IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Daily Assistance for Amyotrophic Lateral Sclerosis Patients Based on a Wearable Multimodal Brain-Computer Interface Mouse","authors":"Ya Jiang;Kendi Li;Yuankai Liang;Di Chen;Mingkui Tan;Yuanqing Li","doi":"10.1109/TNSRE.2024.3520984","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3520984","url":null,"abstract":"Amyotrophic lateral sclerosis (ALS) is a chronic, progressive neurodegenerative disease that mainly causes damage to upper and lower motor neurons. This leads to a progressive deterioration in the voluntary mobility of the upper and lower extremities in ALS patients, which underscores the pressing need for an assistance system to facilitate communication and body movement without relying on neuromuscular function. In this paper, we developed a daily assistance system for ALS patients based on a wearable multimodal brain-computer interface (BCI) mouse. The system comprises two subsystems: a mouse system assisting the upper extremity and a wheelchair system based on the mouse system assisting the lower extremity. By wearing a BCI headband, ALS patients can control a computer cursor on the screen with slight head rotation and eye blinking, and further operate a computer and drive a wheelchair with specially designed graphical user interfaces (GUIs). We designed operating tasks that simulate daily needs and invited ALS patients to perform the tasks. In total, 15 patients with upper extremity limitations performed the mouse system task and 9 patients with lower extremity mobility issues performed the wheelchair system task. To our satisfaction, all the participants fully accomplished the tasks and average accuracies of 83.9% and 87.0% for the two tasks were achieved. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate that the proposed system provides ALS patients with effective daily assistance and shows promising long-term application prospects.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"150-161"},"PeriodicalIF":4.8,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10811971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905892","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":"Deep Learning-Based Post-Stroke Myoelectric Gesture Recognition: From Feature Construction to Network Design","authors":"Tianzhe Bao;Zhiyuan Lu;Ping Zhou","doi":"10.1109/TNSRE.2024.3521583","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3521583","url":null,"abstract":"Recently, robot-assisted rehabilitation has emerged as a promising solution to increase the training intensity of stroke patients while reducing workload on therapists, whilst surface electromyography (sEMG) is expected to serve as a viable control source. In this paper, we delve into the potential of deep learning (DL) for post-stroke hand gesture recognition by collecting the sEMG signals of eight chronic stroke subjects, focusing on three primary aspects: feature domains of sEMG (time, frequency, and wavelet), data structures (one or two-dimensional images), and neural network architectures (CNN, CNN-LSTM, and CNN-LSTM-Attention). A total of 18 DL models were comprehensively evaluated in both intra-subject testing and inter-subject transfer learning tasks, with two post-processing algorithms (Model Voting and Bayesian Fusion) analysed subsequently. Experiment results infer that for intra-subject testing, the average accuracy of CNN-LSTM using two-dimensional frequency features is the highest, reaching 72.95%. For inter-subject transfer learning, the average accuracy of CNN-LSTM-Attention using one-dimensional frequency features is the highest, reaching 68.38%. Through these two experiments, it was found that frequency features had significant advantages over other features in gesture recognition after stroke. Moreover, the post-processing algorithm can further improve the recognition accuracy, and the recognition effect can be increased by 2.03% through the model voting algorithm.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"191-200"},"PeriodicalIF":4.8,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918342","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":"Improving Electromyography Electrode Placement Accuracy in Transtibial Amputees: A Comparative Study of Ultrasound and Palpation Methods","authors":"Faranak Rostamjoud;Friðrika Björk Þorkelsdóttir;Atli Örn Sverrisson;Sigurður Brynjólfsson;Kristín Briem","doi":"10.1109/TNSRE.2024.3520720","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3520720","url":null,"abstract":"In the past decade, significant focus has been on electromyography (EMG) control of prostheses in transtibial amputees (TTAs). Reliable signal acquisition requires accurate EMG electrode placement. Conventional electrode placement methods are challenging due to altered post-surgical anatomy. This study investigated the application of ultrasound imaging for placement of EMG electrodes in TTAs. Four residual limb muscles, Tibialis Anterior (TA), Peroneus Longus (PL), Gastrocnemius Medial (GM), and Gastrocnemius Lateral (GL), were examined in 9 unilateral TTAs. Ultrasound was used to identify each muscle belly’s thickest part and fiber orientation. A Certified Prosthetist Orthotist (CPO) then performed palpation to identify muscle bellies, blinded to ultrasound findings. Distances between ultrasound- and palpation-identified spots were measured. EMG data were contrasted between methods in terms of root mean square (RMS) amplitude and signal-to-noise ratio (SNR). The results indicated that Ultrasound-guided placement produced slightly higher, though non-significant, signal amplitudes (p =0.06) and significantly higher SNR (p =0.04). Moreover, palpation misidentified muscles in four cases. In 72.2% of cases, the distance between ultrasound- and palpation-identified spots was more than 10 mm. The mean distance was the greatest for PL and GL. Relying on palpation to identify PL and TA in TTAs may provide irrelevant EMG due to erroneous placement. Using ultrasound imaging can avoid this and, in addition to accurate muscle identification, may improve signal amplitude and SNR. In conclusion, ultrasound imaging is a valuable tool for enhancing the accuracy of EMG electrode placement in TTAs, which may lead to better prosthetic control outcomes.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"133-139"},"PeriodicalIF":4.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10810499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912518","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":"End-to-End Mandarin Speech Reconstruction Based on Ultrasound Tongue Images Using Deep Learning","authors":"Fengji Li;Fei Shen;Ding Ma;Jie Zhou;Shaochuan Zhang;Li Wang;Fan Fan;Tao Liu;Xiaohong Chen;Tomoki Toda;Haijun Niu","doi":"10.1109/TNSRE.2024.3520498","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3520498","url":null,"abstract":"The loss of speech function following a laryngectomy usually leads to severe physiological and psychological distress for laryngectomees. In clinical practice, most laryngectomees retain intact upper tract articulatory organs, emphasizing the significance of speech rehabilitation that utilizes articulatory motion information to effectively restore speech. This study proposed a deep learning-based end-to-end method for speech reconstruction using ultrasound tongue images. Initially, ultrasound tongue images and speech data were collected simultaneously with a designed Mandarin corpus. Subsequently, a speech reconstruction model was built based on adversarial neural networks. The model includes a pretrained feature extractor to process ultrasound images, an upsampling block to generate speech, and discriminators to ensure the similarity and fidelity of the reconstructed speech. Finally, both objective and subjective evaluations were conducted for the reconstructed speech. The reconstructed speech demonstrated high intelligibility in both Mandarin phonemes and tones. The character error rate of phonemes in automatic speech recognition was 0.2605, and tone error rate obtained from dictation tests was 0.1784, respectively. Objective results showed high similarity between the reconstructed and ground truth speech. Subjective perception results also indicated an acceptable level of naturalness. The proposed method demonstrates its capability to reconstruct tonal Mandarin speech from ultrasound tongue images. However, future research should concentrate on specific conditions of laryngectomees, aiming to enhance and optimize model performance. This will be achieved by enlarging training datasets, investigating the impact of ultrasound tongue imaging parameters, and further refining this method.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"140-149"},"PeriodicalIF":4.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10810495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905886","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":"Biomechanical Modeling and Evaluation of Buttocks Automatic Assisted Repositioning in Bedridden Patients","authors":"Liqing Yang;Ganjun Song;Di Luo;Haotian Xu;Jiamei Han;Mingzhao Xiao;Dingqun Bai;Shan Tian;Wensheng Hou;Jisi Tang;Xin Zhang;Lin Chen","doi":"10.1109/TNSRE.2024.3520146","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3520146","url":null,"abstract":"Pressure ulcers (PUs) pose a significant challenge in the care of bedridden patients, to which automated tilt nursing beds have emerged as a promising solution. However, the lack of effective models to elucidate the mechanical responses of deep tissue during assisted repositioning and identify the optimal tilt angle has hindered the implementation of effective automatic assisted repositioning systems for long-term care patients. Therefore, this study developed a novel computational model that integrates the buttocks with a support mattress to simulate automatic assisted repositioning, thereby analyzing deep tissue responses and optimizing tilt angles for effective load offloading. Inverse modeling was used to reconstruct the 3D shape of the buttocks, nodal equivalence techniques were employed to simplify the mesh and accurately represent internal tissue contacts, and soft tissue parameters were optimized using Response Surface Methodology (RSM). Finally, finite element (FE) analysis was conducted to evaluate the biomechanical responses and optimize the repositioning strategies. Model validation demonstrated a deformation error of \u0000<inline-formula> <tex-math>$6.93~pm ~7.41$ </tex-math></inline-formula>\u0000 mm (mean ± standard deviation) and interface pressure differences within 22.4%, demonstrating the efficacy and bio-fidelity of the system. Repositioning simulations at angles from 0° to 30° showed a 20% reduction in total soft tissue strain, with peak equivalent stress decreasing by 22.27% at the mattress- to-buttock interface and by 20.43% at the muscle-to-adipose tissue interface. These simulations suggest that a 30° turning angle is beneficial for alleviating pressure concentration, which may inspire the design and optimization of automatic assisted repositioning strategies in rehabilitation practices.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"175-182"},"PeriodicalIF":4.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918341","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":"Does Proprioceptive Impairment Affect Feedforward Motor Control? A Cross-Sectional Study on Patients With Brain Damage","authors":"Nicola Valè;Anna Righetti;Enrico Martini;Michele Boldo;Nicola Bombieri;Nicola Smania","doi":"10.1109/TNSRE.2024.3518416","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3518416","url":null,"abstract":"Sensory ataxia and cerebellar ataxia share common manifestations including dysmetria, intentional tremor and lack of smoothness. We formulated a theoretical framework to describe the patients’ sensory and cerebellar ataxic behavior as consequences of a forward model impairment. To test this framework, the present study aimed to compare upper limb movement kinematics in an index-to-nose task between three groups: healthy controls, people with CNS focal lesions and cerebellar deficits and people with CNS focal lesions and somatosensory impairment. We recruited 12 healthy controls (age \u0000<inline-formula> <tex-math>$= 29.0pm 2.9$ </tex-math></inline-formula>\u0000 years, female = 5) and 20 participants with focal CNS lesions. We divided the sample according to the lesion site in participants with lesions in areas involved in the somatosensory information processing (n = 12, age \u0000<inline-formula> <tex-math>$= 62.4pm 13.6$ </tex-math></inline-formula>\u0000 years, female = 5) and participants with lesions in the cerebellum or cerebellar peduncle (n = 8, age \u0000<inline-formula> <tex-math>$= 64.3pm 13.9$ </tex-math></inline-formula>\u0000 years, female = 1). Movement features concerning movement efficiency (average velocity, peak velocity), accuracy (spatial error when pointing to the nose) and motor planning (timing and spatial occurrence of velocity peak, velocity and deviation from ideal trajectory at 150ms after the movement onset) were computed. Both the groups of participants with CNS lesions performed the movement slower than healthy controls. When comparing results from the two groups of patients, we showed that participants with cerebellar lesions were characterized by greater trial-to-trial variability of the velocity peak (repeated measure ANOVA group effect: F = 5.242, p = 0.012) and its timing (condition*group interaction: F = 5.38, p = 0.011). Our findings suggested that both participants with cerebellar and somatosensory deficits showed signs of anticipatory motor control impairment.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"113-121"},"PeriodicalIF":4.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10802931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880407","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":">IEEE Transactions on Neural Systems and Rehabilitation Engineering information for authors","authors":"","doi":"10.1109/TNSRE.2024.3514413","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3514413","url":null,"abstract":"","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"C3-C3"},"PeriodicalIF":4.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803917","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844213","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":"IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information","authors":"","doi":"10.1109/TNSRE.2024.3514395","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3514395","url":null,"abstract":"","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"C2-C2"},"PeriodicalIF":4.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803916","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844480","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":"Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm","authors":"Ying Zhang;Yaping Huang;Jiansong Qi;Sihui Zhang;Mei Tian;Yi Tian;Fanchao Meng;Lin Guan;Tianyi Chang","doi":"10.1109/TNSRE.2024.3518693","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3518693","url":null,"abstract":"Eye-tracking technology is found effective in revealing the specific visual preference of Autism Spectrum Disorder (ASD) which can be characterized by high systemizing and low empathizing abilities. Early diagnosis is vital for ASD’s subsequent treatment. However, existing eye-tracking-based methods suffer from long diagnostic times and low diagnostic accuracy due to the lack of awareness of gaze preference derived from individual differences. Moreover, there is only one publicly available eye-tracking dataset that employs a simple image free-viewing paradigm to collect the gaze patterns of ASD and typically developed (TD) subjects with an average age of 8 years, thus can not effectively support the early diagnosis for preschool children. To tackle the difficulties, in this paper, we first propose an Uncertainty-inspired ASD Screening Network (UASN) that dynamically estimates the contribution of each stimulus viewed by different subjects, and secondly, we design a contrastive image-viewing paradigm and further collect eye movement data from preschool children to reveal the visual behaviors of ASD children accordingly. Specifically, in UASN, we estimate the uncertainty of each stimulus and use it for more efficient model training and a more simplified personalized diagnosis procedure. Besides, by synthesizing two images with the opposite semantic representations and recruiting ASD and TD subjects aged 2-6, we construct a new CI4ASD dataset, which offers a novel contrastive image-viewing paradigm for better diagnosis of ASD in children. Comprehensive experiments are conducted and results have evidenced the effectiveness of the proposed UASN and eye-tracking paradigm.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"122-132"},"PeriodicalIF":4.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10804194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890376","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":"Screen Guided Training Does Not Capture Goal-Oriented Behaviors: Learning Myoelectric Control Mappings From Scratch Using Context Informed Incremental Learning","authors":"Evan Campbell;Ethan Eddy;Xavier Isabel;Scott Bateman;Benoit Gosselin;Ulysse Côté-Allard;Erik Scheme","doi":"10.1109/TNSRE.2024.3518059","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3518059","url":null,"abstract":"Human-machine interfaces based on myoelectric signals typically use screen-guided training (SGT) for model calibration, but this approach fails to capture realistic user behaviors. This study evaluates a user-in-the-loop context-informed incremental learning (CIIL) framework, comparing SGT, SGT followed by CIIL adaptation (SGT-A), and a novel zero-shot adaptation (ZS-A) CIIL approach that begins adapting with no prior training. Sixteen participants completed a Fitts’ Law targeting task using these control schemes, with performance measured via online throughput and offline classification accuracy. Despite lower offline accuracy, the ZS-A model achieved the highest online throughput (\u0000<inline-formula> <tex-math>$1.47~pm ~0.46$ </tex-math></inline-formula>\u0000 bits/s), significantly outperforming the SGT baseline (\u0000<inline-formula> <tex-math>$1.15~pm ~0.37$ </tex-math></inline-formula>\u0000 bits/s) and reached competitive performance within 200 seconds. To further enhance control performance, a novel adaptive sigmoid-based proportional control mapping was introduced, dynamically adjusting control signals to allow precise control near neutral positions and rapid movements at higher activation levels, better aligning with natural user behaviors. These findings demonstrate that CIIL can surpass traditional SGT methods in online performance and emphasize the value of real-time user-in-the-loop data for developing adaptable and intuitive myoelectric interfaces, with implications for prosthetics, rehabilitation, and telerobotics.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"332-342"},"PeriodicalIF":4.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10802919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962837","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}