2025.3603635","DOIUrl":"10.1109/TNSRE.2025.3603635","url":null,"abstract":"Accurate decoding of lower-limb movement from electroencephalography (EEG) is essential for developing brain–computer interface (BCI) controlled exoskeletons in neurorehabilitation. This study investigates 3D velocity decoding at three fibular anatomical markers during overground stepping in healthy participants (<inline-formula> <tex-math>${N}={9}$ </tex-math></inline-formula>), using two approaches: (1) linear regression (LR) and (2) a deep learning (DL) framework combining convolutional neural networks (CNNs) and long short-term memory (LSTM) units. Participants were divided into two groups: G1 (<inline-formula> <tex-math>${n}={5}$ </tex-math></inline-formula>) performed cued forward and self-paced backward steps; G2 (<inline-formula> <tex-math>${n}={4}$ </tex-math></inline-formula>) performed cued forward and backward steps. The DL model significantly outperformed LR, achieving highest decoding accuracy (DA) in the forward-backward direction at the fibular head (R <inline-formula> <tex-math>$= 0.63pm 0.06$ </tex-math></inline-formula>, M±SD). Topographical analysis identified dominant contributions from the sensorimotor cortex (coupled with frontal regions in G2) within the 8–40 Hz band. Functional connectivity (FC) analysis revealed significant differences: only G2 showed statistically significant FC (<inline-formula> <tex-math>${p}lt {0.05}$ </tex-math></inline-formula>), likely reflecting increased cognitive and sensorimotor demands under dual-cue conditions. In G2, FC occurred across delta (0–4 Hz), theta (4–8 Hz), alpha/mu (8–12 Hz), and low-beta (12–18 Hz) bands, linking motor areas associated with lower- and upper-limb control to other cortical regions, including the middle temporal gyrus (MTG), superior frontal gyrus (SFG), posterior cingulate cortex (PCC), superior parietal lobule (SPL), and supramarginal gyrus (SMG). These findings demonstrate that EEG-based 3D decoding of lower-limb kinematics is feasible during realistic locomotor tasks and suggest that cortical synchronization patterns vary with movement context. Our CNN-LSTM framework may support adaptive, intent-driven exoskeleton development for personalized neurorehabilitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3511-3523"},"PeriodicalIF":5.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11143557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952339","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":"Toward Non-Invasive Electrical Stimulation for Guided Optic Nerve Regeneration","authors":"Pooyan Pahlavan;Peter S. Mayer;Anahit Simonyan;Jonathon Cavaleri;Connie Huang;Robert Grady Briggs;Gabriel Zada;Darrin J. Lee;Kimberly K. Gokoffski;Gianluca Lazzi","doi":"10.1109/TNSRE.2025.3603560","DOIUrl":"10.1109/TNSRE.2025.3603560","url":null,"abstract":"The optic nerve plays a critical role in visual information processing by relaying signals from the retina to the brain. Diseases affecting the optic nerve, such as glaucoma, can severely impair vision due to the nerve’s limited capacity for self-repair. One promising approach to promote nerve regeneration involves the use of electric fields to guide axonal growth. Our previous research demonstrated that an electric field applied to the crushed adult rat optic nerve directed full-length axon regeneration and mediated partial restoration of visual function. While effective, this technique involves placing electrodes in direct contact with the optic nerve, posing challenges, including the need for skilled surgeons and the potential for tissue damage during implantation. Leveraging computer simulations and ex-vivo cadaveric measurements, the work in this paper explores noninvasive methods for generating electric fields along the optic nerve. Results show the promise of computational models to correctly estimate the electric fields induced along the optic nerve, providing a platform for designing optimal stimulation systems that will generate fields known to foster axonal growth.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3616-3625"},"PeriodicalIF":5.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11143547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952552","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 Progressive Multi-Domain Adaptation Network With Reinforced Self-Constructed Graphs for Cross-Subject EEG-Based Emotion and Consciousness Recognition","authors":"Rongtao Chen;Chuwen Xie;Jiahui Zhang;Qi You;Jiahui Pan","doi":"10.1109/TNSRE.2025.3603190","DOIUrl":"10.1109/TNSRE.2025.3603190","url":null,"abstract":"Electroencephalogram (EEG)-based emotion recognition is a vital component in brain-computer interface applications. However, it faces two significant challenges: 1) extracting domain-invariant features while effectively preserving emotion-related information, and 2) aligning the joint probability distributions of data across different individuals. To address these challenges, we propose a progressive multi-domain adaptation network with reinforced self-constructed graphs. Specifically, we introduce EEG-CutMix to construct unlabeled mixed-domain data, facilitating the transition between source and target domains. Additionally, a reinforced self-constructed graphs module is employed to extract domain-invariant features. Finally, a progressive multi-domain adaptation framework is constructed to smoothly align the data distributions across individuals. Experiments on cross-subject datasets demonstrate that our model achieves state-of-the-art performance on the SEED and SEED-IV datasets, with accuracies of 97.03% <inline-formula> <tex-math>$pm ~1.65$ </tex-math></inline-formula>% and 88.18% <inline-formula> <tex-math>$pm ~4.55$ </tex-math></inline-formula>%, respectively. Furthermore, tests on a self-recorded dataset, comprising ten healthy subjects and twelve patients with disorders of consciousness (DOC), show that our model achieves a mean accuracy of 86.65% <inline-formula> <tex-math>$pm ~2.28$ </tex-math></inline-formula>% in healthy subjects. Notably, it successfully applies to DOC patients, with four subjects achieving emotion recognition accuracy exceeding 70%. These results validate the effectiveness of our model in EEG emotion recognition and highlight its potential for assessing consciousness levels in DOC patients. The source code for the proposed model is available at GitHub-seizeall/mycode.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3498-3510"},"PeriodicalIF":5.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952613","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 Novel Optimization Framework for fNIRS: Enhancing Brain Image Reconstruction for Neurorehabilitation","authors":"Yunyi Zhao;Uwe Dolinsky;Hubin Zhao;Shufan Yang","doi":"10.1109/TNSRE.2025.3602894","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3602894","url":null,"abstract":"Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that provides valuable insights into brain activity by measuring haemodynamic changes in blood oxygenation. Despite its potential, the accuracy of fNIRS-based brain image reconstruction is often compromised by motion artefacts. While conventional adaptive signal processing methods can be employed to remove these artefacts, neural networks have demonstrated a more effective alternative. However, traditional neural networks typically have substantial computational and memory requirements, making them unsuitable for resource-constrained wearable platforms. In this study, we propose an optimisation framework for neural network processing specifically designed for wearable devices to enhance the clarity and reliability of fNIRS brain images. Through systematic evaluation and integrate of various datasets on resource limited computing platform, we establish a standardised a standardised proceeding pipeline that can be applied across various fNIRS datasets. The proposed framework is validated on three datasets, demonstrating significant improvements in signal quality and image reconstruction accuracy, while achieving a 24% reduction in memory footprint optimisation. Our findings suggest that adopting a universal preprocessing optimisation strategy could standardise fNIRS data analysis for wearable devices, enabling more consistent and interpretable results across studies. This advancement contributes to the broader application of fNIRS in clinical and neurorehabilitation research, make real-time neuroimaging more feasible and effective.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3409-3420"},"PeriodicalIF":5.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929186","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":"User-Adaptive Variable Impedance Control Using Bayesian Optimization for Robot-Aided Ankle Rehabilitation","authors":"Gautham Manoharan;Hyunglae Lee","doi":"10.1109/TNSRE.2025.3602899","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3602899","url":null,"abstract":"This paper presents a user-adaptive variable impedance control approach for robot-aided rehabilitation, initially focusing on an ankle rehabilitation application. The controller dynamically adjusts the impedance parameters based on the user’s motion intent, thereby providing personalized assistance during motor tasks. Bayesian optimization is employed to enhance speed and accuracy during the motor tasks by minimizing an objective function formulated from the user’s kinematic data. The optimization process incorporates a Gaussian process as a surrogate model to address uncertainties inherent in human behaviors. Furthermore, an outlier rejection method based on the Student-t process is integrated into Bayesian optimization to enhance its robustness. To evaluate the effectiveness of the proposed control approach, a goal-directed target-reaching study was conducted with 15 healthy participants using a wearable ankle robot. The performance metrics of speed, accuracy, task completion time, and user effort were used to compare the optimized variable impedance controller against an unoptimized counterpart. Results showed that the optimized controller achieved an average speed improvement of 9.9% and a 7.6% decrease in deviation from the target trajectory compared to the unoptimized controller. Additionally, the optimized controller reduced task completion time by 6.6% while maintaining a similar level of user effort. Notably, the optimal parameters for each individual varied significantly, highlighting the significance of the user-adaptive approach. Overall, this study demonstrates the effectiveness and feasibility of the proposed optimal variable impedance control approach for robot-aided rehabilitation applications, particularly in the context of ankle rehabilitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3355-3366"},"PeriodicalIF":5.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918320","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":"Stiffness and Deflection of Custom-Fit, 3D-Printed Ankle-Foot Orthoses During Walking, and the Influence of Anthropometric Variability","authors":"Jacquelyn R. Brokamp;Ryan S. Pollard;Iván E. Nail-Ulloa;Michael E. Zabala","doi":"10.1109/TNSRE.2025.3602709","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3602709","url":null,"abstract":"Additive manufacturing enables the rapid production and customization of ankle-foot orthoses (AFOs), offering substantial advantages over traditional fabrication methods. Understanding the mechanical properties of these devices, particularly stiffness and deflection during ambulation, is essential for their effective deployment as it may inform future benchtop performance tests, such as fatigue life analysis. However, previous studies seemingly disregard the combined effects of the passive and active ankle joint contributions to stiffness during ambulation, limiting the predictive accuracy of the mechanical performance tests. Accordingly, this study investigates how the AFO-ankle complex quasi-stiffness (the combined stiffness of the AFO and the ankle joint) and participant-specific anthropometric measures (height and body mass) influence AFO deflection throughout the gait cycle. Nine healthy, unimpaired participants were fitted unilaterally with custom-fit AFOs fabricated using XO-Armor® 3D-scanning and 3D-printing technology. Gait analyses were conducted under AFO and no-AFO conditions, focusing on three subphases of stance: controlled plantarflexion (CPF), controlled dorsiflexion (CDF), and powered plantarflexion (PPF). The results revealed significant positive correlations between anthropometric measures and both AFO-ankle complex quasi-stiffness throughout stance phase and peak AFO deflection in dorsiflexion (<inline-formula> <tex-math>$theta _{textit {DF}, textit {max}}$ </tex-math></inline-formula>). Additionally, AFO-ankle complex quasi-stiffness during CDF (<inline-formula> <tex-math>${k}_{textit {AFO}, textit {CDF}}text {)}$ </tex-math></inline-formula> exhibited an inverse relationship with <inline-formula> <tex-math>$theta _{textit {DF}, textit {max}}$ </tex-math></inline-formula>. No significant relationships were identified between maximum deflection in plantarflexion (<inline-formula> <tex-math>$theta _{textit {PF}, textit {max}}text {)}$ </tex-math></inline-formula> and either anthropometric measures or AFO-ankle complex quasi-stiffness during CPF (<inline-formula> <tex-math>${k}_{textit {AFO}, textit {CPF}}text {)}$ </tex-math></inline-formula>. The findings of this study suggest that height, weight, and <inline-formula> <tex-math>${k}_{textit {AFO}, textit {CDF}}$ </tex-math></inline-formula> should be considered when developing standardized protocols for custom-fit AFOs to enhance the predictive accuracy of benchtop testing, particularly when estimating <inline-formula> <tex-math>$theta _{textit {DF}, textit {max}}$ </tex-math></inline-formula>.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3346-3354"},"PeriodicalIF":5.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917305","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 Comparative Case Study of EMG-Driven Controllers in Transtibial Prostheses","authors":"M. Abdelbar;Faranak Rostamjoud;Anna Lára Ármannsdóttir;Atli Örn Sverrisson;Kristín Briem;Sigurur Brynjólfsson","doi":"10.1109/TNSRE.2025.3602296","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3602296","url":null,"abstract":"Lower limb amputation greatly affects quality of life by restricting functional mobility. Despite advancements in prosthetic design, powered transtibial prostheses still have limitations in user control and adaptability to dynamic environments. This research presents a comparative analysis between a novel electromyography (EMG)-driven variable impedance controller (VIC) and a hybrid controller (HC) that integrates a volitional EMG-driven musculoskeletal model with a finite-state machine impedance controller. A Hill-type muscle model was used to model the gastrocnemius and tibialis anterior muscles. Biomechanical testing was conducted with a transtibial amputee to assess the controllers’ performance across various tasks, including ambulation on level ground, stairs, and ramps, using EMG signals from the residual limb. Results demonstrated that the VIC provided more repeatable performance, perceived control, and power output. Notable effect sizes for peak power, observed in ramp ascent (Cohen’s d <inline-formula> <tex-math>$= -1.04$ </tex-math></inline-formula>) and high-speed level ground walking (Cohen’s d <inline-formula> <tex-math>$= -2.92$ </tex-math></inline-formula>), illustrate robust differences in joint-level output even when walking speeds and cadences were comparable. The greater predictability of the VIC led the user to feel more in control and comfortable throughout the various activities. On the other hand, the HC controller performed better in enabling more seamless transitions between gait subphases, particularly during stair ascent, which led to a significantly higher ROM (<inline-formula> <tex-math>$18.63~pm ~1.53$ </tex-math></inline-formula> deg vs. <inline-formula> <tex-math>$12.43~pm ~1.86$ </tex-math></inline-formula> deg) and nearly double peak power compared to the VIC. This comparison lays the groundwork for future research into optimizing EMG-based control strategies that adapt to both biomechanical demands and user preferences.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3388-3399"},"PeriodicalIF":5.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11138026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929122","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}