目的:探索帕金森病伴冻结步态患者胫骨前肌和腓肠肌在直线行走过程中表面肌电(surface electromyogram,sEMG)的改变及其与临床特征之间的相关性。方法:选取符合入选标准的12例帕金森病伴冻结步态患者、13例帕金森病不伴冻结步态患者和1...目的:探索帕金森病伴冻结步态患者胫骨前肌和腓肠肌在直线行走过程中表面肌电(surface electromyogram,sEMG)的改变及其与临床特征之间的相关性。方法:选取符合入选标准的12例帕金森病伴冻结步态患者、13例帕金森病不伴冻结步态患者和11例健康对照受试者接受临床特征、步态时空参数和直线行走sEMG评估。分析步态周期各时段中重症侧胫骨前肌和腓肠肌内侧头的sEMG信号特征改变,指标选用标准化均方根振幅(root mean square,RMS)值和共激活比值。同时,探索sEMG改变与临床特征之间的相关性。结果:与健康受试者和非冻结步态患者相比,冻结步态患者的步速减慢、步幅缩短、摆动相减少、步态变异性增加(P<0.05)。在步态周期的单支撑相阶段,冻结步态患者胫骨前肌标准化RMS较健康对照降低(P<0.05);在摆动前期,冻结步态患者胫骨前肌标准化RMS较非冻结步态患者显著下降(P<0.01),但非冻结步态患者胫骨前肌标准化RMS较健康对照增加(P<0.01)。对于腓肠肌标准化RMS,冻结步态患者在摆动前期较非冻结步态患者和健康对照均显著降低(P<0.05)。此外,冻结步态患者的胫骨前肌-腓肠肌共激活比值在摆动相较非冻结步态患者降低(P<0.05)。冻结步态患者摆动前期腓肠肌标准化RMS与冻结步态严重程度(r=-0.758,P=0.007)、摆动相共激活比值和步幅变异性(r=0.716,P=0.013)显著相关。结论:直线行走步态周期中摆动前期胫骨前肌和腓肠肌的sEMG活动下降、摆动相胫骨前肌-腓肠肌共激活比值降低是帕金森病冻结步态患者的重要特征。展开更多
The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Ef...The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.展开更多
For many years, nerve transfer has been commonly used as a treatment option following peripheral nerve injury, although the precise mechanism underlying successful nerve transfer is not yet clear. We developed an anim...For many years, nerve transfer has been commonly used as a treatment option following peripheral nerve injury, although the precise mechanism underlying successful nerve transfer is not yet clear. We developed an animal model to investigate the mechanism underlying nerve transfer between branches of the spinal accessory nerve (Ac) and suprascapular nerve (Ss) in rats, so that we could observe changes in the number of motor neurons, investigate the 3-dimensional localization of neurons in the anterior horn of the spinal cord, and perform an electromyogram (EMG) of the supraspinatus muscle before and after nerve transfer treatment. The present experiment showed a clear reduction in the number of γ motor neurons. The distributional portion of motor neurons following nerve transfer was mainly within the neuron column innervating the trapezius. Some neurons innervating the supraspinatus muscle also survived post-transfer. Compared with the non-operated group, the EMG restoration rate of the supraspinatus muscle following nerve transfer was 60% in the experimental group and 80% in a surgical control group. Following nerve transfer, there was a distinct reduction in the number of γ motor neurons. Therefore, γ motor neurons may have important effects on the recovery of muscular strength following nerve transfer. Moreover, because the neurons located in regions innervating either the trapezius or supraspinatus muscle were labeled after Ac transfer to Ss, we also suggest that indistinct axon regeneration mechanisms exist in the spinal cord following peripheral nerve transfer.展开更多
As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts h...As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts have focused on leveraging electromyogram(EMG)for personal recognition,aiming to address security concerns.However,obtaining consistent EMG signals from the same individual is inherently challenging,resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition.Notably,conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities.This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary classifier,effectively mitigating the impact of data irregularities in EMG-based recognition.The proposed method employs empirical mode decomposition(EMD)to extract distinctive features.The model comprises two sub-networks designed to follow the siamese network architecture and a decision network integrated with the novel auxiliary classifier,specifically designed to address data irregularities.The two sub-networks sharing a weight calculate the compatibility function.The auxiliary classifier collaborates with a neural network to implement an attention mechanism.The attention mechanism using the auxiliary classifier solves the data irregularity problem by improving the importance of the EMG gesture section.Experimental results validated the efficacy of the proposed personal recognition method,achieving a remarkable 94.35%accuracy involving 100 subjects from the multisession CU_sEMG database(DB).This performance outperforms the existing approaches by 3%,employing auxiliary classifiers.Furthermore,an additional experiment yielded an improvement of over 0.85%of Ninapro DB,3%of CU_sEMG DB compared to the existing EMG-based recognition methods.Consequently,the proposed personal recognition using EMG proves to secure IoT devices,offering robustness against data irregularities.展开更多
As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal reco...As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal recognitionmethod used for granting permission is a password-basedmethod,which causes security problems.Therefore,personal recognition studies using bio-signals are being conducted as a method to access control to devices.Among bio-signal,surface electromyogram(sEMG)can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation.Furthermore,as an advantage,sEMG can be conveniently measured from arms and legs.This paper proposes a personal recognition method using sEMG,based on a multi-stream convolutional neural network(CNN).The proposed method decomposes sEMG signals into intrinsic mode functions(IMF)using empirical mode decomposition(EMD)and transforms each IMF into a spectrogram.Personal recognition is performed by analyzing time–frequency features from the spectrogram transformed intomulti-streamCNN.The database(DB)adopted in this paper is the Ninapro DB,which is a benchmark EMG DB.The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%,compared with the singlestream CNN using the spectrogram of raw sEMG.展开更多
文摘目的:探索帕金森病伴冻结步态患者胫骨前肌和腓肠肌在直线行走过程中表面肌电(surface electromyogram,sEMG)的改变及其与临床特征之间的相关性。方法:选取符合入选标准的12例帕金森病伴冻结步态患者、13例帕金森病不伴冻结步态患者和11例健康对照受试者接受临床特征、步态时空参数和直线行走sEMG评估。分析步态周期各时段中重症侧胫骨前肌和腓肠肌内侧头的sEMG信号特征改变,指标选用标准化均方根振幅(root mean square,RMS)值和共激活比值。同时,探索sEMG改变与临床特征之间的相关性。结果:与健康受试者和非冻结步态患者相比,冻结步态患者的步速减慢、步幅缩短、摆动相减少、步态变异性增加(P<0.05)。在步态周期的单支撑相阶段,冻结步态患者胫骨前肌标准化RMS较健康对照降低(P<0.05);在摆动前期,冻结步态患者胫骨前肌标准化RMS较非冻结步态患者显著下降(P<0.01),但非冻结步态患者胫骨前肌标准化RMS较健康对照增加(P<0.01)。对于腓肠肌标准化RMS,冻结步态患者在摆动前期较非冻结步态患者和健康对照均显著降低(P<0.05)。此外,冻结步态患者的胫骨前肌-腓肠肌共激活比值在摆动相较非冻结步态患者降低(P<0.05)。冻结步态患者摆动前期腓肠肌标准化RMS与冻结步态严重程度(r=-0.758,P=0.007)、摆动相共激活比值和步幅变异性(r=0.716,P=0.013)显著相关。结论:直线行走步态周期中摆动前期胫骨前肌和腓肠肌的sEMG活动下降、摆动相胫骨前肌-腓肠肌共激活比值降低是帕金森病冻结步态患者的重要特征。
基金Project(NIPA-2012-H0401-12-1007) supported by the MKE(The Ministry of Knowledge Economy), Korea, supervised by the NIPAProject(2010-0020163) supported by Key Research Institute Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, Korea
文摘The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.
文摘For many years, nerve transfer has been commonly used as a treatment option following peripheral nerve injury, although the precise mechanism underlying successful nerve transfer is not yet clear. We developed an animal model to investigate the mechanism underlying nerve transfer between branches of the spinal accessory nerve (Ac) and suprascapular nerve (Ss) in rats, so that we could observe changes in the number of motor neurons, investigate the 3-dimensional localization of neurons in the anterior horn of the spinal cord, and perform an electromyogram (EMG) of the supraspinatus muscle before and after nerve transfer treatment. The present experiment showed a clear reduction in the number of γ motor neurons. The distributional portion of motor neurons following nerve transfer was mainly within the neuron column innervating the trapezius. Some neurons innervating the supraspinatus muscle also survived post-transfer. Compared with the non-operated group, the EMG restoration rate of the supraspinatus muscle following nerve transfer was 60% in the experimental group and 80% in a surgical control group. Following nerve transfer, there was a distinct reduction in the number of γ motor neurons. Therefore, γ motor neurons may have important effects on the recovery of muscular strength following nerve transfer. Moreover, because the neurons located in regions innervating either the trapezius or supraspinatus muscle were labeled after Ac transfer to Ss, we also suggest that indistinct axon regeneration mechanisms exist in the spinal cord following peripheral nerve transfer.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea,funded by the Ministry of Education(Nos.NRF2017R1A6A1A03015496,RS-2023-00249555).
文摘As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts have focused on leveraging electromyogram(EMG)for personal recognition,aiming to address security concerns.However,obtaining consistent EMG signals from the same individual is inherently challenging,resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition.Notably,conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities.This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary classifier,effectively mitigating the impact of data irregularities in EMG-based recognition.The proposed method employs empirical mode decomposition(EMD)to extract distinctive features.The model comprises two sub-networks designed to follow the siamese network architecture and a decision network integrated with the novel auxiliary classifier,specifically designed to address data irregularities.The two sub-networks sharing a weight calculate the compatibility function.The auxiliary classifier collaborates with a neural network to implement an attention mechanism.The attention mechanism using the auxiliary classifier solves the data irregularity problem by improving the importance of the EMG gesture section.Experimental results validated the efficacy of the proposed personal recognition method,achieving a remarkable 94.35%accuracy involving 100 subjects from the multisession CU_sEMG database(DB).This performance outperforms the existing approaches by 3%,employing auxiliary classifiers.Furthermore,an additional experiment yielded an improvement of over 0.85%of Ninapro DB,3%of CU_sEMG DB compared to the existing EMG-based recognition methods.Consequently,the proposed personal recognition using EMG proves to secure IoT devices,offering robustness against data irregularities.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2017R1A6A1A03015496)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C1014033).
文摘As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal recognitionmethod used for granting permission is a password-basedmethod,which causes security problems.Therefore,personal recognition studies using bio-signals are being conducted as a method to access control to devices.Among bio-signal,surface electromyogram(sEMG)can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation.Furthermore,as an advantage,sEMG can be conveniently measured from arms and legs.This paper proposes a personal recognition method using sEMG,based on a multi-stream convolutional neural network(CNN).The proposed method decomposes sEMG signals into intrinsic mode functions(IMF)using empirical mode decomposition(EMD)and transforms each IMF into a spectrogram.Personal recognition is performed by analyzing time–frequency features from the spectrogram transformed intomulti-streamCNN.The database(DB)adopted in this paper is the Ninapro DB,which is a benchmark EMG DB.The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%,compared with the singlestream CNN using the spectrogram of raw sEMG.
