Pub Date : 2025-12-24DOI: 10.1109/TNSRE.2025.3648325
Jiawei Chen;Vedika P. Basavatia;Kimberly T. Kwei;Sunil K. Agrawal
Along with motor dysfunction, people with Parkinson’s Disease (PD) often develop cognitive dysfunction, linked to the gait abnormality - freezing of gait (FOG). Spatial navigation in Virtual Reality Floor Mazes (VR-FM) provides a unique framework for studying the effects of cognitive load on walking, with the ability to manipulate the complexity of the cognitive load. In addition, mazes include turns which simulate indoor home environments that people with PD frequently traverse in their daily life. This study is aimed to examine the effects of increasing cognitive load, applied with VR-FM, on motor performance in PD subjects with and without FOG. This is particularly important in understanding Parkinson’s Disease, as cognitive decline is a strong contributor to morbidity and mortality as the disease progresses and may be a contributing factor to FOG. Fourteen subjects with PD, including eight who exhibited FOG, completed VR-FM under three conditions: 1) control mazes where the path to the goal is displayed; 2) easy mazes with two or less decision points; and 3) hard mazes, with more than two decision points. In comparison to non-freezers, freezers took fewer spin steps, shorter and slower strides, and reduced medial-lateral sway of the center of mass. These deficits became worse with maze difficulty, accompanied by further degradation in balance measured by margin of stability. Increased cognitive load imposed by the VR-FM led to gait deterioration and a prioritization for balance in both freezers and non-freezers. This supports the use of VR-FM as a tool to investigate motor-cognitive interplay in PD. Freezers exhibit more pronounced deterioration in gait and balance in VR-FM. Hence, VR-FM can serve as a potential tool to characterize and identify freezers.
{"title":"Navigation in Virtual Reality Floor Mazes: Added Cognitive Demand and Its Effects on Gait and Balance in Parkinson’s Disease","authors":"Jiawei Chen;Vedika P. Basavatia;Kimberly T. Kwei;Sunil K. Agrawal","doi":"10.1109/TNSRE.2025.3648325","DOIUrl":"10.1109/TNSRE.2025.3648325","url":null,"abstract":"Along with motor dysfunction, people with Parkinson’s Disease (PD) often develop cognitive dysfunction, linked to the gait abnormality - freezing of gait (FOG). Spatial navigation in Virtual Reality Floor Mazes (VR-FM) provides a unique framework for studying the effects of cognitive load on walking, with the ability to manipulate the complexity of the cognitive load. In addition, mazes include turns which simulate indoor home environments that people with PD frequently traverse in their daily life. This study is aimed to examine the effects of increasing cognitive load, applied with VR-FM, on motor performance in PD subjects with and without FOG. This is particularly important in understanding Parkinson’s Disease, as cognitive decline is a strong contributor to morbidity and mortality as the disease progresses and may be a contributing factor to FOG. Fourteen subjects with PD, including eight who exhibited FOG, completed VR-FM under three conditions: 1) control mazes where the path to the goal is displayed; 2) easy mazes with two or less decision points; and 3) hard mazes, with more than two decision points. In comparison to non-freezers, freezers took fewer spin steps, shorter and slower strides, and reduced medial-lateral sway of the center of mass. These deficits became worse with maze difficulty, accompanied by further degradation in balance measured by margin of stability. Increased cognitive load imposed by the VR-FM led to gait deterioration and a prioritization for balance in both freezers and non-freezers. This supports the use of VR-FM as a tool to investigate motor-cognitive interplay in PD. Freezers exhibit more pronounced deterioration in gait and balance in VR-FM. Hence, VR-FM can serve as a potential tool to characterize and identify freezers.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"382-393"},"PeriodicalIF":5.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11314791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145827672","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 : 2025-12-24DOI: 10.1109/TNSRE.2025.3647860
You Li;Ruxin He;Qinlian Yang;Manxu Zheng;Junhui Wang;Peng Fang;Rong Song
Functional electrical stimulation (FES) is widely used as an assistive method for foot drop correction. However, existing FES controllers often induce unnatural muscle activation through rigid stimulation or lack adaptability to dynamic changes in gait performance. This study proposed an FES profile optimization method to achieve natural and adaptive stimulation in order to compensate for disturbances through the following two steps: 1) a Hammerstein-structured ankle joint dynamic model was developed to establish the relationship between the FES profiles and musculoskeletal dynamic response and 2) utilizing this model, a Norm-Optimal Iterative Learning Control (NOILC)-based FES controller was designed, and an optimal control learning gain was determined to adjust FES profiles for automatic correction of trajectory tracking errors. The proposed controller’s performance was evaluated using kinematic data from five stroke patients and compared with that under two conditions: no FES and fixed-profile FES. The experimental results showed that the proposed controller could result in ankle dorsiflexion motions closer to the reference trajectory, and the maximum dorsiflexion angle during the swing phase was significantly improved by 3.92° relative to the no FES condition and by 2.06° relative to the fixed-profile FES condition. This study indicates that the proposed controller can provide natural and adaptive FES profiles, enhancing gait performance for stroke patients and showing promising potential for clinical application.
{"title":"Adaptive Adjustment of FES Profiles Using Norm-Optimal Iterative Learning Control for Foot Drop Correction","authors":"You Li;Ruxin He;Qinlian Yang;Manxu Zheng;Junhui Wang;Peng Fang;Rong Song","doi":"10.1109/TNSRE.2025.3647860","DOIUrl":"10.1109/TNSRE.2025.3647860","url":null,"abstract":"Functional electrical stimulation (FES) is widely used as an assistive method for foot drop correction. However, existing FES controllers often induce unnatural muscle activation through rigid stimulation or lack adaptability to dynamic changes in gait performance. This study proposed an FES profile optimization method to achieve natural and adaptive stimulation in order to compensate for disturbances through the following two steps: 1) a Hammerstein-structured ankle joint dynamic model was developed to establish the relationship between the FES profiles and musculoskeletal dynamic response and 2) utilizing this model, a Norm-Optimal Iterative Learning Control (NOILC)-based FES controller was designed, and an optimal control learning gain was determined to adjust FES profiles for automatic correction of trajectory tracking errors. The proposed controller’s performance was evaluated using kinematic data from five stroke patients and compared with that under two conditions: no FES and fixed-profile FES. The experimental results showed that the proposed controller could result in ankle dorsiflexion motions closer to the reference trajectory, and the maximum dorsiflexion angle during the swing phase was significantly improved by 3.92° relative to the no FES condition and by 2.06° relative to the fixed-profile FES condition. This study indicates that the proposed controller can provide natural and adaptive FES profiles, enhancing gait performance for stroke patients and showing promising potential for clinical application.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"478-489"},"PeriodicalIF":5.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11314755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145827606","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 : 2025-12-23DOI: 10.1109/TNSRE.2025.3647591
Isirame Omofuma;Ilaria Fagioli;Sunil Agrawal
Coordinated control of the trunk and pelvis is critical for performing functional upper-body movements, particularly during standing. Deficits in trunk–pelvis coordination are common in populations with neurological or musculoskeletal impairments, contributing to poor balance and limited functional mobility. In this proof-of-concept study, we investigated training strategies in healthy participants to establish a baseline for future rehabilitation applications. Twenty-four individuals were assigned to one of three groups: (i) control, without assistance (Ctrl), (ii) robotic assistance at the trunk, specifically at the thorax (T), and (iii) robotic assistance applied concurrently at the thorax and pelvis (T-P). Training was delivered using the Robotic Upright Stand Trainer (RobUST), which provides assist-as-needed forces based on deviations from target trajectories and normative thorax–pelvis coordination patterns. Participants were trained to perform elliptical thorax movements while standing, a task with progressively increasing postural demands. Results showed that T-P assistance enabled participants to achieve larger ellipse sizes during training compared to T assistance, suggesting that pelvic support facilitated greater exploration of range of motion. Post-training, ellipse tracing accuracy improved in all groups, but only the T-P and Ctrl groups demonstrated significant gains in movement smoothness. Learning-curve analysis further revealed that while T-P participants required a longer acclimatization period, they ultimately achieved higher combined learning metrics than the T group. These findings highlight the potential of trunk–pelvis coordinated assistance to promote greater improvements in postural control than assistance limited to the trunk. The results provide a foundation for developing trunk–pelvis interventions aimed at improving postural control in clinical populations.
{"title":"Postural Control Training With Forces Applied on the Trunk and Pelvis Using a Robotic Upright Stand Trainer (RobUST)","authors":"Isirame Omofuma;Ilaria Fagioli;Sunil Agrawal","doi":"10.1109/TNSRE.2025.3647591","DOIUrl":"10.1109/TNSRE.2025.3647591","url":null,"abstract":"Coordinated control of the trunk and pelvis is critical for performing functional upper-body movements, particularly during standing. Deficits in trunk–pelvis coordination are common in populations with neurological or musculoskeletal impairments, contributing to poor balance and limited functional mobility. In this proof-of-concept study, we investigated training strategies in healthy participants to establish a baseline for future rehabilitation applications. Twenty-four individuals were assigned to one of three groups: (i) control, without assistance (Ctrl), (ii) robotic assistance at the trunk, specifically at the thorax (T), and (iii) robotic assistance applied concurrently at the thorax and pelvis (T-P). Training was delivered using the Robotic Upright Stand Trainer (RobUST), which provides assist-as-needed forces based on deviations from target trajectories and normative thorax–pelvis coordination patterns. Participants were trained to perform elliptical thorax movements while standing, a task with progressively increasing postural demands. Results showed that T-P assistance enabled participants to achieve larger ellipse sizes during training compared to T assistance, suggesting that pelvic support facilitated greater exploration of range of motion. Post-training, ellipse tracing accuracy improved in all groups, but only the T-P and Ctrl groups demonstrated significant gains in movement smoothness. Learning-curve analysis further revealed that while T-P participants required a longer acclimatization period, they ultimately achieved higher combined learning metrics than the T group. These findings highlight the potential of trunk–pelvis coordinated assistance to promote greater improvements in postural control than assistance limited to the trunk. The results provide a foundation for developing trunk–pelvis interventions aimed at improving postural control in clinical populations.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"355-365"},"PeriodicalIF":5.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11313650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145819102","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 : 2025-12-22DOI: 10.1109/TNSRE.2025.3647101
Xi Fu;Rui Liu;Aung Aung Phyo Wai;Hannah Pulferer;Neethu Robinson;Gernot R. Müller-Putz;Cuntai Guan
Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feature extraction, and the scarcity of high-quality gait EEG datasets. To address these issues, we propose EEG2GAIT, a novel hierarchical graph-based model that captures multi-level spatial embeddings of EEG channels using a Hierarchical Graph Convolutional Network (GCN) Pyramid. To further improve decoding performance, we introduce a Hybrid Temporal-Spectral Reward (HTSR) loss function, which integrates time-domain, frequency-domain, and reward-based loss components. In addition, we contribute a new Gait-EEG Dataset (GED), consisting of synchronized EEG and lower-limb joint angle data collected from 50 participants across two laboratory visits. Extensive experiments demonstrate that EEG2GAIT with HTSR achieves superior performance on the GED dataset, reaching a Pearson correlation coefficient ($r$ ) of 0.959, a coefficient of determination (${R}^{{2}}$ ) of 0.914, and a Mean Absolute Error (MAE) of 0.193. On the MoBI dataset, EEG2GAIT likewise consistently outperforms existing methods, achieving an $r$ of 0.779, an ${R}^{{2}}$ of 0.597, and an MAE of 4.384. Statistical analyses confirm that these improvements are significant compared to all prior models. Ablation studies further validate the contributions of the hierarchical GCN modules and the proposed HTSR loss, while saliency analysis highlights the involvement of motor-related brain regions in decoding tasks. Collectively, these findings underscore EEG2GAIT’s potential for advancing brain-computer interface applications, particularly in lower-limb rehabilitation and assistive technologies.
{"title":"EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-Based Gait Decoding","authors":"Xi Fu;Rui Liu;Aung Aung Phyo Wai;Hannah Pulferer;Neethu Robinson;Gernot R. Müller-Putz;Cuntai Guan","doi":"10.1109/TNSRE.2025.3647101","DOIUrl":"10.1109/TNSRE.2025.3647101","url":null,"abstract":"Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feature extraction, and the scarcity of high-quality gait EEG datasets. To address these issues, we propose EEG2GAIT, a novel hierarchical graph-based model that captures multi-level spatial embeddings of EEG channels using a Hierarchical Graph Convolutional Network (GCN) Pyramid. To further improve decoding performance, we introduce a Hybrid Temporal-Spectral Reward (HTSR) loss function, which integrates time-domain, frequency-domain, and reward-based loss components. In addition, we contribute a new Gait-EEG Dataset (GED), consisting of synchronized EEG and lower-limb joint angle data collected from 50 participants across two laboratory visits. Extensive experiments demonstrate that EEG2GAIT with HTSR achieves superior performance on the GED dataset, reaching a Pearson correlation coefficient (<inline-formula> <tex-math>$r$ </tex-math></inline-formula>) of 0.959, a coefficient of determination (<inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula>) of 0.914, and a Mean Absolute Error (MAE) of 0.193. On the MoBI dataset, EEG2GAIT likewise consistently outperforms existing methods, achieving an <inline-formula> <tex-math>$r$ </tex-math></inline-formula> of 0.779, an <inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula> of 0.597, and an MAE of 4.384. Statistical analyses confirm that these improvements are significant compared to all prior models. Ablation studies further validate the contributions of the hierarchical GCN modules and the proposed HTSR loss, while saliency analysis highlights the involvement of motor-related brain regions in decoding tasks. Collectively, these findings underscore EEG2GAIT’s potential for advancing brain-computer interface applications, particularly in lower-limb rehabilitation and assistive technologies.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"441-454"},"PeriodicalIF":5.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11311528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809493","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 : 2025-12-22DOI: 10.1109/TNSRE.2025.3646689
Shahrzad Hedayati;Hasan Abbasi Nozari;Seyed Jalil Sadati Rostami;Sajad Shafiee;Seyyed Ali Zendehbad
Deep brain stimulation (DBS) is an advanced clinical treatment for suppressing tremors in Parkinsonian patients. However, traditional open-loop DBS systems remain unable to adapt to patient-specific neural dynamics, often leading to suboptimal results. To address these limitations, this paper proposes a novel closed-loop DBS scheme based on a data-driven model-free adaptive control (MFAC) strategy, designed to effectively suppress pathological tremors hindering overstimulation and providing less power consumption. Using the basal ganglia (BG) system dynamics which is assumed to be completely unknown, the proposed method overcomes the incomplete regional contraction mapping or inaccurate neural dynamics representations, making it a viable option for patient-specific adaptation. The online control strategy continuously adjusts based on real-time data, using an unknown BG model that is merely employed to generate input-output data for simultaneous regulation of the subthalamic nucleus (STN) and globus pallidus internus (GPi) regions. Three linearization techniques (compact-form, partial-form, and full-form dynamic linearization) are utilized to enhance performance and suppress pathological tremor and bring much flexibility to controller design. Performance metrics, including Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Integral Time Squared Error (ITSE), demonstrate a detailed comparison to check the tracking accuracy and tremor suppression based on the error signal. The controller’s robustness against inter- and intra-patient variations is evaluated through Monte-Carlo (MC) simulations, providing a reliable in-vitro alternative to real-world clinical trials. In addition, a Hardware-In-the-Loop (HIL) setup has been devised using an Arduino microcontroller to validate the proposed individualized closed-loop DBS performance in a more realistic environment, validating the adaptation, and accounting for noise and time delay in real-world clinical situations. The findings indicate that the proposed novel adaptive deep brain stimulator can significantly improve the quality of life for Parkinsonian patients by effectively suppressing the disease-related tremors.
{"title":"Real-Time Model-Free Adaptive Dual Control in Closed-Loop Deep Brain Stimulation: A Path to Individualized Parkinson’s Treatment","authors":"Shahrzad Hedayati;Hasan Abbasi Nozari;Seyed Jalil Sadati Rostami;Sajad Shafiee;Seyyed Ali Zendehbad","doi":"10.1109/TNSRE.2025.3646689","DOIUrl":"10.1109/TNSRE.2025.3646689","url":null,"abstract":"Deep brain stimulation (DBS) is an advanced clinical treatment for suppressing tremors in Parkinsonian patients. However, traditional open-loop DBS systems remain unable to adapt to patient-specific neural dynamics, often leading to suboptimal results. To address these limitations, this paper proposes a novel closed-loop DBS scheme based on a data-driven model-free adaptive control (MFAC) strategy, designed to effectively suppress pathological tremors hindering overstimulation and providing less power consumption. Using the basal ganglia (BG) system dynamics which is assumed to be completely unknown, the proposed method overcomes the incomplete regional contraction mapping or inaccurate neural dynamics representations, making it a viable option for patient-specific adaptation. The online control strategy continuously adjusts based on real-time data, using an unknown BG model that is merely employed to generate input-output data for simultaneous regulation of the subthalamic nucleus (STN) and globus pallidus internus (GPi) regions. Three linearization techniques (compact-form, partial-form, and full-form dynamic linearization) are utilized to enhance performance and suppress pathological tremor and bring much flexibility to controller design. Performance metrics, including Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Integral Time Squared Error (ITSE), demonstrate a detailed comparison to check the tracking accuracy and tremor suppression based on the error signal. The controller’s robustness against inter- and intra-patient variations is evaluated through Monte-Carlo (MC) simulations, providing a reliable in-vitro alternative to real-world clinical trials. In addition, a Hardware-In-the-Loop (HIL) setup has been devised using an Arduino microcontroller to validate the proposed individualized closed-loop DBS performance in a more realistic environment, validating the adaptation, and accounting for noise and time delay in real-world clinical situations. The findings indicate that the proposed novel adaptive deep brain stimulator can significantly improve the quality of life for Parkinsonian patients by effectively suppressing the disease-related tremors.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"372-381"},"PeriodicalIF":5.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11311137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809564","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}
Electromagnetic stimulation is pivotal in diagnosing and treating neurological and psychiatric disorders. However, achieving effective stimulation hinges significantly on the precision of the stimulation focus. Presently, the focal area of electromagnetic stimulation remains at the centimeter scale, which poses substantial challenges when targeting fine brain regions. To address this limitation, this study introduces a novel method that leverages liquid metal to enhance the focusing ability of electromagnetic stimulation. By utilizing liquid metal to concentrate the induced electric field generated by electromagnetic excitation, we can achieve highly focused stimulation. This innovative approach has been preliminarily validated through both finite element simulations and experimental studies, demonstrating the liquid metal’s capacity to significantly enhance the focusing of the induced electric field. The results indicate that liquid metal can reduce the focal size of electromagnetic stimulation to the millimeter scale, with peak induced field strength achieving up to approximately 300% enhancement, realizing a millimeter-scale focal area. Furthermore, it was explored that controlling the spatial distribution of liquid metal could achieve even higher electric field intensity. A measurement platform was constructed to validate the simulation results in gel models, with additional verification conducted through simulations in a realistic human head model based on MRI data. In summary, the liquid metal–based focusing stimulation method proposed in this study represents a significant advancement in improving the precision of electromagnetic stimulation. This innovation holds great promise for advancing the field of precise electromagnetic stimulation, offering a powerful new tool for both research and clinical applications.
{"title":"Research on Focusing Effect of Electromagnetic Stimulation Based on Liquid Metal","authors":"Yuheng Wang;Junjie Lin;Yi Wu;Ren Ma;Jingna Jin;Tao Yin;Zhipeng Liu;Shunqi Zhang","doi":"10.1109/TNSRE.2025.3646866","DOIUrl":"10.1109/TNSRE.2025.3646866","url":null,"abstract":"Electromagnetic stimulation is pivotal in diagnosing and treating neurological and psychiatric disorders. However, achieving effective stimulation hinges significantly on the precision of the stimulation focus. Presently, the focal area of electromagnetic stimulation remains at the centimeter scale, which poses substantial challenges when targeting fine brain regions. To address this limitation, this study introduces a novel method that leverages liquid metal to enhance the focusing ability of electromagnetic stimulation. By utilizing liquid metal to concentrate the induced electric field generated by electromagnetic excitation, we can achieve highly focused stimulation. This innovative approach has been preliminarily validated through both finite element simulations and experimental studies, demonstrating the liquid metal’s capacity to significantly enhance the focusing of the induced electric field. The results indicate that liquid metal can reduce the focal size of electromagnetic stimulation to the millimeter scale, with peak induced field strength achieving up to approximately 300% enhancement, realizing a millimeter-scale focal area. Furthermore, it was explored that controlling the spatial distribution of liquid metal could achieve even higher electric field intensity. A measurement platform was constructed to validate the simulation results in gel models, with additional verification conducted through simulations in a realistic human head model based on MRI data. In summary, the liquid metal–based focusing stimulation method proposed in this study represents a significant advancement in improving the precision of electromagnetic stimulation. This innovation holds great promise for advancing the field of precise electromagnetic stimulation, offering a powerful new tool for both research and clinical applications.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"426-434"},"PeriodicalIF":5.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11311120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809747","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 : 2025-12-22DOI: 10.1109/TNSRE.2025.3647266
Kimia Khoshnami;Edoardo Battaglia;Mark Bromberg;Haohan Zhang
Neck muscle weakness causes the inability to raise and move the head, leading to fatigue, neck pain, and a “head-on-chest” posture (dropped head syndrome) in severe cases, which significantly affects quality of life. Static neck collars are the current standard of care. However, these collars are passive, which cannot restore the head-neck movement necessary for daily tasks. Emerging robotic devices like powered neck exoskeletons were developed to enable head-neck movements. Previous laboratory tests showed improved patients’ ability to follow prescribed trajectories; however, the ability to assist with daily tasks of such a robotic device remains unknown. In this paper, the functional range of motion allowed by a state-of-the-art powered neck exoskeleton was compared to a clinic-standard static neck collar in healthy adults performing simulated daily tasks wearing these devices. Results showed a greater head range of motion and consequently less compensatory torso movements while wearing the neck exoskeleton in its transparent mode. Participants rated the neck exoskeleton more favorably than the static collar in terms of comfort and ability to perform the tasks. Results also revealed the range of motion limits of the current neck exoskeleton for these daily tasks. These results provided justifications for using neck exoskeletons to restore daily functions and offered critical insights into future refinement of this technology to enable head range of motion for critical daily activities.
{"title":"Evaluating Range of Motion of Two Prominent Neck Support Devices for Daily Activities","authors":"Kimia Khoshnami;Edoardo Battaglia;Mark Bromberg;Haohan Zhang","doi":"10.1109/TNSRE.2025.3647266","DOIUrl":"10.1109/TNSRE.2025.3647266","url":null,"abstract":"Neck muscle weakness causes the inability to raise and move the head, leading to fatigue, neck pain, and a “head-on-chest” posture (dropped head syndrome) in severe cases, which significantly affects quality of life. Static neck collars are the current standard of care. However, these collars are passive, which cannot restore the head-neck movement necessary for daily tasks. Emerging robotic devices like powered neck exoskeletons were developed to enable head-neck movements. Previous laboratory tests showed improved patients’ ability to follow prescribed trajectories; however, the ability to assist with daily tasks of such a robotic device remains unknown. In this paper, the functional range of motion allowed by a state-of-the-art powered neck exoskeleton was compared to a clinic-standard static neck collar in healthy adults performing simulated daily tasks wearing these devices. Results showed a greater head range of motion and consequently less compensatory torso movements while wearing the neck exoskeleton in its transparent mode. Participants rated the neck exoskeleton more favorably than the static collar in terms of comfort and ability to perform the tasks. Results also revealed the range of motion limits of the current neck exoskeleton for these daily tasks. These results provided justifications for using neck exoskeletons to restore daily functions and offered critical insights into future refinement of this technology to enable head range of motion for critical daily activities.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"345-354"},"PeriodicalIF":5.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11311498","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809468","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 : 2025-12-19DOI: 10.1109/TNSRE.2025.3644207
{"title":"IEEE Transactions on Neural Systems and Rehabilitation","authors":"","doi":"10.1109/TNSRE.2025.3644207","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3644207","url":null,"abstract":"","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"C3-C3"},"PeriodicalIF":5.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11306210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778121","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 : 2025-12-19DOI: 10.1109/TNSRE.2025.3646472
Laura A. Miller;Kristi L. Turner;Kevin Brenner;Levi J. Hargrove
This study investigates functional performance using a two-degree-of-freedom (2DOF) prosthetic wrist compared to a single-degree-of-freedom (1DOF) wrist in individuals with transradial (below-elbow) amputation. Five participants were fitted with a custom-designed 2DOF prosthetic wrist system integrated with an Ottobock Transcarpal hand and operated via a pattern recognition-based myoelectric control interface. Participants completed two test conditions: one using wrist rotation alone (1DOF, NoWF), and another using wrist rotation combined with wrist flexion and extension (2DOF, WF). A battery of standardized functional assessments was used to evaluate performance in both conditions, including the Southampton Hand Assessment Procedure (SHAP), Box and Blocks Test (BBT), Jebsen-Taylor Hand Function Test (JTHFT), Activity Measure for Upper Limb Amputees (AM-ULA), Clothespin Relocation Task (CRT), and the Assessment of Capacity for Myoelectric Control (ACMC). Across all outcome measures, no statistically significant differences were found between the 1DOF and 2DOF conditions. While the lack of measurable improvement may reflect the influence of factors inherent to the 2DOF design, such as its greater length, added mass compared to 1DOF wrists, or increased control complexity, the results nonetheless indicate that the addition of a second wrist degree of freedom did not compromise functional performance. These findings suggest that more complex multi-DOF systems can be implemented without detriment to user function, an encouraging result for the continued development of advanced upper-limb prosthetic technologies.
{"title":"Assessing Functional Changes With the Integration of Wrist Flexion Into a Myoelectric Prosthesis","authors":"Laura A. Miller;Kristi L. Turner;Kevin Brenner;Levi J. Hargrove","doi":"10.1109/TNSRE.2025.3646472","DOIUrl":"10.1109/TNSRE.2025.3646472","url":null,"abstract":"This study investigates functional performance using a two-degree-of-freedom (2DOF) prosthetic wrist compared to a single-degree-of-freedom (1DOF) wrist in individuals with transradial (below-elbow) amputation. Five participants were fitted with a custom-designed 2DOF prosthetic wrist system integrated with an Ottobock Transcarpal hand and operated via a pattern recognition-based myoelectric control interface. Participants completed two test conditions: one using wrist rotation alone (1DOF, NoWF), and another using wrist rotation combined with wrist flexion and extension (2DOF, WF). A battery of standardized functional assessments was used to evaluate performance in both conditions, including the Southampton Hand Assessment Procedure (SHAP), Box and Blocks Test (BBT), Jebsen-Taylor Hand Function Test (JTHFT), Activity Measure for Upper Limb Amputees (AM-ULA), Clothespin Relocation Task (CRT), and the Assessment of Capacity for Myoelectric Control (ACMC). Across all outcome measures, no statistically significant differences were found between the 1DOF and 2DOF conditions. While the lack of measurable improvement may reflect the influence of factors inherent to the 2DOF design, such as its greater length, added mass compared to 1DOF wrists, or increased control complexity, the results nonetheless indicate that the addition of a second wrist degree of freedom did not compromise functional performance. These findings suggest that more complex multi-DOF systems can be implemented without detriment to user function, an encouraging result for the continued development of advanced upper-limb prosthetic technologies.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"366-371"},"PeriodicalIF":5.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11305176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145793843","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 : 2025-12-18DOI: 10.1109/TNSRE.2025.3646061
Laura De Arco;Ksawery Gusakowski;Carlos A. Cifuentes;Marcela Munera;Marcelo Segatto;Camilo A. R. Díaz
Prosthesis users often experience muscle fatigue and reduced control due to the weight of the device, contributing to high abandonment rates. This study investigates the effects of integrating a soft exoskeleton with a myoelectric prosthesis on upper-limb muscle fatigue and user experience. Nine non-disabled participants performed four functional tasks: drinking from a cup, using a Fork, lifting a box, and reaching overhead, using the prosthesis alone and in combination with the exoskeleton. Muscle activity was recorded via surface electromyography, and perceived exertion was measured using the Borg scale. Kinematics and workload were also assessed through motion capture and the NASA-TLX questionnaire. Usability was evaluated using the System Usability Scale (SUS). Results showed that exoskeleton assistance significantly reduced muscle activation, particularly in the Deltoid, Biceps, and Triceps Lateral Head during the Lift task, with RMS reductions up to 64 % and large effect sizes. Perceived exertion slopes decreased across all tasks, with some instances showing stabilization or reduction during activity. Kinematic analysis indicated minimal impact on shoulder range of motion, with slight adjustments in internal/external rotation remaining within physiological norms. NASA-TLX scores suggested reduced physical demand and effort, and SUS responses indicated moderate usability with room for improvement. These findings demonstrate that soft exoskeletons can effectively unload muscles and reduce fatigue during prosthesis use, highlighting their potential to enhance endurance, task performance, and user comfort. Future work should extend assistance to additional joints and evaluate the system with upper-limb amputees in real-world scenarios.
{"title":"Mitigating Muscle Fatigue in Upper-Limb Prosthesis Users Through Exoskeletal Weight Compensation","authors":"Laura De Arco;Ksawery Gusakowski;Carlos A. Cifuentes;Marcela Munera;Marcelo Segatto;Camilo A. R. Díaz","doi":"10.1109/TNSRE.2025.3646061","DOIUrl":"10.1109/TNSRE.2025.3646061","url":null,"abstract":"Prosthesis users often experience muscle fatigue and reduced control due to the weight of the device, contributing to high abandonment rates. This study investigates the effects of integrating a soft exoskeleton with a myoelectric prosthesis on upper-limb muscle fatigue and user experience. Nine non-disabled participants performed four functional tasks: drinking from a cup, using a Fork, lifting a box, and reaching overhead, using the prosthesis alone and in combination with the exoskeleton. Muscle activity was recorded via surface electromyography, and perceived exertion was measured using the Borg scale. Kinematics and workload were also assessed through motion capture and the NASA-TLX questionnaire. Usability was evaluated using the System Usability Scale (SUS). Results showed that exoskeleton assistance significantly reduced muscle activation, particularly in the Deltoid, Biceps, and Triceps Lateral Head during the Lift task, with RMS reductions up to 64 % and large effect sizes. Perceived exertion slopes decreased across all tasks, with some instances showing stabilization or reduction during activity. Kinematic analysis indicated minimal impact on shoulder range of motion, with slight adjustments in internal/external rotation remaining within physiological norms. NASA-TLX scores suggested reduced physical demand and effort, and SUS responses indicated moderate usability with room for improvement. These findings demonstrate that soft exoskeletons can effectively unload muscles and reduce fatigue during prosthesis use, highlighting their potential to enhance endurance, task performance, and user comfort. Future work should extend assistance to additional joints and evaluate the system with upper-limb amputees in real-world scenarios.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"334-344"},"PeriodicalIF":5.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145781172","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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