基于多核学习的下肢肌电信号动作识别

为了提高下肢肌电控制系统中多运动模式识别的准确性,提出一种基于多核学习(MKL)和小波变换尺度间相关性特征提取的多类识别方法.根据多核学习理论,采用二叉树组合策略构造基于多核学习的多类分类器.对下肢4路表面肌电信号进行离散平稳小波变换,用小波系数尺度间的相关性提取特征向量输入构造的多类分类器,对水平行走时划分的支撑前期、支撑中期、支撑末期、摆动前期、摆动末期这5个细分运动状态进行分类.实验结果表明,所提的多模式识别方法能够以较高识别率区分多个细分运动状态,得到比标准的单核支持向量机(SVM)分类器更好的准确性.     

基于触觉反馈和表面肌电信号的随机时延夹持器遥操作方法

提出了一种基于触觉和表面肌电信号的夹持器抓取力控制遥操作方法.在控制过程中,采用了基于非时间参考的遥操作方法用于消除随机时延对系统稳定性的影响.使用了表面肌电信号作为遥操作控制信号来改善操作者的操控体验.另外,还使用了基于粒子群优化的支持向量回归模型和模糊控制器.实验结果证明,此方法对操作员的用力估计误差约为8.45%,平均跟踪误差约0.6N,可以让操作者更加稳定有效地调节夹持器的夹持力大小.     

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。     

表面肌电信号数字传感器的设计

介绍了表面肌电信号数字传感器的设计方法。根据表面肌电信号产生特点和采集技术的基本要求,研究电极的形状和正确的放置方法,采用仪用放大器INA128设计前置放大电路。设计有源滤波器,应用串行A/D转换芯片输出数字信号。实验表明,该方法可以提高信噪比,减小噪声,有效地提取出表面肌电信号。     

A pseudo-mastication sound presentation device to improve the texture of nursing care foods

The texture of foods is affected by concurrent auditory sensations. To improve the texture of nursing care foods, we developed a pseudo-mastication sound presentation device based on electromyogram (EMG) signals from the muscles of mastication. EMG signals have enabled us to promptly present care recipients with pseudo-mastication sounds. However, actual mastication sounds vary in intensity and duration more than EMG signals. Here, we investigated changes in EMG signals and actual mastication sounds during the mastication of two food types (rice crackers and Japanese pickles) to improve our device. We found that the intensity and duration of mastication sounds decreased as the number of mastication strokes increased. Furthermore, the intensity and duration of mastication sounds and the latency between the onset of EMG signals and the onset of mastication sounds also varied by food type. For EMG signals, only the intensity varied by food type. Based on our findings, we modified our pseudo-mastication sound presentation device to enable control of the intensity and duration of pseudo-mastication sounds based on the number of mastication strokes and food type. Reproducing more natural pseudo-mastication sounds can improve care recipients’ motivation for ingesting nursing care foods, thus preventing malnutrition and frailty.     

Effect of milking stall dimensions on upper limb and shoulder muscle activity in milkers

Increasing societal awareness for animal welfare can promote changes in legislation. Some of these changes may also affect the person that interacts with the animal in a shared workspace, such as in milking stalls. Swiss milking stalls were designed many years ago, when cows were smaller than they are today. A recent animal-based study indicated that welfare decreased in cows exposed to restricted space allowance in milking stalls, which had resulted from increasing body size without adjustment of milking stall dimensions. However, changing the milking stall dimensions without considering the milker may be detrimental. For many years, health issues, particularly of the upper limb and shoulders, have affected milking personnel. The current study investigated the effect of large and standard milking stall dimensions on muscle activity in milkers (as a measure of workload) during milking. This assessment is fundamental to ensure that legislation improving animal welfare does not jeopardize human health. The study took place in an experimental milking parlor that allowed for size adjustment of the individual milking stall. Nine milkers performed 2 shifts of milking in a herringbone and 2 shifts in a side-by-side milking parlor. The milking stall dimensions were large on one side and standard on the other side of the parlor; the 2 sides were switched between milking shifts. We used surface electromyography to monitor bilateral muscle activity of forearm (flexor carpi ulnaris), arm (biceps brachii), and shoulder (deltoideus anterior; upper trapezius) muscles. Statistical analysis was performed separately for the herringbone and the side-by-side parlor for each muscle using mean and maximum muscle activity as the target variables in a linear mixed-effects model. The analysis showed that the different milking stall dimensions did not consistently affect activity of the measured muscles. Our results suggest that milking stall dimensions are not a primary risk factor for poor ergonomics in parlor workers.     

适用不同Brunnstrom等级患者基于表面肌电信号的动作识别方法

针对不同患病程度的脑卒中患者运动意图识别率低的问题,提出了一种适用于不同Brunnstrom等级患者基于表面肌电信号（sEMG）的动作识别方法．首先将所有等级患者sEMG数据进行融合,使用tsfresh库提取特征,然后基于随机森林（random forest,RF）模型筛选特征,并利用筛选的特征训练动作分类模型．进一步,通过研究动作和康复等级的关系,确定了康复评估动作并设计了康复等级自动评估算法．为了验证所提方法的有效性,在24例患者sEMG数据上进行了测试,实验结果表明所提方法能够将9种动作和6类康复等级的平均识别精度分别提升至89.81%和94%．基于所提方法构建的手部康复机器人系统能够实现康复等级自动评估．