IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Chronic CNT Fiber Interface With Median Nerve at Acupoint PC6 for Rat’s Myocardial Ischemia Control","authors":"Aotian Yu;Pengwei Li;Junge Yuan;Lei Han;Meng Li;Xiaodan Song;Chang Liu;Qixuan Fu;Simin Ning;Yemao Chai;Yuanyuan Shang;Anyuan Cao;Cunzhi Liu;Wenjing Xu","doi":"10.1109/TNSRE.2025.3555405","DOIUrl":"10.1109/TNSRE.2025.3555405","url":null,"abstract":"Myocardial ischemia is one of the most prevalent cardiovascular diseases, underscoring the need for safer and effective therapeutic approaches. Peripheral nerve stimulation, particularly vagus nerve stimulation has emerged as a promising non-pharmaceutical therapy for managing myocardial ischemia. However, vagus nerve stimulation carries risks, such as off-target effects and adverse cardiac events due to its extensive innervation and mixed afferent/efferent fiber composition. Therefore, it is crucial to explore a safer and more user-friendly peripheral nerve interface. In this work, we developed a novel chronic median nerve interface using carbon nanotube fibers as electrodes to stimulate the median nerve at the acupoint PC6 for myocardial ischemia control. Carbon nanotube fibers exhibited excellent biocompatibility, flexibility, conductivity, and charge storage capacity, making them ideal for reliable and prolonged median nerve stimulation. Our results demonstrated that median nerve stimulation at the acupoint PC6 achieved therapeutic effects comparable to electroacupuncture, including improvement in S-T segment values, LF/HF ratios, cardiac index and cardiac troponin T, while being safer and easier to operate than vagus nerve stimulation. Moreover, median nerve stimulation exhibited superior transient and residual effects compared to electroacupuncture, despite a slower response time. Additionally, histological and fluorescence analyses confirmed the safety of the CNTF-based interface over time. These findings suggested that median nerve stimulation at the acupoint PC6 combined the efficacy of nerve stimulation with the safety of acupuncture, offering a promising approach for myocardial ischemia control, particularly in chronic and repeated treatment scenarios. Further researches are warranted to optimize CNTF properties, elucidate the underlying mechanisms of median nerve stimulation, and explore its potential in clinical applications.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1295-1304"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10943231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Linearity in Multi-Joint Upper Limb Dynamics Under Small Perturbations for Reliable Mechanical Impedance Estimation","authors":"Seongil Hwang;Hyunah Kang;Sang Hoon Kang","doi":"10.1109/TNSRE.2025.3554805","DOIUrl":"10.1109/TNSRE.2025.3554805","url":null,"abstract":"This study investigates the linear behavior of multi-joint upper limb dynamics under small perturbations, a prerequisite for stochastic estimation of upper limb mechanical impedance, which is crucial for understanding motor control and has the potential to assess neurological disorders. Conflicting reports exist on the linearity of upper limb dynamics under small perturbations, even for healthy individuals. We hypothesized that the multi-joint upper limb behaves linearly under small perturbations and that uncompensated nonlinear robot joint frictions degrade impedance estimation reliability. The upper limb multi-joint mechanical impedance of ten healthy individuals was estimated using a 2-degree-of-freedom direct-drive robot similar to MIT-MANUS, known for small joint frictions, under two conditions: without (using Cartesian proportional-derivative control) and with (using internal model based impedance control) friction compensation. Multiple and partial coherences were close to unity with friction compensation and significantly higher than without it, confirming that the upper limb behaves linearly under small perturbations and that previously reported nonlinearity detected by low coherences was due to small but significant robot joint frictions. It is expected that confirming the linearity of the upper limb under small perturbations allows for confident upper limb impedance estimation, thereby promoting motor control studies and complementing the diagnosis of the altered upper-limb dynamics post-stroke.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1284-1294"},"PeriodicalIF":4.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10942473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Neural Correlates and Behavioral Impact of Peripheral Noise Electrical Stimulation on Motor Learning","authors":"Li-Wei Chou;Man-Wai Kou;Hui-Min Lee;Felipe Fregni;Vincent Chen;Chung-Lan Kao","doi":"10.1109/TNSRE.2025.3555203","DOIUrl":"10.1109/TNSRE.2025.3555203","url":null,"abstract":"Somatosensory input plays a critical role in motor learning. Noise reduces the neural activation threshold and enhances the sensitivity of sensory neurons. While research has demonstrated that peripheral electrical stimulation with noise waveform improves motor performance and functions, the effects of noise electrical stimulation on motor learning remain unknown. This study aimed to investigate the immediate effects of peripheral noise electrical stimulation on motor learning and corresponding neural activities in the motor cortex. Eighteen healthy adults participated in 2 experimental sessions (i.e., noise and sham electrical stimulation conditions) on 2 separate days. Participants performed a grip force tracking task to follow a 0.5 Hz continuous sine wave with amplitudes of 10, 20, and 30% maximal voluntary isometric contraction while the electroencephalogram (EEG) of the sensorimotor cortex and the electromyography (EMG) of the right finger flexors were recorded. The differences (force error) between the actual and the targeted force were calculated, and motor learning was achieved by reducing the force error to a plateau. The efficiency of motor learning was defined as how fast the force error reached a plateau. Two-way (conditions [noise vs sham stimulation] by time [during vs post]) analysis of variance with repeated measures was used to compare the differences in force error, EEG power spectrum density (PSD), and EEG-EMG (corticomuscular) coherence (CMC). The significance level was set at 0.05. Noise electrical stimulation significantly reduced the force error both during and post motor learning (p <0.05)>30 Hz) CMC. We also observed that motor learning resulted in a decrease in EEG PSD beta band and gamma CMC. This study demonstrated that noise electrical stimulation during motor learning significantly reduced the time required to learn a motor task. We also identified neurophysiological signatures that associate with motor learning, including desynchronization of EEG beta power and reduced functional connectivity between the brain and muscles. These findings could potentially help develop novel motor training strategies and precision interventions.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1263-1270"},"PeriodicalIF":4.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10942472","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees","authors":"Elaheh Mohammadreza;Vinicius Prado da Fonseca;Xianta Jiang","doi":"10.1109/TNSRE.2025.3555100","DOIUrl":"10.1109/TNSRE.2025.3555100","url":null,"abstract":"Myoelectric control schemes, pivotal in the control of prosthetic limbs, are often developed and evaluated in ideal laboratory conditions. However, these controlled environments may not fully represent the diverse challenges users face in real-world scenarios. The present study aims to tackle some of the existing research limitations by exploring the influence of various model training protocols on myoelectric pattern recognition within a semi-autonomous control system, which has been shown to reduce user cognitive load and enhance overall system performance. Specifically, we focus on the effects of limb movement and weight-bearing activities. We investigate the effect of four distinct training protocols in pattern recognition control for upper-limb prostheses, including training without a prosthetic hand, training with a prosthetic hand and static gestures, training with a prosthetic hand and dynamic movements guided by a graphical user interface (GUI), and training with a prosthetic hand having dynamic transfers and unguided. By examining these conditions, we aim to provide an understanding of how different training protocols and different labeling methods influence myoelectric pattern recognition control. Our results, based on experiments conducted with 14 non-disabled and one amputee participant, suggest that introducing the weight of the prosthetic hand and dynamic movements of the arm to the training data improves the accuracy and robustness of the control scheme. Real-time control experiments with a group of five non-disabled and one amputee participant using a multi-DOF prosthetic hand also verify our findings.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1338-1348"},"PeriodicalIF":4.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10942474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Exoskeleton Trajectory Toward Minimizing Human Joint Torques","authors":"Tianyi Sun;Zhenlei Chen;Qing Guo;Yao Yan","doi":"10.1109/TNSRE.2025.3553861","DOIUrl":"10.1109/TNSRE.2025.3553861","url":null,"abstract":"The reference trajectory, serving as the sole kinematic guidance, is crucial for exoskeleton robot systems. This study introduces a method for generating an optimal trajectory for lower-limb exoskeletons, aiming at reducing human power during walking. Initially, the human joint angles were computed from measured data by a neighborhood field optimization (NFO). Subsequently, inverse dynamic analysis including seven-link dynamic model of human-exoskeleton coupling and corresponding ground reaction forces optimization were constructed, which was surrogated by a back propagation neural network (BPNN) to accelerate successive analyses. The exoskeleton trajectory, generated by perturbing human movement described by Fourier series, was optimized using a NFO algorithm with a revised initial generation strategy and boundary update function to minimize human joint torques. This approach was found to provide more accurate predictions of human trajectory and ground reaction forces compared to traditional methods, achieving a root mean square error (RMSE) within 5 mm and 3 kN respectively, making it suitable for computational applications. The generated trajectory preserves individual walking patterns and anticipates human motion with a mean leading value of 4.6%, effectively reducing joint torque across various gait phases. This research contributes significantly to the analysis of human-exoskeleton interactions and offers valuable insights for designing energy-efficient exoskeletons.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1231-1241"},"PeriodicalIF":4.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Gait Parameters and Their Variability on Fall Risk Assessment Accuracy Using Wearable Sensor","authors":"Jinghao Cai;Zeyang Guan;Jiachen Wang;Ziyun Ding;Yibin Li;Rui Song;Huanghe Zhang","doi":"10.1109/TNSRE.2025.3572109","DOIUrl":"10.1109/TNSRE.2025.3572109","url":null,"abstract":"Wearable sensors are increasingly utilized in fall risk assessments, providing precise stride-to-stride spatiotemporal gait parameters that are correlated with a heightened risk of falls. However, the impact of these gait parameters and their variability on the overall accuracy of fall risk prediction models remains an open question. This study introduced three fundamental machine learning models—logistic regression, support vector machines (SVM), and an artificial neural network—to predict fall risk among 163 frail older adults. Gait parameters and their variability were collected from a foot-mounted inertial measurement unit (IMU) and computed based on walking test durations ranging from 1 to 15 minutes, instead of using stride numbers, which are impractical in real clinical settings. Leave-one-out cross-validation was employed to evaluate the models’ performance, revealing that optimal walking test durations ranged from 6 to 10 minutes. The artificial neural network demonstrated the highest accuracy, achieving a score of 0.96 during an 8-minute test. These findings provide critical insights for designing experimental protocols in fall risk assessments using wearable technology.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1996-2003"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11008713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback","authors":"Yunjia Xia;Jianan Chen;Jinchen Li;Tingchen Gong;Ernesto E. Vidal-Rosas;Rui Loureiro;Robert J. Cooper;Hubin Zhao","doi":"10.1109/TNSRE.2025.3553794","DOIUrl":"10.1109/TNSRE.2025.3553794","url":null,"abstract":"Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system’s high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1220-1230"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Generation Rate of Speech Imagery on Neural Activity and BCI Decoding Performance: A fNIRS Study","authors":"Zengzhi Guo;Lisheng Xu;Wenjun Tan;Fei Chen","doi":"10.1109/TNSRE.2025.3552606","DOIUrl":"10.1109/TNSRE.2025.3552606","url":null,"abstract":"Brain-computer interface (BCI) enables stroke patients to actively modulate neural activity, fostering neuroplasticity and thereby accelerating the recovery process. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) has become one of the most widely used neuroimaging techniques. Current BCI research primarily focuses on improving the decoding performance. However, a key aspect of stroke rehabilitation lies in inducing stronger cortical activations in the damaged brain areas, thereby accelerating the recovery of brain functions. This study investigated the regulatory mechanism of the generation rate of speech imagery on neural activity and its impact on BCI decoding performance based on fNIRS. As the generation rate increased from 1 word/4 s to 1 word/2 s, and finally to 1 word/1 s, neural activity in speech-related brain regions steadily enhanced. Correspondingly, the accuracy of detecting speech imagery tasks increased from 83.83% to 85.39%, and ultimately showed a significant improvement, reaching 88.28%. Additionally, the differences in neural activities between the “yes” and “no” speech imagery tasks became more pronounced as the generation rate increased, leading to an improvement in classification performance from 62.81% to 65.78%, and ultimately to 67.50%. This study demonstrates that the neural activity level of most speech-related brain regions during speech imagery enhanced as the generation rate increased. Therefore, accelerating the generation rate of speech imagery induces stronger neural activity and more distinct response patterns between different tasks, which holds the potential to facilitate the development of a BCI feedback system with higher neuroplasticity induction and improved decoding performance.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1180-1190"},"PeriodicalIF":4.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10931033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Extended Windows in End-to-End Deep Learning for Improved Continuous Myoelectric Locomotion Prediction","authors":"Yuzhou Lin;Yuyang Zhang;Wenjuan Zhong;Wenxuan Xiong;Zhen Xi;Yi-Feng Chen;Mingming Zhang","doi":"10.1109/TNSRE.2025.3552530","DOIUrl":"10.1109/TNSRE.2025.3552530","url":null,"abstract":"Current surface electromyography (sEMG) methods for locomotion mode prediction face limitations in anticipatory capability due to computation delays and constrained window lengths typically below 500 ms—a practice historically tied to stationarity requirements of handcrafted feature extraction. This study investigates whether end-to-end convolutional neural networks (CNNs) processing raw sEMG signals can overcome these constraints through extended window lengths (250 ms to 1500 ms). We systematically evaluate six window lengths paired with three prediction horizons (model forecasts 50 ms to 150 ms ahead) in a continuous locomotion task involving eight modes and 16 transitions. The optimal configuration (1000 ms window with 150 ms horizon) achieved subject-average accuracies of 96.93% (steady states) and 97.50% (transient states), maintaining 95.03% and 85.53% respectively in real-time simulations. With a net averaged anticipation time of 147.9 ms after 2.1 ms computation latency, this approach demonstrates that windows covering 74% of the gait cycle can synergize with deep learning to balance the inherent trade-off between extracting richer information and maintaining system responsiveness to changes in activity.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1170-1179"},"PeriodicalIF":4.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10931022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Neuroplasticity for Post-Stroke Motor Recovery: Mechanisms, Models, and Neurotechnology","authors":"Wangwang Yan;Yuzhou Lin;Yi-Feng Chen;Yuling Wang;Jingxin Wang;Mingming Zhang","doi":"10.1109/TNSRE.2025.3551753","DOIUrl":"10.1109/TNSRE.2025.3551753","url":null,"abstract":"Stroke remains a significant global health challenge, imposing substantial socioeconomic burdens. Post-stroke neurorehabilitation aims to maximize functional recovery and mitigate persistent disability through effective neuromodulation, while many patients experience prolonged recovery periods with suboptimal outcomes. This review explores innovative neurotechnologies and therapeutic strategies enhancing neuroplasticity for post-stroke motor recovery, with a particular focus on the subacute and chronic phases. We examine key neuroplasticity mechanisms and rehabilitation models informing neurotechnology use, including the vicariation model, the interhemispheric competition model, and the bimodal balance-recovery model. Building on these theoretical foundations, current neurotechnologies are categorized into endogenous drivers of neuroplasticity (e.g., task-oriented training, brain-computer interfaces) and exogenous drivers (e.g., brain stimulation, muscular electrical stimulation, robot-assisted passive movement). However, most approaches lack tailored adjustments combining volitional behavior with brain neuromodulation. Given the heterogeneous effects of current neurotechnologies, we propose that future directions should focus on personalized rehabilitation strategies and closed-loop neuromodulation. These advanced approaches may provide deeper insights into neuroplasticity and potentially expand recovery possibilities for stroke patients.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1156-1168"},"PeriodicalIF":4.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10929703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657125","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}