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
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
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
Background： Both Parkinson＇s disease （PD） and multiple system atrophy （MSA） have associated sleep disorders related to the underlying neurodegenerative pathology. Clinically, MSA with predominant parkinsonism （...Background： Both Parkinson＇s disease （PD） and multiple system atrophy （MSA） have associated sleep disorders related to the underlying neurodegenerative pathology. Clinically, MSA with predominant parkinsonism （MSA-P） resembles PD in the manifestation of prominent parkinsonism, Whether the amount of rapid eye movement （REM） sleep without atonia could be a potential marker for differentiating MSA-P from PD has not been thoroughly investigated. This study aimed to examine whether sleep parameters could provide a method for differentiating MSA-P from PD. Methods： This study comprised 24 MSA-P patients and 30 PD patients, and they were of similar age, gender, and REM sleep behavior disorder （RBD） prevalence. All patients underwent clinical evaluation and one night of video-polysomnography recording. The tonic and phasic chin electromyogram （EMG） activity was manually quantified during REM sleep of each patient. We divided both groups in terms of whether they had RBD to make subgroup analysis. Results： No significant difference between MSA-P group and PD group had been tbund in clinical characteristics and sleep architecture. However, MSA-P patients had higher apnea-hypopnea index （AHI; 1.15 [0.00, 8.73]/h vs. 0.00 [0.00, 0.55]/h, P = 0.024） and higher tonic chin EMG density （34.02 [ 18.48, 57.18]% vs. 8.40 [3.11, 13.061%, P 〈 0.001 ） as compared to PD patients. Subgroup analysis found that tonic EMG density in MSA ＋ RBD subgroup was higher than that in PD ＋ RBD subgroup （55.04 [26.81,69.62]% vs. 11.40 [8.51,20.411%, P 〈 0.001 ）. Furthermore, no evidence of any difference in tonic EMG density emerged between PD ＋ RBD and MSA - RBD subgroups （P 〉 0.05）. Both disease duration （P = 0.056） and AHI （P = 0.051） showed no significant differences during subgroup analysis although there was a trend toward longer disease duration in PD ＋ RBD subgroup and higher AHI in MSA - RBD subgroup. Stepwise multiple linear regression analysis identified the presence of MSA-P （[3 0.552, P 〈 0.001 ） and RBD （[3 = 0.433, P 〈 0.001 ） as predictors of higher tonic EMG density. Conclusion： Tonic chin EMG density could be a potential marker for differentiating MSA-P from PD.展开更多
Background Work-related musculoskeletal disorders （WMSDs） have high prevalence in sewing machine operators employed in the garment industry. Long work duration, sustained low level work and precise hand work are the...Background Work-related musculoskeletal disorders （WMSDs） have high prevalence in sewing machine operators employed in the garment industry. Long work duration, sustained low level work and precise hand work are the main risk factors of neck-shoulder disorders for sewing machine operators. Surface electromyogram （sEMG） offers a valuable tool to determine muscle activity （internal exposure） and quantify muscular load （external exposure）. During sustained and/or repetitive muscle contractions, typical changes of muscle fatigue in sEMG, as an increase in amplitude or a decrease as a shift in spectrum towards lower frequencies, can be observed. In this paper, we measured and quantified the muscle load and muscular activity patterns of neck-shoulder muscles in female sewing machine operators during sustained sewing machine operating tasks using sEMG. Methods A total of 18 healthy women sewing machine operators volunteered to participate in this study. Before their daily sewing machine operating task, we measured the maximal voluntary contractions （MVC） and 20%MVC of bilateral cervical erector spinae （CES） and upper trapezius （UT） respectively, then the sEMG signals of bilateral UT and CES were monitored and recorded continuously during 200 minutes of sustained sewing machine operating simultaneously which equals to 20 time windows with 10 minutes as one time window. After 200 minutes＇ work, we retest 20%MVC of four neck-shoulder muscles and recorded the sEMG signals. Linear analysis, including amplitude probability distribution frequency （APDF）, amplitude analysis parameters such as roof mean square （RMS） and spectrum analysis parameter as median frequency （MF）, were used to calculate and indicate muscle load and muscular activity of bilateral CES and UT. Results During 200 minutes of sewing machine operating, the median load for the left cervical erector spinae （LCES）, right cervical erector spinae （RCES）, left upper trapezius （LUT） and right upper trapezius （RUT） were 6.78%MVE, 6.94%MVE, 6.47%MVE and 5.68%MVE, respectively. Work load of right muscles are significantly higher than that of the left muscles （P〈0.05）; sEMG signal analysis of isometric contractions indicated that the amplitude value before operating was significantly higher than that of after work （P 〈0.01）, and the spectrum value of bilateral CES and UT were significantly lower than those of after work （P 〈0.01）; according to the sEMG signal data of 20 time windows, with operating time pass by, the muscle activity patterns of bilateral CES and UT showed dynamic changes, the maximal amplitude of LCES, RCES, LUT occurred at the 20th time window, RUT at 16th time window, spectrum analysis showed that the lower value happened at 7th, 16th, 20th time windows. Conclusions Female sewing machine operators were exposed to high sustained static load on bilateral neck-shoulder muscles; left neck and shoulder muscles were held in more static positions; the 7th, 16th, and 20th time windows were muscle fatiQue period that erQonomics intervention can protocol at these periods.展开更多
We studied the temporal and spacial character of the electromyogram (EMG) evoked by acupuncture in long-issimus dorsi (LD) muscles of rat, and evaluated the effect of needling direction or local blockade on EMG propag...We studied the temporal and spacial character of the electromyogram (EMG) evoked by acupuncture in long-issimus dorsi (LD) muscles of rat, and evaluated the effect of needling direction or local blockade on EMG propagation. When certain sites on LD muscle were acupunctured, asynchronous EMG could be activated not only at the acupunctured point, but also within the muscle region supplied by the adjacent 2-3 vertebral segments. The EMG evoked by stimulation on the borderline of aponeurosis and muscle venter was larger in amplitude than those on the other sites in the same vertebral segment. When the distance from the recorded site to stimulated site increased, the EMG amplitude decreased, and its latency prolonged. Acupuncture in an oblique direction toward rostral or caudal side of the muscle enhanced the EMG amplitude in the same direction. EMG activity was weakened and its propagation was blocked by local injection of procaine. These results indicated that the character of EMG propagation evoked by展开更多
目的探讨2型糖尿病(T2DM)患者周围神经传导速度异常的影响因素。方法选取2021年1月至2023年1月我院收治的156例T2DM患者为研究对象,将合并周围神经病变(PN)的85例患者设为糖尿病周围神经病变(DPN)组,未合并PN的71例患者设为NDPN组。两...目的探讨2型糖尿病(T2DM)患者周围神经传导速度异常的影响因素。方法选取2021年1月至2023年1月我院收治的156例T2DM患者为研究对象,将合并周围神经病变(PN)的85例患者设为糖尿病周围神经病变(DPN)组,未合并PN的71例患者设为NDPN组。两组均行肌电图检查,比较周围神经传导速度;比较两组的基础资料、生化指标;应用多因素Logistic回归分析DPN的影响因素。结果DPN组的正中感觉神经传导速度、正中运动神经传导速度、双侧腓肠感觉神经传导速度、双侧腓总运动神经传导速度低于NDPN组(P<0.05)。两组的T2DM病程、餐后2 h血糖(2 h PG)、空腹血糖(FPG)、餐后2 h C肽(2 h C-P)、糖化血红蛋白(HbA1c)、尿微量白蛋白(UMA)、血浆渗透压(POP)水平比较,差异具有统计学意义(P<0.05)。多因素Logistic回归分析显示,T2DM病程、2 h PG、FPG、2 h C-P、HbA1c、UMA、POP是DPN的影响因素(P<0.05)。结论DPN发病机制较为复杂,临床应监测T2DM患者病程、2 h PG、FPG、2 h C-P、HbA1c、UMA、POP,定期进行肌电图检查,尽可能预防及延缓DPN发生发展。展开更多
Spinal orthoses were designed to correct poor posture;however,they may restrict trunk movements at all times,making daily activities difficult.Detecting trunk movements can provide instructions for adjusting the stiff...Spinal orthoses were designed to correct poor posture;however,they may restrict trunk movements at all times,making daily activities difficult.Detecting trunk movements can provide instructions for adjusting the stiffness of the spinal orthosis.This study evaluated the feasibility of identifying movements based on surface electromyography(sEMG)signals.Ten participants were tested for different movements with two different modalities:motion without the spinal orthosis(Normal)and with the spinal orthosis(Spinal orthosis).The sEMG signals were collected from eight muscles using surface electrodes during four movements[flexion-extension,lateral bending,axial rotation,and stand to sit to stand].Four time domain features were extracted,with a total of 32 feature vectors.The principal component analysis(PCA)method was adopted to feature selection,and it was found that eight feature dimensions can make cumulative explained variance exceed 95%.The results showed that machine learning algorithms could not only identify Normal and Spinal orthosis movement modalities,but also distinguish four daily movements.Moreover,the classification performance of Random Forest(RF),k-Nearest Neighbor(kNN),and Support Vector Machine(SVM)algorithms were also compared.The results showed that all three machine algorithms have high classification accuracy.The machine learning methods can accurately identify movement patterns by considering sEMG signals,which may provide instructions for adjusting the stiffness of the spinal orthosis.In the future,the spinal orthosis with adjustable stiffness controlled by sEMG signals could help correct poor posture,and permit the wearer to achieve free movement when needed.展开更多
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.
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Background： Both Parkinson＇s disease （PD） and multiple system atrophy （MSA） have associated sleep disorders related to the underlying neurodegenerative pathology. Clinically, MSA with predominant parkinsonism （MSA-P） resembles PD in the manifestation of prominent parkinsonism, Whether the amount of rapid eye movement （REM） sleep without atonia could be a potential marker for differentiating MSA-P from PD has not been thoroughly investigated. This study aimed to examine whether sleep parameters could provide a method for differentiating MSA-P from PD. Methods： This study comprised 24 MSA-P patients and 30 PD patients, and they were of similar age, gender, and REM sleep behavior disorder （RBD） prevalence. All patients underwent clinical evaluation and one night of video-polysomnography recording. The tonic and phasic chin electromyogram （EMG） activity was manually quantified during REM sleep of each patient. We divided both groups in terms of whether they had RBD to make subgroup analysis. Results： No significant difference between MSA-P group and PD group had been tbund in clinical characteristics and sleep architecture. However, MSA-P patients had higher apnea-hypopnea index （AHI; 1.15 [0.00, 8.73]/h vs. 0.00 [0.00, 0.55]/h, P = 0.024） and higher tonic chin EMG density （34.02 [ 18.48, 57.18]% vs. 8.40 [3.11, 13.061%, P 〈 0.001 ） as compared to PD patients. Subgroup analysis found that tonic EMG density in MSA ＋ RBD subgroup was higher than that in PD ＋ RBD subgroup （55.04 [26.81,69.62]% vs. 11.40 [8.51,20.411%, P 〈 0.001 ）. Furthermore, no evidence of any difference in tonic EMG density emerged between PD ＋ RBD and MSA - RBD subgroups （P 〉 0.05）. Both disease duration （P = 0.056） and AHI （P = 0.051） showed no significant differences during subgroup analysis although there was a trend toward longer disease duration in PD ＋ RBD subgroup and higher AHI in MSA - RBD subgroup. Stepwise multiple linear regression analysis identified the presence of MSA-P （[3 0.552, P 〈 0.001 ） and RBD （[3 = 0.433, P 〈 0.001 ） as predictors of higher tonic EMG density. Conclusion： Tonic chin EMG density could be a potential marker for differentiating MSA-P from PD.
文摘Background Work-related musculoskeletal disorders （WMSDs） have high prevalence in sewing machine operators employed in the garment industry. Long work duration, sustained low level work and precise hand work are the main risk factors of neck-shoulder disorders for sewing machine operators. Surface electromyogram （sEMG） offers a valuable tool to determine muscle activity （internal exposure） and quantify muscular load （external exposure）. During sustained and/or repetitive muscle contractions, typical changes of muscle fatigue in sEMG, as an increase in amplitude or a decrease as a shift in spectrum towards lower frequencies, can be observed. In this paper, we measured and quantified the muscle load and muscular activity patterns of neck-shoulder muscles in female sewing machine operators during sustained sewing machine operating tasks using sEMG. Methods A total of 18 healthy women sewing machine operators volunteered to participate in this study. Before their daily sewing machine operating task, we measured the maximal voluntary contractions （MVC） and 20%MVC of bilateral cervical erector spinae （CES） and upper trapezius （UT） respectively, then the sEMG signals of bilateral UT and CES were monitored and recorded continuously during 200 minutes of sustained sewing machine operating simultaneously which equals to 20 time windows with 10 minutes as one time window. After 200 minutes＇ work, we retest 20%MVC of four neck-shoulder muscles and recorded the sEMG signals. Linear analysis, including amplitude probability distribution frequency （APDF）, amplitude analysis parameters such as roof mean square （RMS） and spectrum analysis parameter as median frequency （MF）, were used to calculate and indicate muscle load and muscular activity of bilateral CES and UT. Results During 200 minutes of sewing machine operating, the median load for the left cervical erector spinae （LCES）, right cervical erector spinae （RCES）, left upper trapezius （LUT） and right upper trapezius （RUT） were 6.78%MVE, 6.94%MVE, 6.47%MVE and 5.68%MVE, respectively. Work load of right muscles are significantly higher than that of the left muscles （P〈0.05）; sEMG signal analysis of isometric contractions indicated that the amplitude value before operating was significantly higher than that of after work （P 〈0.01）, and the spectrum value of bilateral CES and UT were significantly lower than those of after work （P 〈0.01）; according to the sEMG signal data of 20 time windows, with operating time pass by, the muscle activity patterns of bilateral CES and UT showed dynamic changes, the maximal amplitude of LCES, RCES, LUT occurred at the 20th time window, RUT at 16th time window, spectrum analysis showed that the lower value happened at 7th, 16th, 20th time windows. Conclusions Female sewing machine operators were exposed to high sustained static load on bilateral neck-shoulder muscles; left neck and shoulder muscles were held in more static positions; the 7th, 16th, and 20th time windows were muscle fatiQue period that erQonomics intervention can protocol at these periods.
基金This work was supported by the fund of Climbing Program provided by the State Science and Technology Commission.
文摘We studied the temporal and spacial character of the electromyogram (EMG) evoked by acupuncture in long-issimus dorsi (LD) muscles of rat, and evaluated the effect of needling direction or local blockade on EMG propagation. When certain sites on LD muscle were acupunctured, asynchronous EMG could be activated not only at the acupunctured point, but also within the muscle region supplied by the adjacent 2-3 vertebral segments. The EMG evoked by stimulation on the borderline of aponeurosis and muscle venter was larger in amplitude than those on the other sites in the same vertebral segment. When the distance from the recorded site to stimulated site increased, the EMG amplitude decreased, and its latency prolonged. Acupuncture in an oblique direction toward rostral or caudal side of the muscle enhanced the EMG amplitude in the same direction. EMG activity was weakened and its propagation was blocked by local injection of procaine. These results indicated that the character of EMG propagation evoked by
文摘目的探讨2型糖尿病(T2DM)患者周围神经传导速度异常的影响因素。方法选取2021年1月至2023年1月我院收治的156例T2DM患者为研究对象,将合并周围神经病变(PN)的85例患者设为糖尿病周围神经病变(DPN)组,未合并PN的71例患者设为NDPN组。两组均行肌电图检查,比较周围神经传导速度;比较两组的基础资料、生化指标;应用多因素Logistic回归分析DPN的影响因素。结果DPN组的正中感觉神经传导速度、正中运动神经传导速度、双侧腓肠感觉神经传导速度、双侧腓总运动神经传导速度低于NDPN组(P<0.05)。两组的T2DM病程、餐后2 h血糖(2 h PG)、空腹血糖(FPG)、餐后2 h C肽(2 h C-P)、糖化血红蛋白(HbA1c)、尿微量白蛋白(UMA)、血浆渗透压(POP)水平比较,差异具有统计学意义(P<0.05)。多因素Logistic回归分析显示,T2DM病程、2 h PG、FPG、2 h C-P、HbA1c、UMA、POP是DPN的影响因素(P<0.05)。结论DPN发病机制较为复杂,临床应监测T2DM患者病程、2 h PG、FPG、2 h C-P、HbA1c、UMA、POP,定期进行肌电图检查,尽可能预防及延缓DPN发生发展。
基金the National Natural Science Foundation of China(Grant numbers 11632013,11772214 and 11802196).
文摘Spinal orthoses were designed to correct poor posture;however,they may restrict trunk movements at all times,making daily activities difficult.Detecting trunk movements can provide instructions for adjusting the stiffness of the spinal orthosis.This study evaluated the feasibility of identifying movements based on surface electromyography(sEMG)signals.Ten participants were tested for different movements with two different modalities:motion without the spinal orthosis(Normal)and with the spinal orthosis(Spinal orthosis).The sEMG signals were collected from eight muscles using surface electrodes during four movements[flexion-extension,lateral bending,axial rotation,and stand to sit to stand].Four time domain features were extracted,with a total of 32 feature vectors.The principal component analysis(PCA)method was adopted to feature selection,and it was found that eight feature dimensions can make cumulative explained variance exceed 95%.The results showed that machine learning algorithms could not only identify Normal and Spinal orthosis movement modalities,but also distinguish four daily movements.Moreover,the classification performance of Random Forest(RF),k-Nearest Neighbor(kNN),and Support Vector Machine(SVM)algorithms were also compared.The results showed that all three machine algorithms have high classification accuracy.The machine learning methods can accurately identify movement patterns by considering sEMG signals,which may provide instructions for adjusting the stiffness of the spinal orthosis.In the future,the spinal orthosis with adjustable stiffness controlled by sEMG signals could help correct poor posture,and permit the wearer to achieve free movement when needed.
