Electromyography of Eating Apples: Influences of Cooking, Cutting, and Peeling

ABSTRACT: The mastication of 7 differently prepared apple samples (raw pieces peeled and unpeeled, sliced, grated, cooked pieces with and without peel, and half-cooked pieces, 10 g each) was evaluated using electromyography (EMG). Eleven subjects participated in the EMG recording of both sides of the masseter and temporal muscles while eating samples normally. Only the grated sample reduced the number of chewing strokes and muscle activity before swallowing. In contrast, the thin apple slices produced significantly shorter contraction duration and cycle time only during the 1st 5 chewing strokes. The EMG duration and cycle in subsequent chews, as well as the other parameters, did not significantly differ between slices and pieces. Cooked apples exhibited significantly lower EMG amplitude and muscle activity per chew than their raw counterparts; however, there was no evidence of reduced total muscle activity required for swallowing. Raw and cooked apples with peel yielded significantly greater EMG amplitude and longer duration than those without peel. These findings suggest that appropriate preparation is necessary for people with various mastication abilities: grated for very low ability, cooked for those with weak chewing force, and unpeeled for mastication training.     

基于表面肌电信号的虚拟现实控制系统设计

为了减少传统康复训练的单调乏味,设计了一种利用无线表面肌电控制虚拟现实环境中厨房的实时控制系统。该系统是以MATLAB为平台,利用图形用户界面进行设计;在算法上利用均方值与移动平均窗方法对实时肌电信号进行检测与分割;采用幅值绝对值均值和小波系数奇异值作为特征值;选取支持向量机算法进行模型训练和分类识别;最后,完成对虚拟厨房的动作控制。结果表明,该系统可以完成对虚拟厨房动作的实时控制,平均识别率为90.31%。后续可用于肌肉康复训练的患者,可提供具有沉浸感和真实感的虚拟厨房生活场景,对患者的康复具有积极意义。     

An investigation into four different sit–stand workstation use schedules

Twelve office workers participated in a study investigating effects of four sit/stand schedules (90-min sit/30-min stand, 80/40, 105/15, and 60/60) via several objective and subjective measures (muscle fatigue, foot swelling, spinal shrinkage, and self-reported discomfort). Results showed that there were no significant differences in shoulder and low back static muscle activities between sitting and standing. Muscle fatigue was developed during workday under all schedules. The longest standing schedule seemed to have a tendency of reducing muscle fatigue. None of the schedules helped or worsened foot swelling and spinal shrinkage. More active break-time activities seemed reducing muscle fatigue and foot swelling. While the self-reported bodily discomfort levels were generally low, the preferred schedules among the participants were varied, although the least standing schedule was the least preferred. We may conclude that effects of using sit–stand workstation to improve musculoskeletal health may be limited but promoting more active break-time activities can help.

Practitioner Summary: Sit–stand workstations are used to reduce work-related musculoskeletal disorders. This study shows that office workers prefer sit/stand durations in the range between 1:1 and 3:1. Longer standing may have the potential to reduce muscle fatigue. However, active break-time activities may be more effective in reducing muscle fatigue and foot swelling.     

基于高阶滤波的肌电信号采集电路设计

针对表面肌电信号(sEMG)信号微弱、频率低、极易受到干扰的特点,设计了具有高共模抑制比、放大增益灵活可调、具有良好抗噪声性能的sEMG信号采集电路.信号的放大部分采用两级放大方案,以避免噪声被过分放大造成肌电信号被淹没的问题.带通滤波器部分由两组5阶Sallen-Key低通和高通滤波电路级联而成,阻带下降速度很快,近似达到-100 dB/(°),对噪声及其他生理电信号干扰的衰减能力极强.针对工频干扰问题,在陷波器部分设置Q值调节电位器,根据实测情况灵活调节电位器旋钮以便获取最佳采集效果.测试结果表明,所设计的电路放大增益可以达到60 dB,对50 Hz工频干扰有很好的抑制作用,可以有效提取20 Hz～500 Hz之间的有用信号,具有良好的抗噪声性能.     

支持向量机在肌电信号模式识别中的应用

为了提高肌电信号多运动模式识别的准确性和实时性,提出了一种基于支持向量机的动作模式分类算法.在给出支持向量机的原理及其多类问题的基本算法基础上,着重介绍了两种改进的支持向量机多类识别算法,即有向无环图算法和基于先聚类后分类的二叉树算法,并比较了它们的优缺点.实验结果表明,针对前臂肌电信号的多运动模式分类,先聚类后分类的二叉树算法具有较高的分类准确性,更少的计算量,更好的实时性.     

Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals

In this work, an attempt has been made to differentiate surface electromyography (sEMG) signals under muscle fatigue and non-fatigue conditions with multiple time window (MTW) features. sEMG signals are recorded from biceps brachii muscles of 50 volunteers. Eleven MTW features are extracted from the acquired signals using four window functions, namely rectangular windows, Hamming windows, trapezoidal windows, and Slepian windows. Prominent features are selected using genetic algorithm and information gain based ranking. Four different classification algorithms, namely naïve Bayes, support vector machines, k-nearest neighbour, and linear discriminant analysis, are used for the study. Classifier performances with the MTW features are compared with the currently used time- and frequency-domain features. The results show a reduction in mean and median frequencies of the signals under fatigue. Mean and variance of the features differ by an order of magnitude between the two cases considered. The number of features is reduced by 45% with the genetic algorithm and 36% with information gain based ranking. The k-nearest neighbour algorithm is found to be the most accurate in classifying the features, with a maximum accuracy of 93% with the features selected using information gain ranking.     

Deep Learning for EMG-based Human-Machine Interaction: A Review

Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution.Recently,many EMG pattern recognition tasks have been addressed using deep learning methods.In this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI.An overview of typical network structures and processing schemes will be provided.Recent progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be introduced.New issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be discussed.We attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep learning.We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems.Furthermore,possible future directions will be presented to pave the way for future research.     

Suboptimal Exposure to Facial Expressions When Viewing Video Messages From a Small Screen: Effects on Emotion, Attention, and Memory.

The authors examined the effects of suboptimally presented facial expressions on emotional and attentional responses and memory among 39 young adults viewing video (business news) messages from a small screen. Facial electromyography (EMG) and respiratory sinus arrhythmia were used as physiological measures of emotion and attention, respectively. Several congruency priming effects were found. In particular, happy facial primes prompted increased (a) pleasure ratings, (b) orbicularis oculi EMG activity, (c) perceived trustworthiness, and (d) recognition memory for video messages with a positive emotional tone. Emotional and other responses to video messages presented on a small screen can be modified with suboptimal affective primes, but even small differences in the emotional tone of the messages should be allowed for. (PsycINFO Database Record (c) 2010 APA, all rights reserved)