基于Myo旋转偏移估计与自适应校正的手势识别方法

在基于表面肌电信号(Surface electromyography, sEMG)的手势识别系统中, 针对Myo环形电极多次实验间旋转位置不同导致的识别精度降低问题, 提出了一种基于极坐标系的电极位置偏移估计与自适应校正的识别方法. 该方法首先建立相对于环形肌电传感器的极坐标系, 提出了极坐标系下活跃极角(Activation polar angle, APA), 用于估计实验中传感器相对于初始位置的横向旋转偏移角度; 进而建立基于偏移角度的线性变换模型, 在肌电信号特征空间内, 对电极偏移位置下的样本进行自适应校正. 在8 种常用手势识别应用中, 设计了两种实验范式: 利用传感器各通道数据循环平移模拟电极横向旋转偏移实验和肌电传感器在小臂肌肉上的真实旋转偏移实验. 结果均表明所提出方法的识别精度远高于未进行校正的模型识别精度. 因此, 所提出的电极偏移估计与自适应校正识别方法, 不仅有效提高了表面肌电交互系统识别的鲁棒性, 也降低了使用者在多次使用时训练成本与学习负担.     

基于典型相关分析与低通滤波的肌电伪迹去除

提出了一种基于典型相关分析(CCA)和低通滤波的盲源分离方法去除脑电信号(EEG)中的肌电伪迹.该方法首先将混入了肌电伪迹的EEG信号分解为不相关的CCA分量,然后对与伪迹源相关的分量进行低通滤波处理,去除这些分量中的高频伪迹成分,最后利用与EEG相关的CCA分量和滤波处理后的新分量重构信号,消除肌电伪迹的影响.实验结果表明,采用CCA能够有效地分离出肌电伪迹,而结合低通滤波技术能够更有效地保留EEG信息.该方法取得了较好的去除肌电伪迹的效果.     

滤除SEMG工频干扰的数字陷波器设计

50 Hz工频干扰是表面肌电信号（SEMG）的主要干扰源之一,消除工频干扰是表面肌电信号处理中的一项重要技术。鉴于原有模拟信号调理电路在工频消噪这一环节上的不足,设计了一种50 Hz数字陷波器用以消噪,减小干扰。实验证明,采用基于窗函数法的FIR原理设计的50 Hz数字陷波器能有效滤除SEMG中的工频干扰并基本不影响50 Hz周围有效SEMG的获取。     

Recognizing the breathing resistances of wearing respirators from respiratory and sEMG signals with artificial neural networks

This study is devoted to recognizing the breathing resistances of wearing respirators from respiratory and surface electromyography (sEMG) signals. Ten subjects were required to sit for 5 min and walk for 5 min while wearing two different models of N95 filtering facepiece respirators (FFRs) and without a respirator. We recorded the sEMG signals from the respiratory muscles of the subjects, and the respiratory amplitude is also collected. Subsequently, fifteen features of the sEMG time domain and respiratory amplitude were extracted and used as input vectors to a recognition model based on artificial neural networks (ANNs). Finally, the experimental results show that these artificial neural networks are effective for recognizing different airway resistances of wearing respirators from sEMG and respiratory signals. The results also indicate that abdominal and scalene are the primary respiratory muscles affected by using N95 FFRs.Relevance to industryRespirator manufactures and administrations can readily employ this paper's findings for recognizing the breathing resistances of wearing respirators from respiratory and surface electromyography (sEMG) signals based on artificial neural networks automatically. Observations of the present study are in support of testing only the two primary muscles (abdominal and scalene) to simplify the evaluation of the effects of the breathing resistances of wearing respirators on respiratory muscles.     

Mind at ease puts a smile on the face: Psychophysiological evidence that processing facilitation elicits positive affect.

The affect system, in its position to monitor organismic–environmental transactions, may be sensitive to the internal dynamics of information processing. Hence, the authors predicted that facilitation of stimulus processing should elicit a brief, mild, positive affective response. In 2 studies, participants watched a series of neutral pictures while the processing ease was unobtrusively manipulated. Affective reactions were assessed with facial electromyography (EMG). In both studies, easy-to-process pictures elicited higher activity over the region of zygomaticus major, indicating positive affect. The EMG data were paralleled by self-reports of positive responses to the facilitated stimuli. The findings suggest a close link between processing dynamics and affect and may help understand several preference phenomena, including the mere-exposure effect. The findings also highlight a potential source of affective biases in social judgments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Physiological and subjective responses to breathing resistance of N95 filtering facepiece respirators in still-sitting and walking

The objective of this study was to assess the impact of breathing resistance on physiological and subjective responses to N95 filtering facepiece respirators (N95 FFRs) during still-sitting and walking. Fifteen subjects sat for 5 min and walked for 5 min while wearing 2 different models of N95 FFRs, 1 model of which was equipped with exhalation valves (N95 FFR/EV). The subjects were monitored by a modified monitoring garment for respiratory signals (RSP) and surface electromyography (sEMG). Subjects also were asked to complete subjective ratings of overall breathing resistance. The results of the physiological measurements in this study have shown that compared with no respirator, wearing N95 FFR had a direct effect on increasing respiratory amplitude, muscle activity and fatigue of abdominal, and fatigue of scalene; The use of N95 FFR/EV conferred limited physiological benefit over N95 FFR in walking; Compared with sitting still, walking significantly decreased respiratory amplitude, but increased respiratory rate, the muscle activity of sternomastoid, scalene, diaphragm and abdominal, the fatigue of scalene and intercostal. The subjective survey showed that wearing respirators and walking had a direct effect on improving the subjective overall breathing resistance. Significantly low to moderate correlation coefficients were shown between physiological values (respiratory amplitude, the muscle activity of diaphragm, the muscle activity and fatigue of scalene and abdominal), and the subjective breathing resistance. This is the first reported study that combines RSP, sEMG and subjective overall breathing resistance to evaluate breathing resistance on the use of N95 FFR in sitting still and walking. The physiological responses to breathing resistance of wearing a N95 FFR for 5 min in still-sitting and walking are relatively small and should generally be well tolerated by healthy persons.Relevance to industryThis paper's findings can be readily employed by respirator manufactures and administrations for evaluating the respiratory muscle function (activity, fatigue) and breathing parameters of wearing N95 FFRs. Observations of present study are in support of issuing new regulations to raise the limit for breathing resistance over short periods at low-moderate exertion tasks. Thus, the manufacturers could easily fulfill the requirements for collection efficiency by adding more filter media while still meeting the requirements for air resistance.     

基于虚拟仪器的表面肌电信号特征提取算法的研究

提出一种基于虚拟仪器的表面肌电信号的特征提取算法。该方法利用虚拟仪器丰富的函数功能,针对肌电信号的非平稳性特征,应用积分阈值法首先去除静息电位,保留最有价值的信号部分,然后利用小波包变换的方法对肌电信号进行小波包分解,根据其投影到不同频段上小波包系数能量的不同,利用能量较大的几组系数重构肌电信号。实验结果表明:该方法能有效地去除静息电位及噪声信号,且保留了肌电信号的细节信息,为肌电信号的模式识别创造了良好的条件。该研究依据虚拟仪器平台,为创建表面肌电信号实时控制机械臂系统提供了研究基础,具有潜在的工程应用价值。     

三自由度比例控制肌电假手的设计

针对电动假手的仿生控制问题,给出了一种三自由度实时比例控制肌电假手的设计方案。通过采集残臂上的4路表面肌电信号（SEMG）,采用能量时域分析法对信号进行了特征提取,并采用二叉决策树模式分类方法识别得到了手部3个自由度7个动作模式。由单片机构成的信号处理和控制电路,实现了三自由度电动假手的实时仿生控制;并根据表面肌电信号的强弱,采用多路SEMG能量加权法来求取多自由度假手的比例控制系数,实现了对电动假手的比例控制。试验结果表明,基于表面肌电信号的三自由度肌电假手响应速度快。动作准确率达到96％以上。     

基于改进小脑模型的sEMG下肢关节力矩预测

关节力矩预测在康复医学、临床医学和运动训练等领域有着重要作用,对力矩连续、实时地预测可以使人机交互设备更好地反馈、复刻人体运动意图。为了给患者提供一个安全、主动、舒适的康复训练环境,提升人机交互设备的柔顺性,提出了一种改进型递归小脑模型神经网络模型关节力矩预测方法。该方法采用肌肉协同分析对采集的相关肌肉的表面肌电信号(sEMG)进行降维,将降维后的sEMG特征向量与关节角速度、关节角度作为输入信号,并在小脑模型神经网络中加入递归单元和模糊逻辑规则,以小波函数作为隶属度函数,对非疲劳、过渡疲劳及疲劳这3种状态下的踝关节背屈跖屈运动的动态力矩进行连续预测。力矩预测值与实际值之间的平均皮尔逊相关系数和平均标准均方根误差分别为0.933 5和0.159 8,实验结果验证了该方法对下肢关节力矩连续预测的准确性和有效性。