Pub Date : 2025-02-10DOI: 10.1109/TNSRE.2025.3540062
Tao Ma;Tianyang Fan;Xun Xu;Tao Sun
Patients with femoral fractures are typically advised to undergo partial weight-bearing (PWB) gait training during the postoperative rehabilitation period to facilitate bone healing and restore lower limb function. Various current portable biofeedback devices monitor ground reaction force (GRF) to assess the femoral loading of patients with fractures during PWB walking. However, due to the influence of muscle forces and the complexity of load transmission in the lower limbs, GRF may not accurately reflect the internal forces in the femur during walking. In this study, we developed an innovative biofeedback device that incorporates inertial measurement units and pressure-sensitive insoles. Utilizing data collected from 12 participants, a physics-informed temporal convolutional network (PITCN) method was proposed to estimate the internal femoral loading. The performance of the PITCN method was compared with two other machine learning approaches and a baseline method, demonstrating superior predictive capabilities. The study also revealed that, irrespective of the weight-bearing level during walking, the peak femoral loading consistently exceeded the peak GRF. Moreover, the timing of the peak values for these two forces within each gait cycle may not always coincide. These findings further emphasize the necessity of monitoring and providing feedback on the actual femoral loading, rather than solely relying on GRF, during PWB gait training for patients with fractures. The developed system is a non-invasive, reliable, and portable device that provides audio feedback. It shows potential as a viable solution for gait rehabilitation training in daily life, contributing to the enhancement of patients’ rehabilitation outcomes.
{"title":"Design of a Portable Biofeedback System for Monitoring Femoral Load During Partial Weight-Bearing Walking","authors":"Tao Ma;Tianyang Fan;Xun Xu;Tao Sun","doi":"10.1109/TNSRE.2025.3540062","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3540062","url":null,"abstract":"Patients with femoral fractures are typically advised to undergo partial weight-bearing (PWB) gait training during the postoperative rehabilitation period to facilitate bone healing and restore lower limb function. Various current portable biofeedback devices monitor ground reaction force (GRF) to assess the femoral loading of patients with fractures during PWB walking. However, due to the influence of muscle forces and the complexity of load transmission in the lower limbs, GRF may not accurately reflect the internal forces in the femur during walking. In this study, we developed an innovative biofeedback device that incorporates inertial measurement units and pressure-sensitive insoles. Utilizing data collected from 12 participants, a physics-informed temporal convolutional network (PITCN) method was proposed to estimate the internal femoral loading. The performance of the PITCN method was compared with two other machine learning approaches and a baseline method, demonstrating superior predictive capabilities. The study also revealed that, irrespective of the weight-bearing level during walking, the peak femoral loading consistently exceeded the peak GRF. Moreover, the timing of the peak values for these two forces within each gait cycle may not always coincide. These findings further emphasize the necessity of monitoring and providing feedback on the actual femoral loading, rather than solely relying on GRF, during PWB gait training for patients with fractures. The developed system is a non-invasive, reliable, and portable device that provides audio feedback. It shows potential as a viable solution for gait rehabilitation training in daily life, contributing to the enhancement of patients’ rehabilitation outcomes.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"770-779"},"PeriodicalIF":4.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430446","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 : 2025-02-10DOI: 10.1109/TNSRE.2025.3540708
B. L. Pugliese;A. Angelucci;F. Parisi;S. Sapienza;E. Fabara;G. Corniani;A. S. Tenforde;A. Aliverti;D. Demarchi;P. Bonato
This study presents a novel wearable solution integrating Polymer Optical Fiber (POF) sensors into a knee sleeve to monitor knee flexion/extension (F/E) patterns during walking. POF sensors offer advantages such as flexibility, light weight, and robustness to electromagnetic interference, making them ideal for wearable applications. However, when one integrates these sensors into a knee sleeve, they exhibit non-linearities, including hysteresis and mode coupling, which complicate signal interpretation. To address this issue, a Long Short-Term Memory (LSTM) network was implemented to model temporal dependencies in sensor output, hence providing accurate knee angle estimates. Data were collected from 31 participants walking at different speeds on a treadmill, using a camera-based motion capture system for validation. Configurations with multiple (up to five) sensors were considered. The best performance was achieved using three sensors, yielding a median root mean square error (RMSE) of 3.41° (interquartile range: 2.50° – $5.19^{circ }text {)}$ . Whereas using multiple sensors generally improved robustness, the inclusion of data from sub-optimally placed sensors negatively affected performance. The technology holds potential for clinical application in knee osteoarthritis (OA) management. Future work should focus on optimizing signal calibration and expanding the dataset to facilitate accounting for the different ways in which the knee sleeve conforms to the anatomy of different individuals.
{"title":"Development of a Wearable Sleeve-Based System Combining Polymer Optical Fiber Sensors and an LSTM Network for Estimating Knee Kinematics","authors":"B. L. Pugliese;A. Angelucci;F. Parisi;S. Sapienza;E. Fabara;G. Corniani;A. S. Tenforde;A. Aliverti;D. Demarchi;P. Bonato","doi":"10.1109/TNSRE.2025.3540708","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3540708","url":null,"abstract":"This study presents a novel wearable solution integrating Polymer Optical Fiber (POF) sensors into a knee sleeve to monitor knee flexion/extension (F/E) patterns during walking. POF sensors offer advantages such as flexibility, light weight, and robustness to electromagnetic interference, making them ideal for wearable applications. However, when one integrates these sensors into a knee sleeve, they exhibit non-linearities, including hysteresis and mode coupling, which complicate signal interpretation. To address this issue, a Long Short-Term Memory (LSTM) network was implemented to model temporal dependencies in sensor output, hence providing accurate knee angle estimates. Data were collected from 31 participants walking at different speeds on a treadmill, using a camera-based motion capture system for validation. Configurations with multiple (up to five) sensors were considered. The best performance was achieved using three sensors, yielding a median root mean square error (RMSE) of 3.41° (interquartile range: 2.50° – <inline-formula> <tex-math>$5.19^{circ }text {)}$ </tex-math></inline-formula>. Whereas using multiple sensors generally improved robustness, the inclusion of data from sub-optimally placed sensors negatively affected performance. The technology holds potential for clinical application in knee osteoarthritis (OA) management. Future work should focus on optimizing signal calibration and expanding the dataset to facilitate accounting for the different ways in which the knee sleeve conforms to the anatomy of different individuals.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"728-738"},"PeriodicalIF":4.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403803","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 : 2025-02-07DOI: 10.1109/TNSRE.2025.3539700
Haoran Ren;Xinyu Jiang;Chenyun Dai
The EMG-based neural control strategy provides technical support and innovative methodologies for the precise control and natural movement of hand prostheses. For enabling the prostheses to mimic the function of the human hand, it is essential to fully understand the principle of neuromuscular control of human hand. However, we still have little knowledge regarding the functional significance of the proportion of common or independent neural input to the antagonist muscles in extensor and flexor during performing different tasks. Therefore, we used a discharge coherence analysis of motor unit (MU) spike trains to investigate different sources of common and independent input between extensor and flexor muscle groups in different hand gestures. 14 gestures were selected, including the extension of individual and multi fingers, as well as pinch finger tasks. The MU spike trains were obtained from the decomposition of high-density surface electromyography (HD-sEMG) recordings using fastICA. The proportion of the independent neural input to extensor or flexor muscle groups was calculated by the ratio of residual coherence to total coherence. The results showed that the two muscle groups are comparable in independent proportion within each muscle group with relatively greater values in the extensor muscle. The degree of common input shared between the two muscle groups exhibited the highest level (around 35%) in the delta band (1-4Hz) compared to a very small proportion (<13%) in the other frequency bands. Additionally, a significant difference was observed in little finger extension and hand close tasks compared to other hand gestures. Overall, the varied proportions of independent neural input across hand gestures and among muscle groups illustrate the precise neural modulation involved in the co-contraction of the flexor and extensor muscles during flexible hand movements, as observed through the microscopic view of motoneurons.
{"title":"Exploring Control Mechanism of Motoneuron Pools for the Forearm Antagonist Synergistic Muscles","authors":"Haoran Ren;Xinyu Jiang;Chenyun Dai","doi":"10.1109/TNSRE.2025.3539700","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3539700","url":null,"abstract":"The EMG-based neural control strategy provides technical support and innovative methodologies for the precise control and natural movement of hand prostheses. For enabling the prostheses to mimic the function of the human hand, it is essential to fully understand the principle of neuromuscular control of human hand. However, we still have little knowledge regarding the functional significance of the proportion of common or independent neural input to the antagonist muscles in extensor and flexor during performing different tasks. Therefore, we used a discharge coherence analysis of motor unit (MU) spike trains to investigate different sources of common and independent input between extensor and flexor muscle groups in different hand gestures. 14 gestures were selected, including the extension of individual and multi fingers, as well as pinch finger tasks. The MU spike trains were obtained from the decomposition of high-density surface electromyography (HD-sEMG) recordings using fastICA. The proportion of the independent neural input to extensor or flexor muscle groups was calculated by the ratio of residual coherence to total coherence. The results showed that the two muscle groups are comparable in independent proportion within each muscle group with relatively greater values in the extensor muscle. The degree of common input shared between the two muscle groups exhibited the highest level (around 35%) in the delta band (1-4Hz) compared to a very small proportion (<13%) in the other frequency bands. Additionally, a significant difference was observed in little finger extension and hand close tasks compared to other hand gestures. Overall, the varied proportions of independent neural input across hand gestures and among muscle groups illustrate the precise neural modulation involved in the co-contraction of the flexor and extensor muscles during flexible hand movements, as observed through the microscopic view of motoneurons.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"750-759"},"PeriodicalIF":4.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10877898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a novel methodology for simultaneously predicting hand grasp and forearm motion using target muscle bioimpedance measurements and regression models. A total of six channels, formed by nine electrodes, are employed for this multi-degree of freedom (DoF) prediction. Given the time-dependent nature of bioimpedance variation, the long short-term memory (LSTM) regression model is more competent in multi-DoF prediction, compared to linear regression (LR), support vector regression (SVR) and multilayer perceptron (MLP). In intra-subject cross-validation, MLP yields an average coefficient of determination (R2) of 0.9256 for predicting hand grasping angle, while LSTM achieves an average R2 of 0.9483 for predicting random simultaneous forearm two-DoF motion. Operation by amputees without the need to train the regression models is possible by mapping muscle bioimpedance variation directly to the prediction angle, allowing for the approximate estimation of single-DoF motion. The efficacy of these prediction approaches is demonstrated in a real-time object grasping task.
{"title":"Forearm Motion and Hand Grasp Prediction Based on Target Muscle Bioimpedance for Human–Machine Interaction","authors":"Tianyang Yao;Yu Wu;Dai Jiang;Richard Bayford;Andreas Demosthenous","doi":"10.1109/TNSRE.2025.3538609","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3538609","url":null,"abstract":"This paper introduces a novel methodology for simultaneously predicting hand grasp and forearm motion using target muscle bioimpedance measurements and regression models. A total of six channels, formed by nine electrodes, are employed for this multi-degree of freedom (DoF) prediction. Given the time-dependent nature of bioimpedance variation, the long short-term memory (LSTM) regression model is more competent in multi-DoF prediction, compared to linear regression (LR), support vector regression (SVR) and multilayer perceptron (MLP). In intra-subject cross-validation, MLP yields an average coefficient of determination (R2) of 0.9256 for predicting hand grasping angle, while LSTM achieves an average R2 of 0.9483 for predicting random simultaneous forearm two-DoF motion. Operation by amputees without the need to train the regression models is possible by mapping muscle bioimpedance variation directly to the prediction angle, allowing for the approximate estimation of single-DoF motion. The efficacy of these prediction approaches is demonstrated in a real-time object grasping task.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"760-769"},"PeriodicalIF":4.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430445","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}
Providing feedback on energy expenditure (EE) may be an important tool to support obesity prevention among manual wheelchair users (MWU). This paper presents a data-driven approach for estimating EE based on data collected from 40 participants (20 MWU and 20 controls without disability) across different activities (lying, sitting and wheelchair propulsion at different intensities). We extracted features from heart rate, inertial measurement units (IMU), and individual personal characteristics to develop activity classification and EE estimation algorithms and investigate the influence of personal characteristics on EE estimates. Support Vector Machines were selected as classifiers, while Support Vector Regressors, Gaussian Processes, Random Forest, XGBoost, and Neural Networks were selected as regression models. High classification accuracy was achieved with minor confusion between activities and EE estimation results showed high generalisation capabilities of the trained models on unseen participants. We explored the impact of changing the position of the IMU on the accuracy of EE estimations. We recommend the wrist as the primary location for sensor placement. It provides a good trade-off between accuracy, high wear compliance rates and the possibility of integrating our algorithms in already existing wearable devices. Our findings showed that including data collected from people without disabilities to develop EE estimation algorithms for MWU did not enhance the estimation accuracy. In conclusion, data-driven algorithms based on wearable sensors and personal characteristics are effective for activity classification and EE estimation in MWU, but need to be personalized and further developed for daily life settings to be ecologically valid.
{"title":"Data-Driven Techniques for Estimating Energy Expenditure in Wheelchair Users","authors":"Roya Doshmanziari;Håkon Strand Aandahl;Håvard Pettersen Reierstad;Marius Lyng Danielsson;Julia Kathrin Baumgart;Damiano Varagnolo","doi":"10.1109/TNSRE.2025.3537333","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3537333","url":null,"abstract":"Providing feedback on energy expenditure (EE) may be an important tool to support obesity prevention among manual wheelchair users (MWU). This paper presents a data-driven approach for estimating EE based on data collected from 40 participants (20 MWU and 20 controls without disability) across different activities (lying, sitting and wheelchair propulsion at different intensities). We extracted features from heart rate, inertial measurement units (IMU), and individual personal characteristics to develop activity classification and EE estimation algorithms and investigate the influence of personal characteristics on EE estimates. Support Vector Machines were selected as classifiers, while Support Vector Regressors, Gaussian Processes, Random Forest, XGBoost, and Neural Networks were selected as regression models. High classification accuracy was achieved with minor confusion between activities and EE estimation results showed high generalisation capabilities of the trained models on unseen participants. We explored the impact of changing the position of the IMU on the accuracy of EE estimations. We recommend the wrist as the primary location for sensor placement. It provides a good trade-off between accuracy, high wear compliance rates and the possibility of integrating our algorithms in already existing wearable devices. Our findings showed that including data collected from people without disabilities to develop EE estimation algorithms for MWU did not enhance the estimation accuracy. In conclusion, data-driven algorithms based on wearable sensors and personal characteristics are effective for activity classification and EE estimation in MWU, but need to be personalized and further developed for daily life settings to be ecologically valid.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"739-749"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858782","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403947","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 : 2025-01-29DOI: 10.1109/TNSRE.2025.3535928
Xiaohan Wang;Weidong Li;Rong Song;Di Ao;Huijing Hu;Le Li
The study aimed to investigate changes in corticomuscular coupling during elbow flexion and extension in stroke survivors using functional near-infrared spectroscopy (fNIRS) and surface electromyography (sEMG), and to evaluate the relationship between coupling characteristics and clinical assessment scales. This study recruited 12 stroke survivors and 12 age-matched healthy subjects, and further divided the subjects into the affected side group, healthy-side group and age-matched healthy group. They performed elbow flexion and extension tasks at 30% and 70% of the maximum voluntary contraction (MVC). The cerebral blood flow dynamics of the bilateral prefrontal cortex, motor cortex, and occipital lobe, along with sEMG signals from the biceps brachii and triceps brachii, were simultaneously recorded. At matched force levels, the fuzzy approximate entropy values of both agonist and antagonistic muscles were notably lower in the affected group compared to the healthy group (P < 0.05). The effective connectivity from the ipsilateral motor cortex to the contralateral motor cortex during elbow movements in the affected group showed a meaningful positive association with the Fugl-Meyer Assessment (FMA) scale. Additionally, the transfer entropy from the contralateral motor cortex to the agonist muscle in the affected group demonstrated a significant positive correlation with the FMA scale at 70% MVC during elbow flexion. This research identified differences in intermuscular coordination, brain network connectivity, and corticomuscular coupling between stroke survivors and healthy individuals during motor tasks and our findings suggest that it can serve as a potential quantitative marker for assessing upper limb motor function post-stroke. The relationship between these characteristics and clinical scales signifies potential quantitative assessment parameters for stroke rehabilitation, underscoring the importance of exploring corticomuscular coupling in the recovery of upper limb motor function post-stroke.
{"title":"Corticomuscular Coupling Alterations During Elbow Isometric Contraction Correlated With Clinical Scores: An fNIRS-sEMG Study in Stroke Survivors","authors":"Xiaohan Wang;Weidong Li;Rong Song;Di Ao;Huijing Hu;Le Li","doi":"10.1109/TNSRE.2025.3535928","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3535928","url":null,"abstract":"The study aimed to investigate changes in corticomuscular coupling during elbow flexion and extension in stroke survivors using functional near-infrared spectroscopy (fNIRS) and surface electromyography (sEMG), and to evaluate the relationship between coupling characteristics and clinical assessment scales. This study recruited 12 stroke survivors and 12 age-matched healthy subjects, and further divided the subjects into the affected side group, healthy-side group and age-matched healthy group. They performed elbow flexion and extension tasks at 30% and 70% of the maximum voluntary contraction (MVC). The cerebral blood flow dynamics of the bilateral prefrontal cortex, motor cortex, and occipital lobe, along with sEMG signals from the biceps brachii and triceps brachii, were simultaneously recorded. At matched force levels, the fuzzy approximate entropy values of both agonist and antagonistic muscles were notably lower in the affected group compared to the healthy group (P < 0.05). The effective connectivity from the ipsilateral motor cortex to the contralateral motor cortex during elbow movements in the affected group showed a meaningful positive association with the Fugl-Meyer Assessment (FMA) scale. Additionally, the transfer entropy from the contralateral motor cortex to the agonist muscle in the affected group demonstrated a significant positive correlation with the FMA scale at 70% MVC during elbow flexion. This research identified differences in intermuscular coordination, brain network connectivity, and corticomuscular coupling between stroke survivors and healthy individuals during motor tasks and our findings suggest that it can serve as a potential quantitative marker for assessing upper limb motor function post-stroke. The relationship between these characteristics and clinical scales signifies potential quantitative assessment parameters for stroke rehabilitation, underscoring the importance of exploring corticomuscular coupling in the recovery of upper limb motor function post-stroke.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"696-704"},"PeriodicalIF":4.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379520","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 : 2025-01-29DOI: 10.1109/TNSRE.2025.3535696
Lin Meng;Xiaofei Zhang;Yu Shi;Xinge Li;Jun Pang;Lei Chen;Xiaodong Zhu;Rui Xu;Dong Ming
As one of the main motor indicators of Parkinson’s disease (PD), postural instability and gait disorder (PIGD) might manifest in various but subtle symptoms at early stage resulting in relatively high misdiagnosis rate. Quantitative gait assessment under dual task or complex motor task (i.e., turning) may contribute to better understanding of PIGD and provide a better diagnostic indicator of early-stage PD. However, few studies have explored gait deviation evaluation algorithms under a complex dual task that reflect disease specificity. In this work, we proposed a novel inertial-based gait normalcy index (GNI) based on inertial-based quantitative gait assessment model to characterize the overall gait performance during both straight walking and turning with or without serial-3 subtraction task. The factor of group and task on the GNI variable was investigated and the feasibility of GNI to improve early-stage PD diagnostic performance was validated. The experimental results showed that the task paradigm is a significant factor on GNI performance where the dual-task GNI at turn had the best discriminating ability between early PD and HC (AUC =0.992) and was significantly correlated with UPDRS III (r =0.81), MMSE(r =0.57) and Mini-BEST(r =0.65). We also observed that the turning-based GNI has larger effect size compared to clinical scales, demonstrating that GNI during turning can reflect the changes of functional mobility in rehabilitation for the early PD. Our work offers an innovative and potential gait biomarker for early-stage PD diagnostics and provides a new perspective into gait performance of complex dual task and its application in early PD.
{"title":"Inertial-Based Dual-Task Gait Normalcy Index at Turns: A Potential Novel Gait Biomarker for Early-Stage Parkinson’s Disease","authors":"Lin Meng;Xiaofei Zhang;Yu Shi;Xinge Li;Jun Pang;Lei Chen;Xiaodong Zhu;Rui Xu;Dong Ming","doi":"10.1109/TNSRE.2025.3535696","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3535696","url":null,"abstract":"As one of the main motor indicators of Parkinson’s disease (PD), postural instability and gait disorder (PIGD) might manifest in various but subtle symptoms at early stage resulting in relatively high misdiagnosis rate. Quantitative gait assessment under dual task or complex motor task (i.e., turning) may contribute to better understanding of PIGD and provide a better diagnostic indicator of early-stage PD. However, few studies have explored gait deviation evaluation algorithms under a complex dual task that reflect disease specificity. In this work, we proposed a novel inertial-based gait normalcy index (GNI) based on inertial-based quantitative gait assessment model to characterize the overall gait performance during both straight walking and turning with or without serial-3 subtraction task. The factor of group and task on the GNI variable was investigated and the feasibility of GNI to improve early-stage PD diagnostic performance was validated. The experimental results showed that the task paradigm is a significant factor on GNI performance where the dual-task GNI at turn had the best discriminating ability between early PD and HC (AUC =0.992) and was significantly correlated with UPDRS III (r =0.81), MMSE(r =0.57) and Mini-BEST(r =0.65). We also observed that the turning-based GNI has larger effect size compared to clinical scales, demonstrating that GNI during turning can reflect the changes of functional mobility in rehabilitation for the early PD. Our work offers an innovative and potential gait biomarker for early-stage PD diagnostics and provides a new perspective into gait performance of complex dual task and its application in early PD.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"687-695"},"PeriodicalIF":4.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403946","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 : 2025-01-28DOI: 10.1109/TNSRE.2025.3535639
Ruikai Cao;Yixuan Sheng;Anqin Dong;Honghai Liu
It is evident that voluntary effort plays a crucial role in electrical stimulation rehabilitation, facilitating neuroplasticity enhancement in patients with neurological disorders. In this paper, we present a multichannel system designed for simultaneous functional electrical stimulation (FES) and volitional EMG (vEMG) acquisition using shared electrodes. The system employs hardware blanking with electrodes shorting to suppress stimulation artifacts and accelerate residual charge dissipation. Additionally, we adapt the Savitzky-Golay filter to extract high-quality, real-time vEMG from FES-contaminated signals, with the optimal filter parameters for different stimulation and blanking periods determined using a genetic algorithm and semi-synthesized signals. Simulation and experimental results confirm that the proposed system ensures robust and high-quality vEMG acquisition, even under varying parameters and across different individuals. In summary, this work advances the development of closed-loop rehabilitation applications and enables further investigation of neuromuscular characteristics under FES.
{"title":"Design of a Savitzky-Golay Filter-Based vEMG-FES System","authors":"Ruikai Cao;Yixuan Sheng;Anqin Dong;Honghai Liu","doi":"10.1109/TNSRE.2025.3535639","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3535639","url":null,"abstract":"It is evident that voluntary effort plays a crucial role in electrical stimulation rehabilitation, facilitating neuroplasticity enhancement in patients with neurological disorders. In this paper, we present a multichannel system designed for simultaneous functional electrical stimulation (FES) and volitional EMG (vEMG) acquisition using shared electrodes. The system employs hardware blanking with electrodes shorting to suppress stimulation artifacts and accelerate residual charge dissipation. Additionally, we adapt the Savitzky-Golay filter to extract high-quality, real-time vEMG from FES-contaminated signals, with the optimal filter parameters for different stimulation and blanking periods determined using a genetic algorithm and semi-synthesized signals. Simulation and experimental results confirm that the proposed system ensures robust and high-quality vEMG acquisition, even under varying parameters and across different individuals. In summary, this work advances the development of closed-loop rehabilitation applications and enables further investigation of neuromuscular characteristics under FES.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"610-619"},"PeriodicalIF":4.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361095","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 : 2025-01-28DOI: 10.1109/TNSRE.2025.3535681
Junxiang Zheng;Bing Jiang;Saurabh Biswas;Su Young Lee;Erin Ealba Bumann;Teresa E. Lever;Jeonghee Kim;Hangue Park
Mastication plays an important role in effective food digestion and nutrient absorption. Therefore, regulating masticatory force in people with declining mastication function is significant for maintaining health and quality of life. In this study, we tested the effect of tactile augmentation on mastication force. To augment tactile feedback during mastication, we applied closed-loop electrical stimulation onto the mandibular vestibule using an intraoral tooth-borne electronic system. We hypothesized that closed-loop electrical stimulation, timed with mastication and applied to the nerves delivering tactile feedback to the brain, would evoke an increase in masticatory force. Experiments were completed using the intraoral system with six healthy human subjects who masticated soft and hard foods with and without stimulation during the experiment. Their mastication forces were recorded ten times per condition. The recorded mastication force profile showed that mastication force was higher with the harder food. Also, mastication force increased when electrical stimulation was applied, compared to the non-stimulated condition. These results support the hypothesis that tactile augmentation by intraoral closed-loop electrical stimulation will increase masticatory force. Other mastication parameters including period, spike width, and duty cycle are also changed by electrical stimulation. Further, stimulation left a strong aftereffect on these mastication parameters.
{"title":"Alteration of Mastication Force via Intraoral Closed-Loop Electrical Stimulation","authors":"Junxiang Zheng;Bing Jiang;Saurabh Biswas;Su Young Lee;Erin Ealba Bumann;Teresa E. Lever;Jeonghee Kim;Hangue Park","doi":"10.1109/TNSRE.2025.3535681","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3535681","url":null,"abstract":"Mastication plays an important role in effective food digestion and nutrient absorption. Therefore, regulating masticatory force in people with declining mastication function is significant for maintaining health and quality of life. In this study, we tested the effect of tactile augmentation on mastication force. To augment tactile feedback during mastication, we applied closed-loop electrical stimulation onto the mandibular vestibule using an intraoral tooth-borne electronic system. We hypothesized that closed-loop electrical stimulation, timed with mastication and applied to the nerves delivering tactile feedback to the brain, would evoke an increase in masticatory force. Experiments were completed using the intraoral system with six healthy human subjects who masticated soft and hard foods with and without stimulation during the experiment. Their mastication forces were recorded ten times per condition. The recorded mastication force profile showed that mastication force was higher with the harder food. Also, mastication force increased when electrical stimulation was applied, compared to the non-stimulated condition. These results support the hypothesis that tactile augmentation by intraoral closed-loop electrical stimulation will increase masticatory force. Other mastication parameters including period, spike width, and duty cycle are also changed by electrical stimulation. Further, stimulation left a strong aftereffect on these mastication parameters.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"675-686"},"PeriodicalIF":4.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361197","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 : 2025-01-28DOI: 10.1109/TNSRE.2025.3535564
Dapeng Chen;Song Zhang;Lianshun Shen;Chenkai Li;Jianying Hua;Jia Liu;Aiguo Song
To help the blind or visually impaired (BVI) read digitally conveniently and at low cost, we propose a bounce-type actuator driven by electromagnetic force, and based on this, a refreshable Braille display (RBD) which can display both Braille and tactile graphic information is manufactured. The internal components of the bounce-type actuator include an electromagnet and two permanent magnets placed in a stepped manner. By passing current in different directions to the coil, the electromagnet can bounce up and down between the two permanent magnets under the action of magnetic force, thereby achieving changes in the raised state of the Braille dot. The permanent magnets placed in staggered positions have an attractive and supportive effect on the electromagnet, so the raised Braille dot can be locked in a specific position when the electromagnet is not powered on and provide a large latching force. In this paper, the force analysis and finite element simulation of the actuator actuation condition of the Braille dot actuator are carried out, and the refresh rate is measured experimentally to be about 16 Hz. Meanwhile, the overall refresh rate of the prototype can be up to 2.7 Hz. Benefiting from the full-latching structure, the RBD proposed in this paper is characterized by a large latching force, a high refresh rate, and an easy scalability. It also has the advantages of low cost, low energy consumption, reliability, and durability, providing an easy-to-promote tool for BVI to read digital information.
{"title":"Design and Evaluation of an Electromagnetic Bounce-Type Refreshable Braille Display","authors":"Dapeng Chen;Song Zhang;Lianshun Shen;Chenkai Li;Jianying Hua;Jia Liu;Aiguo Song","doi":"10.1109/TNSRE.2025.3535564","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3535564","url":null,"abstract":"To help the blind or visually impaired (BVI) read digitally conveniently and at low cost, we propose a bounce-type actuator driven by electromagnetic force, and based on this, a refreshable Braille display (RBD) which can display both Braille and tactile graphic information is manufactured. The internal components of the bounce-type actuator include an electromagnet and two permanent magnets placed in a stepped manner. By passing current in different directions to the coil, the electromagnet can bounce up and down between the two permanent magnets under the action of magnetic force, thereby achieving changes in the raised state of the Braille dot. The permanent magnets placed in staggered positions have an attractive and supportive effect on the electromagnet, so the raised Braille dot can be locked in a specific position when the electromagnet is not powered on and provide a large latching force. In this paper, the force analysis and finite element simulation of the actuator actuation condition of the Braille dot actuator are carried out, and the refresh rate is measured experimentally to be about 16 Hz. Meanwhile, the overall refresh rate of the prototype can be up to 2.7 Hz. Benefiting from the full-latching structure, the RBD proposed in this paper is characterized by a large latching force, a high refresh rate, and an easy scalability. It also has the advantages of low cost, low energy consumption, reliability, and durability, providing an easy-to-promote tool for BVI to read digital information.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"705-716"},"PeriodicalIF":4.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379566","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：