The loss of an upper limb significantly affects daily activities, making advanced prosthesis control crucial for improving the quality of life. Pattern recognition applied to electromyographic signals has emerged as a leading solution for controlling prosthetic hands; yet, most studies focus solely on steady-state muscle activity, neglecting the transient phase of contraction, thereby limiting real-world applicability. To address this limitation, this study introduces a hierarchical approach that combines an Onset Detection Algorithm, a 9-class steady-state gesture classifier, and a three-level force classifier. Additionally, it investigates Self-selected contraction levels across three grasp types, corresponding to subjectively perceived low, medium, and high forces, chosen according to each participant’s preference or perceived exertion. Results demonstrate improved classification accuracy and responsiveness, particularly during early muscle contraction, outperforming state-of-the-art methods. Moreover, optimal contraction levels were found to be grasp-dependent and significantly lower than those commonly used in the literature, emphasizing the need to adjust reference values to reduce fatigue and enhance comfort.
{"title":"Hierarchical Classification of EMG Signal for Hand and Wrist Gestures and Forces in Myoelectric Control","authors":"Roberto Billardello;Katarina Dejanovic;Francesca Cordella;Daniele D’Accolti;Christian Cipriani;Loredana Zollo","doi":"10.1109/TNSRE.2026.3660210","DOIUrl":"10.1109/TNSRE.2026.3660210","url":null,"abstract":"The loss of an upper limb significantly affects daily activities, making advanced prosthesis control crucial for improving the quality of life. Pattern recognition applied to electromyographic signals has emerged as a leading solution for controlling prosthetic hands; yet, most studies focus solely on steady-state muscle activity, neglecting the transient phase of contraction, thereby limiting real-world applicability. To address this limitation, this study introduces a hierarchical approach that combines an Onset Detection Algorithm, a 9-class steady-state gesture classifier, and a three-level force classifier. Additionally, it investigates Self-selected contraction levels across three grasp types, corresponding to subjectively perceived low, medium, and high forces, chosen according to each participant’s preference or perceived exertion. Results demonstrate improved classification accuracy and responsiveness, particularly during early muscle contraction, outperforming state-of-the-art methods. Moreover, optimal contraction levels were found to be grasp-dependent and significantly lower than those commonly used in the literature, emphasizing the need to adjust reference values to reduce fatigue and enhance comfort.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"978-987"},"PeriodicalIF":5.2,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131830","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}
Previously, we proposed a power-free isokinetic training robot designed to provide resistive isokinetic training for knee injury patients during advanced-stage rehabilitation. However, patients in the early stage often lack sufficient muscle strength and necessitate assistive support. To address this limitation, this study introduces a hybrid assistive-resistive isokinetic training robot that integrates active assistance for early-stage knee rehabilitation and power-free resistive training for advanced stages. The system features a compact mechanical design and a reconfigurable control circuit capable of dynamically switching among three modes: active, passive (regeneration), and passive (consumption). Ten healthy subjects and ten knee-injury patients participated in the experimental validation. The results confirmed the adaptability of the system across multiple rehabilitation stages. These findings demonstrate the feasibility of the hybrid assistive-resistive isokinetic training robot and highlight the potential of the system for both clinical application and home-based rehabilitation. Future work will focus on extending the system to multi-joint training and enhancing control algorithms for broader patient populations.
{"title":"A Hybrid Assistive-Resistive Isokinetic Training Robot for Full-Cycle Knee Rehabilitation","authors":"Haoyang Wu;Wenjie Chen;Liheng Tuo;Jiaxin Ren;Linhang Ju;Xingyu Hu;Yixin Shao;Di Shi;Lecheng Ruan;Yan Huang;Bi Zhang;Kunyang Wang;Yanggang Feng;Wuxiang Zhang","doi":"10.1109/TNSRE.2026.3661538","DOIUrl":"10.1109/TNSRE.2026.3661538","url":null,"abstract":"Previously, we proposed a power-free isokinetic training robot designed to provide resistive isokinetic training for knee injury patients during advanced-stage rehabilitation. However, patients in the early stage often lack sufficient muscle strength and necessitate assistive support. To address this limitation, this study introduces a hybrid assistive-resistive isokinetic training robot that integrates active assistance for early-stage knee rehabilitation and power-free resistive training for advanced stages. The system features a compact mechanical design and a reconfigurable control circuit capable of dynamically switching among three modes: active, passive (regeneration), and passive (consumption). Ten healthy subjects and ten knee-injury patients participated in the experimental validation. The results confirmed the adaptability of the system across multiple rehabilitation stages. These findings demonstrate the feasibility of the hybrid assistive-resistive isokinetic training robot and highlight the potential of the system for both clinical application and home-based rehabilitation. Future work will focus on extending the system to multi-joint training and enhancing control algorithms for broader patient populations.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"1294-1304"},"PeriodicalIF":5.2,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131873","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 : 2026-02-03DOI: 10.1109/TNSRE.2026.3660517
Alex C. Dzewaltowski;Philippe Malcolm
Robotic devices can expand the repertoire of rehabilitation methods by enabling actions that cannot be replicated by a physical therapist. We previously developed a technique, we term ‘rapid assistance,’ that can assist movements beginning within the electromechanical delay between muscle activation and muscle contraction. Here, we evaluated the effects of repeated arm extension training with rapid assistance in older adults (n = 18) during a single session. We compared training with rapid assistance to a control group that performed unassisted arm extension training. Participants positively adapted to rapid assistance indicated by quickening reaction times (15.69%, ${t} = -1.79$ , ${p} =0.089$ , ${d} =0.36$ ) and greater extension angular velocities (47.93%, ${t} =3.47$ , ${p} =0.002$ , ${d} =0.56$ ) compared to the control group following training. These motor performance improvements following rapid assistance training may be due to reducing Golgi-tendon inhibition during muscle contraction thereby, introducing an alternate strategy to improve motor performance. This specialized assistive timing may address a trade-off present in rehabilitative practice between assisting a patient or sufficiently challenging them to facilitate functional recovery.
机器人设备可以通过实现物理治疗师无法复制的动作来扩展康复方法的范围。我们之前开发了一种技术，我们称之为“快速辅助”，它可以在肌肉激活和肌肉收缩之间的机电延迟中帮助开始的运动。在这里，我们评估了老年人（n = 18）在单次快速辅助下重复手臂伸展训练的效果。我们将快速辅助训练与无辅助手臂伸展训练的对照组进行了比较。与训练后的对照组相比，参与者积极适应快速援助，反应时间加快（15.69%,t = -1.79, p = 0.089, d = 0.36），扩展角速度加快（47.93%,t = 3.47, p = 0.002, d = 0.56）。快速辅助训练后运动表现的改善可能是由于肌肉收缩过程中高尔基肌腱抑制的减少，从而引入了一种改善运动表现的替代策略。这种专门的辅助时机可以解决在帮助患者或充分挑战他们促进功能恢复之间的权衡。
{"title":"A Robotic Assistance With Specialized Timing Improves Motor Performance: Implications for Movement Training","authors":"Alex C. Dzewaltowski;Philippe Malcolm","doi":"10.1109/TNSRE.2026.3660517","DOIUrl":"10.1109/TNSRE.2026.3660517","url":null,"abstract":"Robotic devices can expand the repertoire of rehabilitation methods by enabling actions that cannot be replicated by a physical therapist. We previously developed a technique, we term ‘rapid assistance,’ that can assist movements beginning within the electromechanical delay between muscle activation and muscle contraction. Here, we evaluated the effects of repeated arm extension training with rapid assistance in older adults (n = 18) during a single session. We compared training with rapid assistance to a control group that performed unassisted arm extension training. Participants positively adapted to rapid assistance indicated by quickening reaction times (15.69%, <inline-formula> <tex-math>${t} = -1.79$ </tex-math></inline-formula>, <inline-formula> <tex-math>${p} =0.089$ </tex-math></inline-formula>, <inline-formula> <tex-math>${d} =0.36$ </tex-math></inline-formula>) and greater extension angular velocities (47.93%, <inline-formula> <tex-math>${t} =3.47$ </tex-math></inline-formula>, <inline-formula> <tex-math>${p} =0.002$ </tex-math></inline-formula>, <inline-formula> <tex-math>${d} =0.56$ </tex-math></inline-formula>) compared to the control group following training. These motor performance improvements following rapid assistance training may be due to reducing Golgi-tendon inhibition during muscle contraction thereby, introducing an alternate strategy to improve motor performance. This specialized assistive timing may address a trade-off present in rehabilitative practice between assisting a patient or sufficiently challenging them to facilitate functional recovery.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"1041-1047"},"PeriodicalIF":5.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11371382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113224","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 : 2026-02-03DOI: 10.1109/TNSRE.2026.3660726
Willy Chou;Bor-Shing Lin;Yu-Chia Chang;Bor-Shyh Lin
Stroke is an emergency cerebrovascular event, resulting in damage to cranial nerves, and the subsequent rehabilitation is necessary to restore neurological function and improve patient outcomes. In clinical settings, the rehabilitation effectiveness is assessed subjectively by experienced physicians or through the use of performance assessment scales. Although several techniques, such as electroencephalography (EEG), cardiopulmonary exercise testing (CPET), and functional magnetic resonance imaging (fMRI), may also be utilized in the evaluation of rehabilitation effectiveness, they require higher costs and the expertise of professional medical staff for experienced operation and post-analysis. Based on the technique of near-infrared spectroscopy (NIRS), a field programmable gate array (FPGA)-based rehabilitation effect assessment headband was proposed. The designed headband could monitor the change in cerebral blood flow non-invasively and continuously under exercise. From the changes in cerebral blood flow, eight perfusion indexes were also extracted in real time and utilized to assess the cardiopulmonary function status of middle-aged and older adults before and after rehabilitation. The analysis algorithm would be completed in the wireless and wearable headband to greatly improve the convenience of use. The experimental results showed that the cardiopulmonary function status could be effectively classified from defined perfusion indexes, and the differences between defined perfusion indexes and the neural network output before and after rehabilitation were also significant.
{"title":"Design of FPGA-Based Rehabilitation Effect Assessment Headband","authors":"Willy Chou;Bor-Shing Lin;Yu-Chia Chang;Bor-Shyh Lin","doi":"10.1109/TNSRE.2026.3660726","DOIUrl":"10.1109/TNSRE.2026.3660726","url":null,"abstract":"Stroke is an emergency cerebrovascular event, resulting in damage to cranial nerves, and the subsequent rehabilitation is necessary to restore neurological function and improve patient outcomes. In clinical settings, the rehabilitation effectiveness is assessed subjectively by experienced physicians or through the use of performance assessment scales. Although several techniques, such as electroencephalography (EEG), cardiopulmonary exercise testing (CPET), and functional magnetic resonance imaging (fMRI), may also be utilized in the evaluation of rehabilitation effectiveness, they require higher costs and the expertise of professional medical staff for experienced operation and post-analysis. Based on the technique of near-infrared spectroscopy (NIRS), a field programmable gate array (FPGA)-based rehabilitation effect assessment headband was proposed. The designed headband could monitor the change in cerebral blood flow non-invasively and continuously under exercise. From the changes in cerebral blood flow, eight perfusion indexes were also extracted in real time and utilized to assess the cardiopulmonary function status of middle-aged and older adults before and after rehabilitation. The analysis algorithm would be completed in the wireless and wearable headband to greatly improve the convenience of use. The experimental results showed that the cardiopulmonary function status could be effectively classified from defined perfusion indexes, and the differences between defined perfusion indexes and the neural network output before and after rehabilitation were also significant.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"988-996"},"PeriodicalIF":5.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11371400","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113193","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 : 2026-02-02DOI: 10.1109/TNSRE.2026.3660215
InHwa Lee;Christopher L. Hunt;Nitish V. Thakor;Rahul R. Kaliki
In recent years, extended reality-based myoelectric training has emerged as a promising approach to prepare users for advanced prosthesis control. This study: 1) identified User Needs for an ideal training tool through qualitative interviews with occupational therapists; 2) developed the Myoelectric Training in Extended Reality (MyoTrainXR) system; and 3) evaluated its usability using an advanced postural control strategy. Six individuals with intact limbs and two with trans-radial upper limb loss underwent four 45-minute training sessions with the Block Builder module. The Pasta Box Task was used during training and evaluation, and the Cup Transfer Task was used only during evaluation. In the Pasta Box Task, participants with intact limbs maintained a 100% completion rate, while their success rate increased from $86.1pm 5.8$ % to $98.5pm 3.7$ %. Participants with upper limb loss began with completion rates between 0% and 40%, improving to 100%, with success rates between 90.9% and 100% by the final evaluation. Iteration completion times showed significant reduction across all participants ($p$ -value < 0.05, linear mixed-effects model), with the median decreasing from 19.4 to 15.8 seconds. The Cup Transfer Task showed a similar trend of significant improvement, demonstrating that the acquired skills generalized to an untrained task. The system also demonstrated excellent usability, with an average System Usability Scale score of $81.9pm 10.0$ . These findings indicate that our user-centered extended reality training tool holds promise for enhancing myoelectric control proficiency.
{"title":"Design and Evaluation of User-Centered Extended Reality Myoelectric Prosthesis Training Tool","authors":"InHwa Lee;Christopher L. Hunt;Nitish V. Thakor;Rahul R. Kaliki","doi":"10.1109/TNSRE.2026.3660215","DOIUrl":"10.1109/TNSRE.2026.3660215","url":null,"abstract":"In recent years, extended reality-based myoelectric training has emerged as a promising approach to prepare users for advanced prosthesis control. This study: 1) identified User Needs for an ideal training tool through qualitative interviews with occupational therapists; 2) developed the Myoelectric Training in Extended Reality (MyoTrainXR) system; and 3) evaluated its usability using an advanced postural control strategy. Six individuals with intact limbs and two with trans-radial upper limb loss underwent four 45-minute training sessions with the Block Builder module. The Pasta Box Task was used during training and evaluation, and the Cup Transfer Task was used only during evaluation. In the Pasta Box Task, participants with intact limbs maintained a 100% completion rate, while their success rate increased from <inline-formula> <tex-math>$86.1pm 5.8$ </tex-math></inline-formula>% to <inline-formula> <tex-math>$98.5pm 3.7$ </tex-math></inline-formula>%. Participants with upper limb loss began with completion rates between 0% and 40%, improving to 100%, with success rates between 90.9% and 100% by the final evaluation. Iteration completion times showed significant reduction across all participants (<inline-formula> <tex-math>$p$ </tex-math></inline-formula>-value < 0.05, linear mixed-effects model), with the median decreasing from 19.4 to 15.8 seconds. The Cup Transfer Task showed a similar trend of significant improvement, demonstrating that the acquired skills generalized to an untrained task. The system also demonstrated excellent usability, with an average System Usability Scale score of <inline-formula> <tex-math>$81.9pm 10.0$ </tex-math></inline-formula>. These findings indicate that our user-centered extended reality training tool holds promise for enhancing myoelectric control proficiency.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"1010-1020"},"PeriodicalIF":5.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146105317","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}
Robot-aided rehabilitation effectively supports treatment of upper-limb disorders and enhances outcomes when combined with traditional therapy. Artificial intelligence enables behavioral cloning of physiotherapists’ expertise to autonomously modulate robot assistance from real-time multimodal patient data. Therefore, this paper aims to propose and validate a behavioral cloning strategy, namely Physiotherapist-Supervised Parameter Adaptation (PSPA), for online tuning the robot assistance level replicating the physiotherapists’ decision-making.The experimental validation was conducted in a clinical setting involving ten post-surgical orthopedic patients who participated in a robot-aided rehabilitation session using the KUKA LWR 4 + robot. The sessions were supervised by physiotherapists who could adjust the level of robotic assistance as needed, thus labelling the collected patient multimodal data. The validation aimed at i) identifying the best-performing input modality, feature set, and classifier, and ii) comparing the capability of the approach in tailoring the assistance level with respect to the established performance-based (PB) one.Combining biomechanical and physiological features significantly improved the classification performance across all classifiers, with the highest performance observed for the Multilayer Perceptron on the present dataset. Moreover, using the optimized feature set, the proposed PSPA methodology achieved an even greater alignment with the physiotherapists’ decisions with respect to the PB approach ($Delta text {F1-score} = {15}.{40} pm {30}.{33}%$ , $rho = {0}.{56} pm {0}.{21}$ for PSPA, $rho = -{0}.{12} pm {0}.{43}$ for PB).
{"title":"Behavioral Cloning of Physiotherapists in Adapting Robot Control Parameter","authors":"Rita Molle;Christian Tamantini;Clemente Lauretti;Davide Sebastiani;Fabio Santacaterina;Marco Bravi;Federica Bressi;Sandra Miccinilli;Loredana Zollo","doi":"10.1109/TNSRE.2026.3659215","DOIUrl":"10.1109/TNSRE.2026.3659215","url":null,"abstract":"Robot-aided rehabilitation effectively supports treatment of upper-limb disorders and enhances outcomes when combined with traditional therapy. Artificial intelligence enables behavioral cloning of physiotherapists’ expertise to autonomously modulate robot assistance from real-time multimodal patient data. Therefore, this paper aims to propose and validate a behavioral cloning strategy, namely Physiotherapist-Supervised Parameter Adaptation (PSPA), for online tuning the robot assistance level replicating the physiotherapists’ decision-making.The experimental validation was conducted in a clinical setting involving ten post-surgical orthopedic patients who participated in a robot-aided rehabilitation session using the KUKA LWR 4 + robot. The sessions were supervised by physiotherapists who could adjust the level of robotic assistance as needed, thus labelling the collected patient multimodal data. The validation aimed at i) identifying the best-performing input modality, feature set, and classifier, and ii) comparing the capability of the approach in tailoring the assistance level with respect to the established performance-based (PB) one.Combining biomechanical and physiological features significantly improved the classification performance across all classifiers, with the highest performance observed for the Multilayer Perceptron on the present dataset. Moreover, using the optimized feature set, the proposed PSPA methodology achieved an even greater alignment with the physiotherapists’ decisions with respect to the PB approach (<inline-formula> <tex-math>$Delta text {F1-score} = {15}.{40} pm {30}.{33}%$ </tex-math></inline-formula>, <inline-formula> <tex-math>$rho = {0}.{56} pm {0}.{21}$ </tex-math></inline-formula> for PSPA, <inline-formula> <tex-math>$rho = -{0}.{12} pm {0}.{43}$ </tex-math></inline-formula> for PB).","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"966-977"},"PeriodicalIF":5.2,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367816","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085647","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 : 2026-01-29DOI: 10.1109/TNSRE.2026.3659248
Yitong Zhou;Jun Zhang;Yingying Li;Congzhi Tang;Xi Yang;Aiguo Song
Stroke survivors often suffer from severe discomfort caused by persistent hand edema, including hand pain, restricted knuckle mobility, and negative emotions, which significantly hinders normal rehabilitation progress. Whether physical or psychological in impact, the prevention and treatment of hand edema warrant significant attention throughout all stages of stroke rehabilitation. However, traditional rehabilitation therapies generally neglect edema management, focusing primarily on motor function recovery. Consequently, this study introduces the TESSR (ThErmal Stimulation device for Stroke Rehabilitation), a novel apparatus designed to promote hand rehabilitation for stroke survivors. TESSR is equipped with a flexible stimulation array comprising 50 independently controlled stimulation units, each capable of rapid heating and cooling transitions as well as delivering sustained and stable thermal outputs. This alternating thermal stimulation (TS) is designed to alleviate edema and activate the sensory cortex, thereby facilitating neuroplasticity. A controlled clinical study was conducted with 12 stroke survivors to assess the effectiveness of the TESSR device. The participants were divided into two groups: one receiving conventional rehabilitation (CR group) and the other receiving supplementary TS conducted by the TESSR device (TS group). Following a two-week intervention, the TS group exhibited a notable reduction in hand edema, with a mean decrease of 38.6%, compared to the minimal improvement observed in the CR group. Furthermore, participants in the TS group demonstrated improved hand function, including enhanced sensory perception and diminished pain. The findings demonstrate the potential of TESSR as a comprehensive and effective tool for hand rehabilitation.
脑卒中幸存者通常会因手部持续水肿而遭受严重不适，包括手部疼痛、关节活动受限以及负面情绪，这严重阻碍了正常的康复进展。无论是生理上还是心理上的影响，手部水肿的预防和治疗在卒中康复的各个阶段都值得重视。然而，传统的康复疗法通常忽视水肿的治疗，主要关注运动功能的恢复。因此，本研究介绍了TESSR (ThErmal Stimulation device for Stroke Rehabilitation)，这是一种旨在促进中风幸存者手部康复的新型设备。TESSR配备了一个灵活的增产阵列，包括50个独立控制的增产单元，每个单元都能够快速加热和冷却转换，并提供持续稳定的热输出。这种交替热刺激（TS）旨在减轻水肿，激活感觉皮层，从而促进神经可塑性。对12名中风幸存者进行了一项对照临床研究，以评估TESSR装置的有效性。参与者分为两组：一组接受常规康复治疗（CR组），另一组接受TESSR装置辅助TS治疗（TS组）。经过两周的干预，TS组手部水肿明显减少，平均减少38.6%，而CR组的改善最小。此外，TS组的参与者表现出了手功能的改善，包括感觉知觉增强和疼痛减轻。研究结果显示TESSR作为一种全面有效的手部康复工具的潜力。
{"title":"Functional Assessment of a Flexible Thermal Stimulation Device for Hand Edema Elimination in Stroke Survivors","authors":"Yitong Zhou;Jun Zhang;Yingying Li;Congzhi Tang;Xi Yang;Aiguo Song","doi":"10.1109/TNSRE.2026.3659248","DOIUrl":"10.1109/TNSRE.2026.3659248","url":null,"abstract":"Stroke survivors often suffer from severe discomfort caused by persistent hand edema, including hand pain, restricted knuckle mobility, and negative emotions, which significantly hinders normal rehabilitation progress. Whether physical or psychological in impact, the prevention and treatment of hand edema warrant significant attention throughout all stages of stroke rehabilitation. However, traditional rehabilitation therapies generally neglect edema management, focusing primarily on motor function recovery. Consequently, this study introduces the TESSR (ThErmal Stimulation device for Stroke Rehabilitation), a novel apparatus designed to promote hand rehabilitation for stroke survivors. TESSR is equipped with a flexible stimulation array comprising 50 independently controlled stimulation units, each capable of rapid heating and cooling transitions as well as delivering sustained and stable thermal outputs. This alternating thermal stimulation (TS) is designed to alleviate edema and activate the sensory cortex, thereby facilitating neuroplasticity. A controlled clinical study was conducted with 12 stroke survivors to assess the effectiveness of the TESSR device. The participants were divided into two groups: one receiving conventional rehabilitation (CR group) and the other receiving supplementary TS conducted by the TESSR device (TS group). Following a two-week intervention, the TS group exhibited a notable reduction in hand edema, with a mean decrease of 38.6%, compared to the minimal improvement observed in the CR group. Furthermore, participants in the TS group demonstrated improved hand function, including enhanced sensory perception and diminished pain. The findings demonstrate the potential of TESSR as a comprehensive and effective tool for hand rehabilitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"941-951"},"PeriodicalIF":5.2,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367718","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085596","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 : 2026-01-28DOI: 10.1109/TNSRE.2026.3658740
Siwen Wei;Yongzhi Huang;Haiqing Yu;Jiayuan Meng;Rui Xu;Tzyy-Ping Jung;Minpeng Xu;Dong Ming
Real-time monitoring of sustained attention fluctuations during continuous complex tasks is vital for enhancing task performance and preventing accidents. Attention modulates neurons in the visual cortex in various ways to improve the visual sensitivity at an attended location. EEG-based brain-computer interfaces (BCIs) offer one of the most effective approaches for monitoring the state of human individuals. Whether transient responses evoked by brief stimuli, steady-state responses elicited by prolonged stimuli, or spontaneous neural oscillations, researchers can extract recognized electrophysiological features that reflect attention levels. However, unimodal features face inherent limitations, such as the low signal-to-noise ratio of transient responses and susceptibility of spontaneous rhythms to electrophysiological interference. Nevertheless, few studies have explored multimodal feature fusion for attention state monitoring. Here, we developed an innovative continuous go/no-go task to concurrently evoke both event-related potential (ERP) and steady-state visual evoked potential (SSVEP), while modulating spontaneous oscillatory activities through attentional engagement. To maximize the attentional modulation effect, we integrated the contrast-response functions of the modulation effect of attention on SSVEP and implemented 12 stimulus contrast levels to identify optimal visual stimulation intensity. Results from 25 subjects demonstrated that the decline in sustained attention during a continuous task was predictable before behavioral mistakes. Classification performance peaked at 31.60% stimulus contrast condition using the fused features combining spontaneous beta-band oscillations and SSVEP responses (average: 74.48%; best: 90.83%). These findings advance the development of more robust real-time attention monitoring systems based on BCI technology.
{"title":"Predicting Attention Decline: An Integrated Beta-Band and SSVEP Approach for Visual Brain–Computer Interfaces","authors":"Siwen Wei;Yongzhi Huang;Haiqing Yu;Jiayuan Meng;Rui Xu;Tzyy-Ping Jung;Minpeng Xu;Dong Ming","doi":"10.1109/TNSRE.2026.3658740","DOIUrl":"https://doi.org/10.1109/TNSRE.2026.3658740","url":null,"abstract":"Real-time monitoring of sustained attention fluctuations during continuous complex tasks is vital for enhancing task performance and preventing accidents. Attention modulates neurons in the visual cortex in various ways to improve the visual sensitivity at an attended location. EEG-based brain-computer interfaces (BCIs) offer one of the most effective approaches for monitoring the state of human individuals. Whether transient responses evoked by brief stimuli, steady-state responses elicited by prolonged stimuli, or spontaneous neural oscillations, researchers can extract recognized electrophysiological features that reflect attention levels. However, unimodal features face inherent limitations, such as the low signal-to-noise ratio of transient responses and susceptibility of spontaneous rhythms to electrophysiological interference. Nevertheless, few studies have explored multimodal feature fusion for attention state monitoring. Here, we developed an innovative continuous go/no-go task to concurrently evoke both event-related potential (ERP) and steady-state visual evoked potential (SSVEP), while modulating spontaneous oscillatory activities through attentional engagement. To maximize the attentional modulation effect, we integrated the contrast-response functions of the modulation effect of attention on SSVEP and implemented 12 stimulus contrast levels to identify optimal visual stimulation intensity. Results from 25 subjects demonstrated that the decline in sustained attention during a continuous task was predictable before behavioral mistakes. Classification performance peaked at 31.60% stimulus contrast condition using the fused features combining spontaneous beta-band oscillations and SSVEP responses (average: 74.48%; best: 90.83%). These findings advance the development of more robust real-time attention monitoring systems based on BCI technology.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"997-1009"},"PeriodicalIF":5.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175776","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}
Evoked compound action potential (ECAP) is extensively used as biomarker for neural activity in closed-loop spinal cord stimulation (SCS). However, accurate monitoring of neural activity is challenged by the low signal-to-noise ratio of ECAP recording, primarily due to the interference from stimulation-induced artifacts. This study proposes biphasic electrostimulation artifact model (BEAM) for ECAP extraction and assesses its efficacy using an enhanced quantification approach. The BEAM model is built based on the principle of additivity and incorporates boundary conditions that account for the equivalent electrical environment and temporal relationships. A bilinear growth curve model is introduced to calculate the E-score, enabling a comparison of ECAP extraction results with other methods. The proposed approach is extensively validated using clinical data from nine-month SCS therapy. BEAM method achieves the highest E-scores and reduces stimulation artifacts clinically without distorting information related to neural activity. BEAM accurately characterizes the morphology of artifacts generated by stimulation across various neurostimulation scenarios. Our model may lead to unique interpretation of neural activation in ECAP signals, evoked by electrical stimulation especially in closed-loop controlling strategies that aim to maintain consistent neural excitation.
{"title":"Biphasic Electrostimulation Artifact Model for ECAP Extraction in Spinal Cord Stimulation","authors":"Haochen Zhang;Xuesong Luo;Bozhi Ma;Luming Li;Xulan Yin;Zhongwei Luo;Boyang Zhang;Xi Zhang;Hongda Li;Qingyu Yao;Junhong Lim","doi":"10.1109/TNSRE.2026.3658636","DOIUrl":"10.1109/TNSRE.2026.3658636","url":null,"abstract":"Evoked compound action potential (ECAP) is extensively used as biomarker for neural activity in closed-loop spinal cord stimulation (SCS). However, accurate monitoring of neural activity is challenged by the low signal-to-noise ratio of ECAP recording, primarily due to the interference from stimulation-induced artifacts. This study proposes biphasic electrostimulation artifact model (BEAM) for ECAP extraction and assesses its efficacy using an enhanced quantification approach. The BEAM model is built based on the principle of additivity and incorporates boundary conditions that account for the equivalent electrical environment and temporal relationships. A bilinear growth curve model is introduced to calculate the E-score, enabling a comparison of ECAP extraction results with other methods. The proposed approach is extensively validated using clinical data from nine-month SCS therapy. BEAM method achieves the highest E-scores and reduces stimulation artifacts clinically without distorting information related to neural activity. BEAM accurately characterizes the morphology of artifacts generated by stimulation across various neurostimulation scenarios. Our model may lead to unique interpretation of neural activation in ECAP signals, evoked by electrical stimulation especially in closed-loop controlling strategies that aim to maintain consistent neural excitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"880-893"},"PeriodicalIF":5.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131818","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 : 2026-01-28DOI: 10.1109/TNSRE.2026.3658597
Ali KhalilianMotamed Bonab;Volkan Patoglu
Robotic exoskeletons can enhance human locomotion by reducing its metabolic cost. Designing effective wearable assistive devices requires a systematic approach that accounts for the influence of device kinematics/dynamics and effects of assistance torques on human performance. While comprehensive human-subject experiments to evaluate multiple designs are often impractical, musculoskeletal simulations can serve as a powerful tool for optimizing exoskeleton designs and their corresponding assistance strategies. This paper presents a musculoskeletal simulation-based multi-criteria design optimization framework to systematically evaluate and compare various exoskeleton configurations under realistic physical constraints. In this study, the multi-criteria optimization framework is used to characterize the trade-off between metabolic efficiency and power use of mono- and bi-articular lower-limb exoskeleton configurations under optimal assistance torques. The multi-criteria optimization results provide a fair basis for rigorous comparison among various exoskeleton configurations and their corresponding optimal assistance torque profiles, considering realistic actuator saturation limits and the detrimental effects of exoskeleton reflected inertia on metabolic consumption. The results offer valuable insights to guide assistive exoskeleton designs under real-world constraints.
{"title":"Musculoskeletal Simulation-Based Multi-Criteria Optimization Framework for Exoskeleton Design","authors":"Ali KhalilianMotamed Bonab;Volkan Patoglu","doi":"10.1109/TNSRE.2026.3658597","DOIUrl":"10.1109/TNSRE.2026.3658597","url":null,"abstract":"Robotic exoskeletons can enhance human locomotion by reducing its metabolic cost. Designing effective wearable assistive devices requires a systematic approach that accounts for the influence of device kinematics/dynamics and effects of assistance torques on human performance. While comprehensive human-subject experiments to evaluate multiple designs are often impractical, musculoskeletal simulations can serve as a powerful tool for optimizing exoskeleton designs and their corresponding assistance strategies. This paper presents a musculoskeletal simulation-based multi-criteria design optimization framework to systematically evaluate and compare various exoskeleton configurations under realistic physical constraints. In this study, the multi-criteria optimization framework is used to characterize the trade-off between metabolic efficiency and power use of mono- and bi-articular lower-limb exoskeleton configurations under optimal assistance torques. The multi-criteria optimization results provide a fair basis for rigorous comparison among various exoskeleton configurations and their corresponding optimal assistance torque profiles, considering realistic actuator saturation limits and the detrimental effects of exoskeleton reflected inertia on metabolic consumption. The results offer valuable insights to guide assistive exoskeleton designs under real-world constraints.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"868-879"},"PeriodicalIF":5.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131870","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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