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为了提高人体肌电信号对于下肢动作识别的准确率,提出一种基于遗传算法(GA)优化的径向基(RBF)神经网络分类模型。通过采集人体日常8种下肢动作的表面肌电信号并选择“sym6”系小波函数对肌电信号进行滤波预处理,使用主成分分析法(PCA)对时频域特征降维,把特征向量输入GA算法优化的RBF神经网络进行训练和识别。实验结果表明,该方法对同一受试者8种下肢动作的平均识别率为94.00%±0.45%;对15位不同受试下肢动作识别率达到89.30%,比传统BP神经网络的识别准确率提高11.8%,预测时间缩短6 s。所提出的方法为肌电信号应用于下肢智能康复机器人的意图识别研究提供参考,有助于病人的康复。 相似文献
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《Clinical neurophysiology》2021,132(4):967-974
ObjectiveChronic fatigue syndrome (CFS) and fibromyalgia (FM) are disorders of unknown etiology and unclear pathophysiology, with overlapping symptoms of – especially muscular –fatigue and pain. Studies have shown increased muscle fiber conduction velocity (CV) in the non-painful muscles of FM patients. We investigated whether CFS patients also show CV abnormalities.MethodsFemales with CFS (n = 25), with FM (n = 22), and healthy controls (n = 21) underwent surface electromyography of the biceps brachii, loaded up to 20% of maximum strength, during short static contractions. The mean CV and motor unit potential (MUP) velocities with their statistical distribution were measured.ResultsThe CV changes with force differed between CFS-group and both FM-group and controls (P = 0.01). The CV of the CFS-group increased excessively with force (P < 0.001), whereas that of the controls increased only slightly and non-significantly, and that of the FM-group did not increase at all. In the CFS-group, the number of MUPs conveying very high conduction velocities increased abundantly with force and the MUPs narrowed.ConclusionOur results suggest disturbed muscle membrane function in CFS patients, in their motor units involved in low force generation. Central neural deregulation may contribute to this disturbance.SignificanceThese findings help to detangle the underlying mechanisms of CFS. 相似文献
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《Clinical neurophysiology》2021,132(2):315-322
ObjectivePrevious studies have demonstrated voluntary movement alterations as well as motor cortex excitability and plasticity changes in patients with mild cognitive impairment (MCI). To investigate the pathophysiology of movement abnormalities in MCI, we tested possible relationships between movement abnormalities and primary motor cortex alterations in patients.MethodsFourteen amnestic MCI (aMCI) patients and 16 healthy controls were studied. Cognitive assessment was performed using clinical scales. Finger tapping was recorded by a motion analysis system. Transcranial magnetic stimulation was used to test the input/output curve of motor evoked potentials, intracortical inhibition, and short-latency afferent inhibition. Primary motor cortex plasticity was probed by theta burst stimulation. We investigated correlations between movement abnormalities, clinical scores, and cortical neurophysiological parameters.ResultsMCI patients showed less rhythmic movement but no other movement abnormalities. Cortical excitability measures were normal in patients, whereas plasticity was reduced. Movement rhythm abnormalities correlated with frontal dysfunction scores.ConclusionOur study in MCI patients demonstrated abnormal voluntary movement and plasticity changes, with no correlation between the two. Altered rhythm correlated with frontal dysfunction.SignificanceOur results contribute to the understanding of pathophysiological mechanisms of motor impairment in MCI. 相似文献
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目的: 观察悬吊训练(SET)对脑卒中患者平衡功能恢复的影响。方法: 脑卒中平衡功能障碍患者40例,随机分为对照组和观察组各20例,2组均接受常规康复训练,在此基础上,观察组加以悬吊训练。治疗前和治疗4周后采用Berg平衡量表(BBS)、Barthel指数(BI)以及采集竖脊肌表面肌电平均肌电值(AEMG)和中位频率值(MF)对2组患者进行评定。结果: 治疗4周后,2组BBS、BI评分均明显高于治疗前(均P<0.01),且观察组以上评分均明显高于对照组(均P<0.05)。治疗前,2组患者AEMG、MF值组内比较健侧均明显高于患侧(均P<0.05);治疗4周后,2组患者健患侧竖脊肌AEMG、MF值均较治疗前明显提高(均P<0.05),观察组健患侧AEMG、MF值均较对照组明显提高(均P<0.05),但观察组健患侧AEMG、MF值之间无统计学差异。结论: SET能够改善脑卒中患者的竖脊肌AEMG、MF值及BBS和BI评分,提高脑卒中患者躯干肌肉运动功能,从而提高平衡功能以及日常生活活动能力,值得在临床推广应用。 相似文献
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IntroductionThe use of motor imagery (MI) has been shown to offer significant improvements in movement performance in sports, and is now receiving a lot of attention as a relatively new therapeutic approach which can be applied in rehabilitation. However, the effects of MI on the quality of movement is still unclear. This study explored the immediate effect of MI on reaching tasks in healthy subjects.Methods17 healthy individuals (33 ± 8.2 years) participated in the study. Surface electromyography (sEMG) and inertial measurement units (IMU) were used to identify muscle activity and angular velocity in both upper limbs. Participants performed a reach task using their dominant and non-dominant arms at their most comfortable speed, they were then asked to imagine themselves performing the same reaching task, and finally they were asked to repeat the reaching task.ResultsSignificant decreases were seen in the muscle activity between pre and post MI for Biceps Brachii, Anterior Deltoid and Triceps Brachii. In addition, a significant increase was seen in extension angular velocity post MI.DiscussionThe results indicate that the use of MI just after physical practice appears to have an immediate effect on the muscle activity and kinematics during a reaching task, which may suggest an improved quality of movement.ConclusionThis proof of concept study shows the potential for MI to improve the quality of performing reaching task and offers a possible therapeutic option for Stroke survivors and other neuromuscular disorders. 相似文献
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目的:评估被动式负重外骨骼在穿戴者站立负重姿态下的承重性能。方法:招募7名健康成年男子作为受试者,在不穿外骨骼无负重、穿戴被动式负重外骨骼背负25 kg制式背包和不穿被动式负重外骨骼背负25 kg制式背包3种条件下保持静止站立姿态10 min,观察受试者斜方肌与竖脊肌的肌电信号、肩部压力和能量消耗等指标。结果:受试者穿戴被动式负重外骨骼负重较不穿外骨骼负重,竖脊肌肌电强度增加,斜方肌肌电强度平均降低68.68%,肩部压力平均降低85.56%,耗氧量、二氧化碳呼出量和能量消耗分别降低9.47%、16.34%、10.87%。结论:该被动式负重外骨骼能减轻穿戴者颈部、肩部肌肉的负担,从而降低能量消耗,提高穿戴者的负重效率,具有良好的承重性能。 相似文献
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Hiroyuki Nodera MD Yusuke Osaki MD PHD Hiroki Yamazaki MD Atsuko Mori MD PHD Yuishin Izumi MD PHD Ryuji Kaji MD PHD 《Muscle & nerve》2019,59(2):224-228
Introduction: The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio-sound identification by artificial intelligence, we hypothesized that the extraction of characteristic resting EMG signals and application of machine learning algorithms could help classify various EMG discharges. Methods: Data files of 6 classes of resting EMG signals were divided into 2-s segments. Extraction of characteristic features (384 and 4,367 features each) was used to classify the 6 types of discharges using machine learning algorithms. Results: Across 841 audio files, the best overall accuracy of 90.4% was observed for the smaller feature set. Among the feature classes, mel-frequency cepstral coefficients (MFCC)-related features were useful in correct classification. Conclusions: We showed that needle EMG resting signals were satisfactorily classifiable by the combination of feature extraction and machine learning, and this can be applied to clinical settings. Muscle Nerve 59 :224–228, 2019 相似文献
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