Pub Date : 2024-08-28DOI: 10.1109/TNSRE.2024.3450854
Ge Gao;Xu Zhang;Xiang Chen;Zhang Chen
The objective of this work is to develop a novel myoelectric pattern recognition (MPR) method to mitigate the concurrent interference of electrode shift and loosening, thereby improving the practicality of MPR-based gestural interfaces towards intelligent control. A Siamese auto-encoder network (SAEN) was established to learn robust feature representations against random occurrences of both electrode shift and loosening. The SAEN model was trained with a variety of shifted-view and masked-view feature maps, which were simulated through feature transformation operated on the original feature maps. Specifically, three mean square error (MSE) losses were devised to warrant the trained model’s capability in adaptive recovery of any given interfered data. The SAEN was deployed as an independent feature extractor followed by a common support vector machine acting as the classifier. To evaluate the effectiveness of the proposed method, an eight-channel armband was adopted to collect surface electromyography (EMG) signals from nine subjects performing six gestures. Under the condition of concurrent interference, the proposed method achieved the highest classification accuracy in both offline and online testing compared to five common methods, with statistical significance (p <0.05). The proposed method was demonstrated to be effective in mitigating the electrode shift and loosening interferences. Our work offers a valuable solution for enhancing the robustness of myoelectric control systems.
{"title":"Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network","authors":"Ge Gao;Xu Zhang;Xiang Chen;Zhang Chen","doi":"10.1109/TNSRE.2024.3450854","DOIUrl":"10.1109/TNSRE.2024.3450854","url":null,"abstract":"The objective of this work is to develop a novel myoelectric pattern recognition (MPR) method to mitigate the concurrent interference of electrode shift and loosening, thereby improving the practicality of MPR-based gestural interfaces towards intelligent control. A Siamese auto-encoder network (SAEN) was established to learn robust feature representations against random occurrences of both electrode shift and loosening. The SAEN model was trained with a variety of shifted-view and masked-view feature maps, which were simulated through feature transformation operated on the original feature maps. Specifically, three mean square error (MSE) losses were devised to warrant the trained model’s capability in adaptive recovery of any given interfered data. The SAEN was deployed as an independent feature extractor followed by a common support vector machine acting as the classifier. To evaluate the effectiveness of the proposed method, an eight-channel armband was adopted to collect surface electromyography (EMG) signals from nine subjects performing six gestures. Under the condition of concurrent interference, the proposed method achieved the highest classification accuracy in both offline and online testing compared to five common methods, with statistical significance (p <0.05). The proposed method was demonstrated to be effective in mitigating the electrode shift and loosening interferences. Our work offers a valuable solution for enhancing the robustness of myoelectric control systems.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3388-3398"},"PeriodicalIF":4.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654774","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086101","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}
Through phase-amplitude analysis, this study investigated how low-frequency postural fluctuations interact with high-frequency scalp electroencephalography (EEG) amplitudes, shedding light on age-related mechanic differences in balance control during uneven surface navigation. Twenty young ( $24.1~pm ~1.9$ years) and twenty older adults ( $66.2~pm ~2.7$ years) stood on a training stabilometer with visual guidance, while their scalp EEG and stabilometer plate movements were monitored. In addition to analyzing the dynamics of the postural fluctuation phase, phase-amplitude coupling (PAC) for postural fluctuations below 2 Hz and within EEG sub-bands (theta: 4-7 Hz, alpha: 8-12 Hz, beta: 13-35 Hz) was calculated. The results indicated that older adults exhibited significantly larger postural fluctuation amplitudes(p <0.001)> ${p} =0.005$ ) than young adults. The PAC between postural fluctuation and theta EEG (FCz and bilateral temporal-parietal-occipital area), as well as that between postural fluctuation and alpha EEG oscillation, was lower in older adults than in young adults (p <0.05).> ${p}=0.006$ ), was higher in older adults than in young adults. In summary, the postural fluctuation phase and phase-amplitude coupling between postural fluctuation and EEG are sensitive indicators of the age-related decline in postural adjustments, reflecting less flexible motor state transitions and adaptive changes in error monitoring and visuospatial attention.
{"title":"Age-Related Topological Organization of Phase-Amplitude Coupling Between Postural Fluctuations and Scalp EEG During Unsteady Stance","authors":"Yi-Ching Chen;Yi-Ying Tsai;Wei-Min Huang;Chen-Guang Zhao;Ing-Shiou Hwang","doi":"10.1109/TNSRE.2024.3451023","DOIUrl":"10.1109/TNSRE.2024.3451023","url":null,"abstract":"Through phase-amplitude analysis, this study investigated how low-frequency postural fluctuations interact with high-frequency scalp electroencephalography (EEG) amplitudes, shedding light on age-related mechanic differences in balance control during uneven surface navigation. Twenty young (\u0000<inline-formula> <tex-math>$24.1~pm ~1.9$ </tex-math></inline-formula>\u0000 years) and twenty older adults (\u0000<inline-formula> <tex-math>$66.2~pm ~2.7$ </tex-math></inline-formula>\u0000 years) stood on a training stabilometer with visual guidance, while their scalp EEG and stabilometer plate movements were monitored. In addition to analyzing the dynamics of the postural fluctuation phase, phase-amplitude coupling (PAC) for postural fluctuations below 2 Hz and within EEG sub-bands (theta: 4-7 Hz, alpha: 8-12 Hz, beta: 13-35 Hz) was calculated. The results indicated that older adults exhibited significantly larger postural fluctuation amplitudes(p <0.001)> <tex-math>${p} =0.005$ </tex-math></inline-formula>\u0000) than young adults. The PAC between postural fluctuation and theta EEG (FCz and bilateral temporal-parietal-occipital area), as well as that between postural fluctuation and alpha EEG oscillation, was lower in older adults than in young adults (p <0.05).> <tex-math>${p}=0.006$ </tex-math></inline-formula>\u0000), was higher in older adults than in young adults. In summary, the postural fluctuation phase and phase-amplitude coupling between postural fluctuation and EEG are sensitive indicators of the age-related decline in postural adjustments, reflecting less flexible motor state transitions and adaptive changes in error monitoring and visuospatial attention.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3231-3239"},"PeriodicalIF":4.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086097","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}
Current control approaches for gross prosthetic arm movement mainly regulate movement over a continuous range of target poses. However, these methods suffer from output fluctuation caused by input signal variations during gross arm movements. Prosthesis control approaches with a finite number of discrete target poses can address this issue and reduce the complexity of the pose control process. However, it remains under-explored in the literature and suffers from the consequences of misclassifying the target poses. Here, we propose a novel Uncertainty-Aware Discrete-Target Prosthesis Control (UA-DPC) approach. This approach consists of (1) an uncertainty-aware classification scheme to reduce unintended pose switches caused by misclassifications, and (2) real-time trajectory planning that adjusts motion to be rapid or conservative based on low or high quantified uncertainty, respectively. By addressing the impact of misclassification, this approach facilitates more efficient and smooth movements. Human-in-the-loop experiments were conducted in a virtual reality environment with 12 non-disabled participants. The participants controlled a transhumeral prosthesis using three approaches: the proposed UA-DPC, a discrete-target approach based on a traditional off-the-shelf classifier, and a continuous-target approach. The results demonstrate the superior performance of UA-DPC, which provides more efficient task completion with fewer misclassification instances as well as smoother residual limb and prosthesis movement.
{"title":"Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement","authors":"Tianshi Yu;Alireza Mohammadi;Ying Tan;Peter Choong;Denny Oetomo","doi":"10.1109/TNSRE.2024.3450973","DOIUrl":"10.1109/TNSRE.2024.3450973","url":null,"abstract":"Current control approaches for gross prosthetic arm movement mainly regulate movement over a continuous range of target poses. However, these methods suffer from output fluctuation caused by input signal variations during gross arm movements. Prosthesis control approaches with a finite number of discrete target poses can address this issue and reduce the complexity of the pose control process. However, it remains under-explored in the literature and suffers from the consequences of misclassifying the target poses. Here, we propose a novel Uncertainty-Aware Discrete-Target Prosthesis Control (UA-DPC) approach. This approach consists of (1) an uncertainty-aware classification scheme to reduce unintended pose switches caused by misclassifications, and (2) real-time trajectory planning that adjusts motion to be rapid or conservative based on low or high quantified uncertainty, respectively. By addressing the impact of misclassification, this approach facilitates more efficient and smooth movements. Human-in-the-loop experiments were conducted in a virtual reality environment with 12 non-disabled participants. The participants controlled a transhumeral prosthesis using three approaches: the proposed UA-DPC, a discrete-target approach based on a traditional off-the-shelf classifier, and a continuous-target approach. The results demonstrate the superior performance of UA-DPC, which provides more efficient task completion with fewer misclassification instances as well as smoother residual limb and prosthesis movement.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3210-3221"},"PeriodicalIF":4.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086098","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 : 2024-08-27DOI: 10.1109/TNSRE.2024.3450443
Xingwei An;Yutao Zhou;Yang Di;Ying Han;Dong Ming
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer’s disease (AD). However, almost machine learning and deep learning methods are rarely analyzed from the perspective of spatial structure and temporal dimension. In order to make full use of rs-fMRI data, this study constructed a dynamic spatiotemporal graph neural network model, which mainly includes three modules: temporal block, spatial block, and graph pooling block. Our proposed model can extract the BOLD signal of the subject’s fMRI data and the spatial structure of functional connections between different brain regions, and improve the decision-making results of the model. In the study of AD, MCI and NC, the classification accuracy reached 83.78% outperforming previously reported, which manifested that our model could effectively learn spatiotemporal, and dynamic spatio-temporal method plays an important role in identifying different groups of subjects. In summary, this paper proposed an end-to-end dynamic spatio-temporal graph neural network model, which uses the information of the temporal dimension and spatial structure in rs-fMRI data, and achieves the improvement of the three classification performance among AD, MCI and NC.
{"title":"A Novel Method to Identify Mild Cognitive Impairment Using Dynamic Spatio-Temporal Graph Neural Network","authors":"Xingwei An;Yutao Zhou;Yang Di;Ying Han;Dong Ming","doi":"10.1109/TNSRE.2024.3450443","DOIUrl":"10.1109/TNSRE.2024.3450443","url":null,"abstract":"Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer’s disease (AD). However, almost machine learning and deep learning methods are rarely analyzed from the perspective of spatial structure and temporal dimension. In order to make full use of rs-fMRI data, this study constructed a dynamic spatiotemporal graph neural network model, which mainly includes three modules: temporal block, spatial block, and graph pooling block. Our proposed model can extract the BOLD signal of the subject’s fMRI data and the spatial structure of functional connections between different brain regions, and improve the decision-making results of the model. In the study of AD, MCI and NC, the classification accuracy reached 83.78% outperforming previously reported, which manifested that our model could effectively learn spatiotemporal, and dynamic spatio-temporal method plays an important role in identifying different groups of subjects. In summary, this paper proposed an end-to-end dynamic spatio-temporal graph neural network model, which uses the information of the temporal dimension and spatial structure in rs-fMRI data, and achieves the improvement of the three classification performance among AD, MCI and NC.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3328-3337"},"PeriodicalIF":4.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10649582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142080194","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 : 2024-08-27DOI: 10.1109/TNSRE.2024.3450795
Jinzhao Zhou;Yiqun Duan;Yu-Cheng Chang;Yu-Kai Wang;Chin-Teng Lin
Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroencephalography (EEG) signals. Improving decoding performance requires the development of a more effective encoder for the EEG modality. Nonetheless, learning generalizable EEG representations remains a challenge due to the relatively small scale of existing EEG datasets. In this paper, we propose enhancing the EEG encoder to improve subsequent decoding performance. Specifically, we introduce the discrete Conformer encoder (D-Conformer) to transform EEG signals into discrete representations and bootstrap the learning process by imposing EEG-language alignment from the early training stage. The D-Conformer captures both local and global patterns from EEG signals and discretizes the EEG representation, making the representation more resilient to variations, while early-stage EEG-language alignment mitigates the limitations of small EEG datasets and facilitates the learning of the semantic representations from EEG signals. These enhancements result in improved EEG representations and decoding performance. We conducted extensive experiments and ablation studies to thoroughly evaluate the proposed method. Utilizing the D-Conformer encoder and bootstrapping training strategy, our approach demonstrates superior decoding performance across various tasks, including word-level, sentence-level, and sentiment-level decoding from EEG signals. Specifically, in word-level classification, we show that our encoding method produces more distinctive representations and higher classification performance compared to the EEG encoders from existing methods. At the sentence level, our model outperformed the baseline by 5.45%, achieving a BLEU-1 score of 42.31%. Furthermore, in sentiment classification, our model exceeded the baseline by 14%, achieving a sentiment classification accuracy of 69.3%.
{"title":"BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision","authors":"Jinzhao Zhou;Yiqun Duan;Yu-Cheng Chang;Yu-Kai Wang;Chin-Teng Lin","doi":"10.1109/TNSRE.2024.3450795","DOIUrl":"10.1109/TNSRE.2024.3450795","url":null,"abstract":"Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroencephalography (EEG) signals. Improving decoding performance requires the development of a more effective encoder for the EEG modality. Nonetheless, learning generalizable EEG representations remains a challenge due to the relatively small scale of existing EEG datasets. In this paper, we propose enhancing the EEG encoder to improve subsequent decoding performance. Specifically, we introduce the discrete Conformer encoder (D-Conformer) to transform EEG signals into discrete representations and bootstrap the learning process by imposing EEG-language alignment from the early training stage. The D-Conformer captures both local and global patterns from EEG signals and discretizes the EEG representation, making the representation more resilient to variations, while early-stage EEG-language alignment mitigates the limitations of small EEG datasets and facilitates the learning of the semantic representations from EEG signals. These enhancements result in improved EEG representations and decoding performance. We conducted extensive experiments and ablation studies to thoroughly evaluate the proposed method. Utilizing the D-Conformer encoder and bootstrapping training strategy, our approach demonstrates superior decoding performance across various tasks, including word-level, sentence-level, and sentiment-level decoding from EEG signals. Specifically, in word-level classification, we show that our encoding method produces more distinctive representations and higher classification performance compared to the EEG encoders from existing methods. At the sentence level, our model outperformed the baseline by 5.45%, achieving a BLEU-1 score of 42.31%. Furthermore, in sentiment classification, our model exceeded the baseline by 14%, achieving a sentiment classification accuracy of 69.3%.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3278-3288"},"PeriodicalIF":4.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10649644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142080195","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 : 2024-08-26DOI: 10.1109/TNSRE.2024.3450008
Tamim Ahmed;Thanassis Rikakis;Aisling Kelliher;Steven L. Wolf
The evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adapting of therapy. In this paper, clinicians rated task, segment, and composite movement feature performance for 478 videos of stroke survivors executing upper extremity therapy tasks. We used the clinician ratings to develop a Hierarchical Bayesian Model (HBM) with task, segment, and composite layers for computing the statistical relation of movement quality changes to function. The model was enhanced through a detailed correlation graph ( $Delta _{textit {HBM}}$ ) that links computationally extracted kinematics with clinician-rated composite features for different task-segment combinations. Utilizing the weights and correlation graphs, we finally derive reverse cascading probabilities of the proposed HBM from kinematics to composite features, segments, and tasks. In a test involving 98 cases where clinician ratings differed, the HBM resolved 95% of these discrepancies. The model effectively aligned kinematic data with specific task-segment combinations in over 90% of cases. Once the HBM is expanded and refined through additional data it can be used for the automated calculation of statistical relations between changes in kinematics and performance of functional tasks and the generation of therapy assessment recommendations for clinicians. While our work primarily focuses on the upper extremities of stroke survivors, the HBM can be adapted to many other neurorehabilitation contexts.
{"title":"A Hierarchical Bayesian Model for Cyber-Human Assessment of Movement in Upper Extremity Stroke Rehabilitation","authors":"Tamim Ahmed;Thanassis Rikakis;Aisling Kelliher;Steven L. Wolf","doi":"10.1109/TNSRE.2024.3450008","DOIUrl":"10.1109/TNSRE.2024.3450008","url":null,"abstract":"The evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adapting of therapy. In this paper, clinicians rated task, segment, and composite movement feature performance for 478 videos of stroke survivors executing upper extremity therapy tasks. We used the clinician ratings to develop a Hierarchical Bayesian Model (HBM) with task, segment, and composite layers for computing the statistical relation of movement quality changes to function. The model was enhanced through a detailed correlation graph (\u0000<inline-formula> <tex-math>$Delta _{textit {HBM}}$ </tex-math></inline-formula>\u0000) that links computationally extracted kinematics with clinician-rated composite features for different task-segment combinations. Utilizing the weights and correlation graphs, we finally derive reverse cascading probabilities of the proposed HBM from kinematics to composite features, segments, and tasks. In a test involving 98 cases where clinician ratings differed, the HBM resolved 95% of these discrepancies. The model effectively aligned kinematic data with specific task-segment combinations in over 90% of cases. Once the HBM is expanded and refined through additional data it can be used for the automated calculation of statistical relations between changes in kinematics and performance of functional tasks and the generation of therapy assessment recommendations for clinicians. While our work primarily focuses on the upper extremities of stroke survivors, the HBM can be adapted to many other neurorehabilitation contexts.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3157-3166"},"PeriodicalIF":4.8,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648760","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142072700","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}
Hand motor impairment has seriously affected the daily life of the elderly. We developed an electromyography (EMG) exosuit system with bidirectional hand support for bilateral coordination assistance based on a dynamic gesture recognition model using graph convolutional network (GCN) and long short-term memory network (LSTM). The system included a hardware subsystem and a software subsystem. The hardware subsystem included an exosuit jacket, a backpack module, an EMG recognition module, and a bidirectional support glove. The software subsystem based on the dynamic gesture recognition model was designed to identify dynamic and static gestures by extracting the spatio-temporal features of the patient’s EMG signals and to control glove movement. The offline training experiment built the gesture recognition models for each subject and evaluated the feasibility of the recognition model; the online control experiments verified the effectiveness of the exosuit system. The experimental results showed that the proposed model achieve a gesture recognition rate of 96.42% $pm ~3.26$ %, which is higher than the other three traditional recognition models. All subjects successfully completed two daily tasks within a short time and the success rate of bilateral coordination assistance are 88.75% and 86.88%. The exosuit system can effectively help patients by bidirectional hand support strategy for bilateral coordination assistance in daily tasks, and the proposed method can be applied to various limb assistance scenarios.
{"title":"An Exosuit System With Bidirectional Hand Support for Bilateral Assistance Based on Dynamic Gesture Recognition","authors":"Zhichuan Tang;Zhihao Zhu;Shengye Lv;Xuanyu Hong;Yuxin Peng;Nuo Chen","doi":"10.1109/TNSRE.2024.3449338","DOIUrl":"10.1109/TNSRE.2024.3449338","url":null,"abstract":"Hand motor impairment has seriously affected the daily life of the elderly. We developed an electromyography (EMG) exosuit system with bidirectional hand support for bilateral coordination assistance based on a dynamic gesture recognition model using graph convolutional network (GCN) and long short-term memory network (LSTM). The system included a hardware subsystem and a software subsystem. The hardware subsystem included an exosuit jacket, a backpack module, an EMG recognition module, and a bidirectional support glove. The software subsystem based on the dynamic gesture recognition model was designed to identify dynamic and static gestures by extracting the spatio-temporal features of the patient’s EMG signals and to control glove movement. The offline training experiment built the gesture recognition models for each subject and evaluated the feasibility of the recognition model; the online control experiments verified the effectiveness of the exosuit system. The experimental results showed that the proposed model achieve a gesture recognition rate of 96.42% \u0000<inline-formula> <tex-math>$pm ~3.26$ </tex-math></inline-formula>\u0000%, which is higher than the other three traditional recognition models. All subjects successfully completed two daily tasks within a short time and the success rate of bilateral coordination assistance are 88.75% and 86.88%. The exosuit system can effectively help patients by bidirectional hand support strategy for bilateral coordination assistance in daily tasks, and the proposed method can be applied to various limb assistance scenarios.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3147-3156"},"PeriodicalIF":4.8,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142072702","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}
Dopaminergic treatment has proved effective to Parkinson’s disease (PD), but the conventional treatment assessment is human-administered and prone to intra- and inter-assessor variability. In this paper, we propose a knowledge-driven framework and discover that brain ACtivation-Transition-Spectrum (ACTS) features achieve effective quantified assessments of dopaminergic therapy in PD. Firstly, brain activities of fifty-one PD patients during clinical walking tests under the OFF and ON states (without and with dopaminergic medication) were measured with functional near-infrared spectroscopy (fNIRS). Then, brain ACTS features were formulated based on the medication-induced variations in temporal features of brain regional activation, transition features of brain hemodynamic states, and graph spectrum of brain functional connectivity. Afterwards, a prior selection algorithm was constructed based on recursive feature elimination and graph spectrum analysis for the selection of principal discriminative features. Further, linear discriminant analysis was conducted to predict the treatment-induced improvements. The results demonstrated that the proposed method decreased the misclassification probability from 42% to 16% in the evaluations of dopaminergic treatment and outperformed existing fNIRS-based methods. Therefore, the proposed method promises a quantified and objective approach for dopaminergic treatment assessment, and our brain ACTS features may serve as promising functional biomarkers for treatment evaluation.
{"title":"A Knowledge-Driven Framework Discovers Brain ACtivation-Transition-Spectrum (ACTS) Features for Parkinson’s Disease","authors":"Jiewei Lu;Jin Wang;Yuanyuan Cheng;Zhilin Shu;Yue Wang;Xinyuan Zhang;Zhizhong Zhu;Yang Yu;Jialing Wu;Jianda Han;Ningbo Yu","doi":"10.1109/TNSRE.2024.3449316","DOIUrl":"10.1109/TNSRE.2024.3449316","url":null,"abstract":"Dopaminergic treatment has proved effective to Parkinson’s disease (PD), but the conventional treatment assessment is human-administered and prone to intra- and inter-assessor variability. In this paper, we propose a knowledge-driven framework and discover that brain ACtivation-Transition-Spectrum (ACTS) features achieve effective quantified assessments of dopaminergic therapy in PD. Firstly, brain activities of fifty-one PD patients during clinical walking tests under the OFF and ON states (without and with dopaminergic medication) were measured with functional near-infrared spectroscopy (fNIRS). Then, brain ACTS features were formulated based on the medication-induced variations in temporal features of brain regional activation, transition features of brain hemodynamic states, and graph spectrum of brain functional connectivity. Afterwards, a prior selection algorithm was constructed based on recursive feature elimination and graph spectrum analysis for the selection of principal discriminative features. Further, linear discriminant analysis was conducted to predict the treatment-induced improvements. The results demonstrated that the proposed method decreased the misclassification probability from 42% to 16% in the evaluations of dopaminergic treatment and outperformed existing fNIRS-based methods. Therefore, the proposed method promises a quantified and objective approach for dopaminergic treatment assessment, and our brain ACTS features may serve as promising functional biomarkers for treatment evaluation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3135-3146"},"PeriodicalIF":4.8,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142072701","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 : 2024-08-26DOI: 10.1109/TNSRE.2024.3449706
Junhao Zhang;Peter H. Veltink;Edwin H. F. van Asseldonk
Accurately estimating whole-body angular momentum (WBAM) during daily activities may benefit from choosing a locally-defined reference frame aligned with anatomical axes, particularly during activities involving body turns. Local reference frames, potentially defined by pelvis heading angles, horizontal center of mass velocity (vCoM), or average angular velocity ( ${A}omega $ ), can be utilized. To minimize the impact of inherent mediolateral oscillations of these frames, such as those caused by pelvis or vCoM rotation in the transverse plane, a low-pass filter is recommended. This study investigates how differences among global, local reference frames pre- and post-filtering affect WBAM component distribution across anatomical axes during straight-line walking and various turning tasks, which is lacking in the literature. Results highlighted significant effects of reference frame choice on WBAM distribution in the anteroposterior (AP) and mediolateral (ML) axes in all tasks. Specifically, expressing WBAM in the vCoM-oriented local reference frame yielded significantly lower (or higher) WBAM in the AP (or ML) axes compared to pelvis-oriented and ${A}omega $ -oriented frames. However, these significant differences disappeared after employing a low-pass filter to local reference frames. Therefore, employing low-pass filtered local reference frames is crucial to enhance their applicability in both straight-line and turning tasks, ensuring more precise WBAM estimates. In applications that require expressing anatomical axes-dependent biomechanical parameters in a local reference frame, pelvis- and vCoM-oriented frames are more practical compared to the A $omega $ -oriented frame, as they can be determined by a reduced optical marker set or inertial sensors in future applications when the whole-body kinematics is not available.
{"title":"Reframing Whole-Body Angular Momentum: Exploring the Impact of Low-Pass Filtered Dynamic Local Reference Frames During Straight-Line and Turning Gaits","authors":"Junhao Zhang;Peter H. Veltink;Edwin H. F. van Asseldonk","doi":"10.1109/TNSRE.2024.3449706","DOIUrl":"10.1109/TNSRE.2024.3449706","url":null,"abstract":"Accurately estimating whole-body angular momentum (WBAM) during daily activities may benefit from choosing a locally-defined reference frame aligned with anatomical axes, particularly during activities involving body turns. Local reference frames, potentially defined by pelvis heading angles, horizontal center of mass velocity (vCoM), or average angular velocity (\u0000<inline-formula> <tex-math>${A}omega $ </tex-math></inline-formula>\u0000), can be utilized. To minimize the impact of inherent mediolateral oscillations of these frames, such as those caused by pelvis or vCoM rotation in the transverse plane, a low-pass filter is recommended. This study investigates how differences among global, local reference frames pre- and post-filtering affect WBAM component distribution across anatomical axes during straight-line walking and various turning tasks, which is lacking in the literature. Results highlighted significant effects of reference frame choice on WBAM distribution in the anteroposterior (AP) and mediolateral (ML) axes in all tasks. Specifically, expressing WBAM in the vCoM-oriented local reference frame yielded significantly lower (or higher) WBAM in the AP (or ML) axes compared to pelvis-oriented and \u0000<inline-formula> <tex-math>${A}omega $ </tex-math></inline-formula>\u0000-oriented frames. However, these significant differences disappeared after employing a low-pass filter to local reference frames. Therefore, employing low-pass filtered local reference frames is crucial to enhance their applicability in both straight-line and turning tasks, ensuring more precise WBAM estimates. In applications that require expressing anatomical axes-dependent biomechanical parameters in a local reference frame, pelvis- and vCoM-oriented frames are more practical compared to the A\u0000<inline-formula> <tex-math>$omega $ </tex-math></inline-formula>\u0000-oriented frame, as they can be determined by a reduced optical marker set or inertial sensors in future applications when the whole-body kinematics is not available.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3167-3178"},"PeriodicalIF":4.8,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142072703","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 : 2024-08-22DOI: 10.1109/TNSRE.2024.3447791
Alex Bersani;Mercy Amankwah;Daniela Calvetti;Erkki Somersalo;Marco Viceconti;Giorgio Davico
The inherent redundancy of the musculoskeletal systems is traditionally solved by optimizing a cost function. This approach may not be correct to model non-adult or pathological populations likely to adopt a “non-optimal” motor control strategy. Over the years, various methods have been developed to address this limitation, such as the stochastic approach. A well-known implementation of this approach, Metabolica, samples a wide number of plausible solutions instead of searching for a single one, leveraging Bayesian statistics and Markov Chain Monte Carlo algorithm, yet allowing muscles to abruptly change their activation levels. To overcome this and other limitations, we developed a new implementation of the stochastic approach (Myobolica), adding constraints and parameters to ensure the identification of physiological solutions. The aim of this study was to evaluate Myobolica, and quantify the differences in terms of width of the solution band (muscle control variability) compared to Metabolica. To this end, both muscle forces and knee joint force solutions bands estimated by the two approaches were compared to one another, and against (i) the solution identified by static optimization and (ii) experimentally measured knee joint forces. The use of Myobolica led to a marked narrowing of the solution band compared to Metabolica. Furthermore, the Myobolica solutions well correlated with the experimental data (R $^{{2}} = 0.92$ , RMSE = 0.3 BW), but not as much with the optimal solution (R $^{{2}} = 0.82$ , RMSE = 0.63 BW). Additional analyses are required to confirm the findings and further improve this implementation.
{"title":"Myobolica: A Stochastic Approach to Estimate Physiological Muscle Control Variability","authors":"Alex Bersani;Mercy Amankwah;Daniela Calvetti;Erkki Somersalo;Marco Viceconti;Giorgio Davico","doi":"10.1109/TNSRE.2024.3447791","DOIUrl":"10.1109/TNSRE.2024.3447791","url":null,"abstract":"The inherent redundancy of the musculoskeletal systems is traditionally solved by optimizing a cost function. This approach may not be correct to model non-adult or pathological populations likely to adopt a “non-optimal” motor control strategy. Over the years, various methods have been developed to address this limitation, such as the stochastic approach. A well-known implementation of this approach, Metabolica, samples a wide number of plausible solutions instead of searching for a single one, leveraging Bayesian statistics and Markov Chain Monte Carlo algorithm, yet allowing muscles to abruptly change their activation levels. To overcome this and other limitations, we developed a new implementation of the stochastic approach (Myobolica), adding constraints and parameters to ensure the identification of physiological solutions. The aim of this study was to evaluate Myobolica, and quantify the differences in terms of width of the solution band (muscle control variability) compared to Metabolica. To this end, both muscle forces and knee joint force solutions bands estimated by the two approaches were compared to one another, and against (i) the solution identified by static optimization and (ii) experimentally measured knee joint forces. The use of Myobolica led to a marked narrowing of the solution band compared to Metabolica. Furthermore, the Myobolica solutions well correlated with the experimental data (R\u0000<inline-formula> <tex-math>$^{{2}} = 0.92$ </tex-math></inline-formula>\u0000, RMSE = 0.3 BW), but not as much with the optimal solution (R\u0000<inline-formula> <tex-math>$^{{2}} = 0.82$ </tex-math></inline-formula>\u0000, RMSE = 0.63 BW). Additional analyses are required to confirm the findings and further improve this implementation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3270-3277"},"PeriodicalIF":4.8,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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