The human-computer interaction (HCI) is now playing a great role in computer technology. This study introduces an automatic document control technique which is based on the human hand waving movements. The recognition...The human-computer interaction (HCI) is now playing a great role in computer technology. This study introduces an automatic document control technique which is based on the human hand waving movements. The recognition of hand movement is realized according to the surface electromyography (sEMG). A collector is set on the forearm. The sEMG signal is recorded and conveyed to a PC terminal by using wireless Zigbee. An automatic algorithm is developed in order to extract the characteristics of sEMG, recognize the waving movements, and transmit to document control command. The developed human-computer interaction technique can be used as a new gallery for teaching, as well as an assistant tool for disabled person.展开更多
AIM: To determine the accuracy of 2-channel surface electromyography(sE MG) for diagnosing oropharyngeal dysphagia(OPD) in patients with cerebral palsy.METHODS: Participants with cerebral palsy and OPD between 5 and 3...AIM: To determine the accuracy of 2-channel surface electromyography(sE MG) for diagnosing oropharyngeal dysphagia(OPD) in patients with cerebral palsy.METHODS: Participants with cerebral palsy and OPD between 5 and 30 years of age and age- and sexmatched healthy individuals received s EMG testing during swallowing. Electrodes were placed over the submental and infrahyoid muscles, and s EMG recordings were made during stepwise(starting at 3 mL) determination of maximum swallowing volume. Outcome measures included submental muscle group maximum amplitude, infrahyoid muscle group maximum amplitude(IMGMA), time lag between the peak amplitudes of 2 muscle groups, and amplitude difference between the 2 muscle groups.RESULTS: A total of 20 participants with cerebral palsy and OPD(OPD group) and 60 age- and sex-matched healthy volunteers(control group) were recruited. Among 20 patients with OPD, 19 had Dysphagia Outcome and Severity Scale records. Of them, 8 were classified as severe dysphagia(level 1), 1 was moderate dysphagia(level 3), 4 were mild to moderate dysphagia(level 4), 3 were mild dysphagia(level 5), and 3 were within functional limits(level 6). Although the groups were matched for age and sex, participants in the OPD group were significantly shorter, weighed less and had lower body mass index than their counterparts in the control group(both, P < 0.001). All s EMG parameter values were significantly higher in the OPD group compared with the control group(P < 0.05). Differences were most pronounced at the 3 mL swallowing volume. IMGMA at the 3 mL volume was the best predictor of OPD with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 85.0%, 90.0%, 73.9%, 94.7% and 88.8%, respectively.展开更多
We analyze muscular dystrophy recorded by sEMG and use standard methodologies and nonlinear chaotic methods here including the RQA. We reach sufficient evidence that the sEMG signal contains a large chaotic component....We analyze muscular dystrophy recorded by sEMG and use standard methodologies and nonlinear chaotic methods here including the RQA. We reach sufficient evidence that the sEMG signal contains a large chaotic component. We have estimated the correlation dimension (fractal measure), the largest Lyapunov exponent, the LZ complexity and the %Rec and %Det of the RQA demonstrating that such indexes are able to detect the presence of repetitive hidden patterns in sEMG which, in turn, senses the level of MU synchronization within the muscle. The results give also an interesting methodological indication in the sense that it evidences the manner in which nonlinear methods and RQA must be arranged and applied in clinical routine in order to obtain results of clinical interest. We have studied the muscular dystrophy and evidence that the continuous regime of chaotic transitions that we have in muscular mechanisms may benefit in this pathology by the use of the NPT treatment that we have considered in detail in our previous publications.展开更多
To explore the mechanisms underlying exercise-induced local muscle fatigue in patients with idiopathic Parkinson＇s disease （PD）,we used surface electromyography to record myoelectric signals from the tibialis anter...To explore the mechanisms underlying exercise-induced local muscle fatigue in patients with idiopathic Parkinson＇s disease （PD）,we used surface electromyography to record myoelectric signals from the tibialis anterior muscle during isometric contraction-induced fatigue until exhaustion.The results revealed no significant differences between patients with idiopathic PD and healthy controls in maximum voluntary contraction of the tibialis anterior muscle.The basic characteristics of surface electromyography were also similar between the two groups.The duration of isometric contraction at 50% maximum voluntary contraction was shortened in PD patients.In addition,PD patients exhibited a stronger increase in mean square amplitude,but a weaker decrease in median frequency and mean power frequency compared with healthy controls during isometric contraction.The skeletal muscles of PD patients revealed specificity of surface electromyography findings,indicating increased fatigability compared with healthy controls.展开更多
BACKGROUND Dystonic gait(DG) is one of clinical symptoms associated with functional dystonia in the functional movement disorders(FMDs). Dystonia is often initiated or worsened by voluntary action and associated with ...BACKGROUND Dystonic gait(DG) is one of clinical symptoms associated with functional dystonia in the functional movement disorders(FMDs). Dystonia is often initiated or worsened by voluntary action and associated with overflow muscle activation. There is no report for DG in FMDs caused by an abnormal pattern in the ankle muscle recruitment strategy during gait.CASE SUMMARY A 52-year-old male patient presented with persistent limping gait. When we requested him to do dorsiflexion and plantar flexion of his ankle in the standing and seating positions, we didn’t see any abnormality. However, we could see the DG during the gait. There were no evidences of common peroneal neuropathy and L5 radiculopathy in the electrodiagnostic study. Magnetic resonance imaging of the lumbar spine, lower leg, and brain had no definite finding. No specific finding was seen in the neurologic examination. For further evaluation, a wireless surface electromyography(EMG) was performed. During the gait, EMG amplitude of left medial and lateral gastrocnemius(GCM) muscles was larger than right medial and lateral GCM muscles. When we analyzed EMG signals for each muscle, there were EMG bursts of double-contraction in the left medial and lateral GCM muscles, while EMG analysis of right medial and lateral GCM muscles noted regular bursts of single contraction. We could find a cause of DG in FMDs.CONCLUSION We report an importance of a wireless surface EMG, in which other examination didn’t reveal the cause of DG in FMDs.展开更多
The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and c...The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.展开更多
文摘The human-computer interaction (HCI) is now playing a great role in computer technology. This study introduces an automatic document control technique which is based on the human hand waving movements. The recognition of hand movement is realized according to the surface electromyography (sEMG). A collector is set on the forearm. The sEMG signal is recorded and conveyed to a PC terminal by using wireless Zigbee. An automatic algorithm is developed in order to extract the characteristics of sEMG, recognize the waving movements, and transmit to document control command. The developed human-computer interaction technique can be used as a new gallery for teaching, as well as an assistant tool for disabled person.
文摘AIM: To determine the accuracy of 2-channel surface electromyography(sE MG) for diagnosing oropharyngeal dysphagia(OPD) in patients with cerebral palsy.METHODS: Participants with cerebral palsy and OPD between 5 and 30 years of age and age- and sexmatched healthy individuals received s EMG testing during swallowing. Electrodes were placed over the submental and infrahyoid muscles, and s EMG recordings were made during stepwise(starting at 3 mL) determination of maximum swallowing volume. Outcome measures included submental muscle group maximum amplitude, infrahyoid muscle group maximum amplitude(IMGMA), time lag between the peak amplitudes of 2 muscle groups, and amplitude difference between the 2 muscle groups.RESULTS: A total of 20 participants with cerebral palsy and OPD(OPD group) and 60 age- and sex-matched healthy volunteers(control group) were recruited. Among 20 patients with OPD, 19 had Dysphagia Outcome and Severity Scale records. Of them, 8 were classified as severe dysphagia(level 1), 1 was moderate dysphagia(level 3), 4 were mild to moderate dysphagia(level 4), 3 were mild dysphagia(level 5), and 3 were within functional limits(level 6). Although the groups were matched for age and sex, participants in the OPD group were significantly shorter, weighed less and had lower body mass index than their counterparts in the control group(both, P < 0.001). All s EMG parameter values were significantly higher in the OPD group compared with the control group(P < 0.05). Differences were most pronounced at the 3 mL swallowing volume. IMGMA at the 3 mL volume was the best predictor of OPD with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 85.0%, 90.0%, 73.9%, 94.7% and 88.8%, respectively.
文摘为了提高表面肌电信号(surface electromyography,sEMG)的手势分类准确率,通过惯性测量单元(inertial measurement unit,IMU)与采集姿态信号与sEMG的混合信号,提出了GRUBiLSTM双层网络的实时手势分类算法。第1层门控循环单元(gated recurrent unit,GRU)利用能量组合算子特征对混合信号进行突变点检测,定位运动态数据起始点;第2层双向长短时记忆循环神经网络(Bi-directional long short term memory,BiLSTM)使用能量核相图特征对运动态混合信号进行2个方向10种手势的分类。通过离线模型优化,分类算法识别时间低于40 ms,突变点检测精度88.7%以上,手势分类准确率为85%,信息传输率(informationtranslaterate, ITR)达到89.9 bits/min,与基于机器学习的分类算法相比,在准确率与计算效率上具有优势。
文摘We analyze muscular dystrophy recorded by sEMG and use standard methodologies and nonlinear chaotic methods here including the RQA. We reach sufficient evidence that the sEMG signal contains a large chaotic component. We have estimated the correlation dimension (fractal measure), the largest Lyapunov exponent, the LZ complexity and the %Rec and %Det of the RQA demonstrating that such indexes are able to detect the presence of repetitive hidden patterns in sEMG which, in turn, senses the level of MU synchronization within the muscle. The results give also an interesting methodological indication in the sense that it evidences the manner in which nonlinear methods and RQA must be arranged and applied in clinical routine in order to obtain results of clinical interest. We have studied the muscular dystrophy and evidence that the continuous regime of chaotic transitions that we have in muscular mechanisms may benefit in this pathology by the use of the NPT treatment that we have considered in detail in our previous publications.
文摘To explore the mechanisms underlying exercise-induced local muscle fatigue in patients with idiopathic Parkinson＇s disease （PD）,we used surface electromyography to record myoelectric signals from the tibialis anterior muscle during isometric contraction-induced fatigue until exhaustion.The results revealed no significant differences between patients with idiopathic PD and healthy controls in maximum voluntary contraction of the tibialis anterior muscle.The basic characteristics of surface electromyography were also similar between the two groups.The duration of isometric contraction at 50% maximum voluntary contraction was shortened in PD patients.In addition,PD patients exhibited a stronger increase in mean square amplitude,but a weaker decrease in median frequency and mean power frequency compared with healthy controls during isometric contraction.The skeletal muscles of PD patients revealed specificity of surface electromyography findings,indicating increased fatigability compared with healthy controls.
文摘BACKGROUND Dystonic gait(DG) is one of clinical symptoms associated with functional dystonia in the functional movement disorders(FMDs). Dystonia is often initiated or worsened by voluntary action and associated with overflow muscle activation. There is no report for DG in FMDs caused by an abnormal pattern in the ankle muscle recruitment strategy during gait.CASE SUMMARY A 52-year-old male patient presented with persistent limping gait. When we requested him to do dorsiflexion and plantar flexion of his ankle in the standing and seating positions, we didn’t see any abnormality. However, we could see the DG during the gait. There were no evidences of common peroneal neuropathy and L5 radiculopathy in the electrodiagnostic study. Magnetic resonance imaging of the lumbar spine, lower leg, and brain had no definite finding. No specific finding was seen in the neurologic examination. For further evaluation, a wireless surface electromyography(EMG) was performed. During the gait, EMG amplitude of left medial and lateral gastrocnemius(GCM) muscles was larger than right medial and lateral GCM muscles. When we analyzed EMG signals for each muscle, there were EMG bursts of double-contraction in the left medial and lateral GCM muscles, while EMG analysis of right medial and lateral GCM muscles noted regular bursts of single contraction. We could find a cause of DG in FMDs.CONCLUSION We report an importance of a wireless surface EMG, in which other examination didn’t reveal the cause of DG in FMDs.
基金supported by the Development of Sleep Disordered Breathing Detection and Auxiliary Regulation System Project(No.2019I1009)。
文摘The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.
