Total forearm support during a typing task may reduce the risk of Trapezius' Myalgia development

ObjectiveEvaluate the influence of alternating the position of the upper limbs, between fully supported and unsupported forearms, in the Upper Trapezius (UT) activity during a typing task on a straight-edged desk.BackgroundErgonomic barriers, such as reduced desk area, is one of the reasons that force computer users to work without supporting their forearms. Unsupported forearms may lead to increased UT muscle fatigue, increasing the potential for lesions, with Trapezius Myalgia (TM) being a possible outcome.Method15 healthy volunteers were assessed (6 females, average age of 3,7 ± 9,5 years old). The protocol included an alternated position of forearms every 5 min between fully supported and unsupported forearms, with a 20-min total duration of a typing task. Surface electromyography readings were collected from both UTs.ResultsSignificant differences were found in the variation of the EMG signal between the two positions for the non-dominant arm after 10 min (p < .05) of typing. The non-dominant UT registered higher levels of activity than the dominant UT. Supported forearms reduced the electrical activity in both UTs, with a greater difference in the non-dominant.ConclusionThis study consolidates the current knowledge that unsupported upper limb during typing tasks significantly increases UT's electrical activity. By fully supporting the forearm, that activity is reduced. Females and the non-dominant UT showed higher electrical activity, potentially increasing the risk of developing TM.ApplicationHealthcare providers, safety and health professionals, and ergonomists should be mindful of the forearm position when advising computer users to prevent TM.     

sEMG-based continuous estimation of joint angles of human legs by using BP neural network

In this paper, we propose an mth order nonlinear model to describe the relationship between the surface electromyography (sEMG) signals and the joint angles of human legs, in which a simple BP neural network is built for the model estimation. The inputs of the model are sEMG time series that have been processed, and the outputs of the model are the joint angles of hip, knee, and ankle. To validate the effectiveness of the BP neural network, six able-bodied people and four spinal cord injury (SCI) patients participated in the experiment. Two movement modes including the treadmill exercise and the leg extension exercise at different speeds and different loads were respectively conducted by the able-bodied individuals, and only the treadmill exercise was selected for the SCI patients. Seven channels of sEMG from seven human leg muscles were recorded and three joint angles including the hip joint, knee joint and the ankle joint were sampled simultaneously. The results present that this method has a good performance on joint angles estimation by using sEMG for both able-bodied subjects and SCI patients. The average angle estimation root-mean-square (rms) error for leg extension exercise is less than 9°, and the average rms error for treadmill exercise is less than 6° for all the able-bodied subjects. The average angle estimation rms error of the SCI patients is even smaller (less than 5°) than that of the able-bodied people because of a smaller movement range. This method would be used to rehabilitation robot or functional electrical stimulation (FES) for active rehabilitation of SCI patients or stroke patients based on sEMG signals.     

基于EEMD和二代小波变换的表面肌电信号消噪方法

为了更好地消除混杂在表面肌电信号(sEMG)中的噪声,提出了一种基于总体平均经验模式分解(EEMD)和二代小波变换的sEMG消噪新方法。首先对信号加入白噪声处理后进行经验模态分解(EMD),然后对高频的内蕴模式函数(IMF)分量进行二代小波阈值消噪处理,最后把处理后的高频IMF分量与低频IMF分量进行叠加,重构后的信号即为去噪信号。实验结果表明,该方法融合了二代小波与EEMD的优点,能更好的消除噪声,最大限度的保留有用信号,并具有更高的信噪比。     

基于柔性神经树和表面肌电信号的手势识别模型

表面肌电信号因为具有非入侵式、易于采集特点,被广泛应用于康复医疗和行为识别等领域.传统的基于表面肌电信号sEMG( Surface Electromyography)的手势识别通常采用数字信号处理DSP( Digital Signal Processing)芯片或者集合方法研究实时识别问题.这些方法易导致数学模型参数繁多、硬件连接复杂和实时识别率较低.提出一种基于肌电信号与柔性神经树FNT(Flexible Neural Trees)模型的实时手势识别模型.柔性神经树模型通过简单的预定义建立,能够解决人工神经网络ANN( Artificial Neural Network)的结构高依赖性问题.柔性神经树模型不仅能够避免复杂的计算和电路连接,还具有较高的实时识别率.针对六名参与者的六种手势进行实验,实验结果表明:该模型的均方根误差RMSE(Root Mean Square Error)最低为0.000385,实时识别率最高可达97.53％.     

改进 S 变换谱估计的动态手势肌电特征处理

手语是各种手势动态变化的一种复杂运动模式,手势特征处理效果直接关系到手语识别的准确性。本文提出一种基于改进S变换谱估计的动态手势肌电特征处理新方法。对采集的表面肌电信号进行S变换,引入优化因子调节时频分辨率并生成改进S变换谱;定义谱的时间和频率分量为二维随机变量,以改进S变换谱元素为二维随机变量样本,通过高斯核密度估计得到二维核密度函数。仿真和实验均表明,改进S变换谱估计方法有效抑制了白噪声,并使动态手势的肌电暂态突变特征得到加强。与经验模态分解、自排序熵、奇异值排序熵等方法对比,基于该方法的动态手势识别率分别提高了10.0%、6.67%和11.67%,特征处理方法的效果明显。     

基于 sEMG 和肌肉骨骼模型的手指 多关节力矩耦合分析

手指内部力矩受表面肌电信号、肌力、手部姿态等因素影响而无法直接获取,为了实时且准确地获取手指各关节力矩以 及耦合力矩并应用于手部康复机器人的交互控制中,提出了一种基于表面肌电信号和肌肉骨骼模型的手指多关节力矩和耦合 力矩分析与实时获取方法。 首先设计了自适应手指关节角度采集系统,通过实验同步采集指浅屈肌与指伸肌的肌电信号以及 手指各关节的角度数据,建立手指多关节力矩模型,从而获取手指各关节力矩。 然后建立手指 D-H 模型,结合虚功原理获取手 指的耦合力矩。 最后,辨识了手指多关节力矩模型的参数,并通过 OpenSim 软件获取了仿真力矩。 计算力矩与仿真力矩的对比 结果显示:4 名被试 3 个关节力矩的均方根误差分别为 0. 156 7、0. 097 425、0. 084 95,证明了该方法能够实时并准确的获取手指 各关节力矩和耦合力矩,能够满足手部康复机器人交互控制准确性和实时性的需求。     

基于肌肉协同激活模型的上肢关节运动连续估计

关节运动连续估计为基于表面肌电信号的人机交互提供了一种更为自然灵活的方式。提出了一种基于肌肉协同理论和支持向量回归的激活模型进行上肢关节角度的估计。首先利用非负矩阵分解算法对肌电信号进行解耦,提取独立动作的协同元;然后根据非负最小二乘算法计算相应协同元激活系数;最后通过支持向量回归构建了映射激活系数到关节角度的激活模型,利用建立的激活模型从采集的表面肌电信号得到关节运动的连续估计。对2个关节独立和组合运动的估计实验表明,该模型能获得较高的估计精度。     

便携式低噪声sEMG调理电路设计

针对sEMG采集中传统调理电路结构复杂、体积大、功耗高、固有噪声高的问题,在对调理电路进行功能分析、噪声分析的基础上,对典型调理电路的结构进行优化和合并。替换反向积分电路、右腿驱动电路、射频抑制电路、去除硬件工频滤波电路,增强其输入直流电压容错性,减少元件数量,降低其固有噪声、功耗。实验表明,设计的sEMG调理电路噪声抑制、便携性,均达到国际主流水准。     

柔性同心圆差分阵列表面肌电电极研制

设计了一种阵列式表面肌电信号采集系统.整个采集系统包括表面肌电电极阵列和信号调理电路两部分.电极阵列基于柔性印刷板工艺,电极载体材料(聚酰亚胺,130 μm)具有较高的机械柔性,适用于各种弯曲的皮肤表面.肌电电极采用同心圆差分的空间滤波结构,可有效提高表面肌电信号的MUAP区分度.信号调理电路对电极采到的肌电信号进行放大和滤波.实验表明,所设计的电极装置可以采集到MUAP分辨力较高的表面肌电信号,并适用于脸部肌肉等弯曲度比较大的部位.     

上肢康复机器人实验平台肌电信号采集系统的设计

表面肌电信号是肌肉收缩的同时伴随的一种电压信号,是一种复杂的表皮下肌电信号活动在皮肤表面处的时间和空间上综合得出的结果,能够反映出神经、肌肉的功能状态。正是其在相同肌群规律性和在不同肌群差异性,使得利用肌电信号作为人机接口来控制上肢康复机器人成为可能。本文的主要内容是肌电信号采集系统的设计,将从硬件电路以及软件设计两部分进行阐述。其中硬件电路主要由表面电极、信号调理、NI-USB-6210数据采集卡和上位机四部分组成;系统软件采用虚拟仪器开发平台LabVIEW编程,完成肌电信号实时采集、滤波处理、数据存储等功能。