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.     

基于熵和PSO优化SVM的肌电信号跌倒识别

跌倒对老年人的健康构成严重危害,设计了一种基于肌电信号的跌倒识别方法,可用于跌倒检测报警。该方法首先对表面肌电动作信号进行小波包分解,再依据信号特征选取信号的低频分量并重构,计算其排列组合熵,最后以4路肌电信号对应的排列组合熵组成的特征向量输入SVM进行模式识别并采用粒子群算法对SVM中惩罚参数c和核函数参数g进行优化,对8种动作进行识别实验,跌倒识别率88%,特异度98.3%,平均识别率97.0%,优于网格法和遗传算法支持向量机（GA-SVM）的参数优化,具有较强的鲁棒性和抗干扰能力。     

基于谱插值与经验模态分解的表面肌电信号降噪处理*

根据表面肌电(surface electromyography, sEMG)信号的噪声特性来探讨其降噪方法的应用问题。采用谱插值法来削弱工频干扰以避免工频处的肌电信息成分丢失,再选取通过经验模态分解(empirical mode decomposition, EMD)方法获得的内在模态函数(intrinsic mode function, IMF)分量作小波软阈值分析,并将部分明显的低频IMF干扰分量及冗余分量去除,然后对相应IMF分量进行重构便可得到降噪处理后的sEMG信号。仿真和真实信号的降噪实验结果     

基于表面肌电的运动意图识别方法研究及应用综述

表面肌电信号 (Surface electromyography, sEMG) 是人体自身的资源, 蕴含着关联人体运动的丰富信息, 用它作为交互媒介以构建人机交互 (Human-robot interaction, HRI) 系统有天然的优势.通过肌电信号实现人机自然交互的关键是由肌电信号识别出人体运动意图, 通常包括离散动作模态分类、关节连续运动量估计及关节刚度/阻抗估计等三方面内容.本文详细归纳基于表面肌电的运动识别方法研究成果, 总结当前研究的特点; 随后, 介绍基于表面肌电的运动识别技术的应用现状, 并探讨制约其推广的主要问题; 最后, 展望该技术的未来发展.     

Quantification of dance movement by simultaneous measurement of body motion and biophysical information

The purpose of this research is a quantitative analysis of movement patterns of dance,which cannot be analyzed with a motion capture system alone,using simultaneous measurement of body motion and biophysical information.In this research,two kinds of same leg movement are captured by simultaneous measurement;one is a leg movement with given strength,the other is a leg movement without strength on condition of basic experiment using optical motion capture and electromyography (EMG) equipment in order to quantitatively analyze characteristics of leg movement.Also,we measured the motion of the traditional Japanese dance using the constructed system.We can visualize leg movement of Japanese dance by displaying a 3D CG character animation with motion data and EMG data.In addition,we expect that our research will help dancers and researchers on dance through giving new information on dance movement which cannot be analyzed with only motion capture.     

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。     

表面肌电信号的AR 参数模型分析方法

根据实际肌电信号的随机性特征，对其建立AR(Autoregressive)模型，得到其AR模型的各项参数，分析此系数和对应肌肉活动所确定的肢体动作之间的关系，从而得到基于动作模式的表面肌电信号(EMG)AR模型参数分析方法。     

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。     

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。