Performance of a Vertical‐Slot Fish Pass for the Sea Lamprey Petromyzon marinus L. and Habitat Recolonization

In 2011, a vertical‐slot fish pass was built at the Coimbra Açude‐Ponte dam (Mondego River, Portugal), approximately 45 km upstream from the river mouth. The performance of this infrastructure for sea lamprey passage was evaluated between 2011 and 2015 using several complementary methodologies, namely radio telemetry [conventional and electromyogram (EMG)], passive integrated transponder (PIT) telemetry and electrofishing surveys. During the study period, the electrofishing revealed a 29‐fold increase in the abundance of larval sea lamprey upstream of the fish pass. Of the 20 radio‐tagged individuals released downstream from the dam, 33% managed to find and successfully surpass the obstacle in less than 2 weeks, reaching the spawning areas located in the upstream stretch of the main river and in one important tributary. Fish pass efficiency was assessed with a PIT antenna installed in the last upstream pool and revealed a 31% efficiency, with differences between and within migratory seasons. Time of day and river flow significantly influenced the attraction efficiency of the fish pass, with lampreys negotiating it mainly during the night period and when discharge was below 50 m³ s^?1. Sea lampreys tagged with EMG transmitters took 3 h to negotiate the fish pass, during which high muscular effort was only registered during passage, or passage attempts, of the vertical slots. The use of complementary methodologies provided a comprehensive passage evaluation for sea lamprey, a species for which there is a considerable paucity of valuable data concerning behavioural, physiological and environmental influences on obstacle negotiation. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.     

二维相空间中肌电信息熵的计算

肌电信号的检测与分析对于临床诊断和康复工程具有重要的现实意义。从生理学角度上讲各种动作是肌肉的收缩产生的,同时伴有肌肉内部各运动单位电位的变化产生了肌电信号。通过对肌电信号一维时间序列的研究,发现其具备一定的混沌特性。信息熵是一个信息量的概念,在重构后的二维相空间中计算了各种肌电信号的信息熵,并对所得结果进行分析,确定了肌肉与动作之间的规律,此规律能为假肢的研究提供科学依据。     

基于脑电信号和肌电信号协同控制的智能小车系统

智能轮椅能有效提高行动不便者在日常生活中的活动能力和自理能力, 但目前智能轮椅的控制方案往往存在着控制动作困难, 响应速度慢等难题. 针对这一情况, 本文结合现有生物电机械接口技术和设备控制技术, 提出一套基于脑电信号和肌电信号协同控制的设备控制方案, 该方案允许用户仅通过4种简单的头部动作, 即使用眨眼、左咬合、右咬...     

An accurate forearm EMG signal classification method using two‐channel electrode

An accurate electromyography (EMG) classification algorithm to control a virtual hand prosthesis with 12 degrees of freedom using two surface EMG electrodes is presented in this paper. We propose the application of independent component analysis (ICA) for blind‐source separation of the EMG signals obtained from two electrodes. One of the problems affecting the EMG classification accuracy is the location dependence of the EMG signal due to the superposition of signals from multiple sources. ICA is used to separate the two signals obtained from two surface electrodes into two independent EMG signals prior to the feature extraction and classification processes. We demonstrate that the EMG classification accuracy can be improved using the ICA algorithm. We also propose a novel eigen‐based feature that is extracted from the short‐time Fourier transform (STFT) magnitude spectrum. Our new feature not only decreases feature dimensions but also performs better than other well‐known features. We also implement the EMG classification scheme on the virtual robot arm. The performance shows promising result as indicated by a decrease in the Davies–Bolden (DB) index after applying the ICA © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

5-DOF上肢康复机械臂交互式康复训练控制策略

设计一种5-DOF穿戴式偏瘫上肢康复机械臂系统,提出被动和主动两阶段的交互式康复训练控制策略。在被动训练中,根据偏瘫患者上肢单侧受损的特点,提取偏瘫患者的健侧上肢4块肌肉的表面肌电信号(Surface electromyogram, sEMG) 作为康复机械臂的控制信号,利用AR参数模型和BP神经网络来理解患者的运动意图,驱动机械臂辅助患侧上肢实现特定的康复训练动作。在主动训练中,通过实时获取各关节力矩信号来判断人体上肢的运动所产生的作用力方向与大小,并利用比例控制器和运动学雅可比逆矩阵控制康复臂末端速度、驱动各关节运动。试验结果证明了提出方法的正确性和有效性。     

Noninvasive fetal ECG estimation using adaptive comb filter

This paper describes a robust and simple algorithm for fetal electrocardiogram (FECG) estimation from abdominal signal using adaptive comb filter (ACF). The ACF can adjust itself to the temporal variations in fundamental frequency, which makes it qualified for the estimation of quasi-periodic component from physiologic signal, such as ECG. The validity and performance of the described method are confirmed through experiments on real fetal ECG data. A comparison with the well-known independent component analysis (ICA) method has also been presented.     

基于非线性特征的表面肌电信号模式识别方法

该文提出一种计算非线性时间序列信号Lyapunov指数的新方法球均值Lyapunov指数计算法,用于肢体肌电信号的特征提取与分类。首先采用所提方法计算出肌电信号的Lyapunov指数,并与关联维组合构成输入特征向量,然后用二叉树法构造基于对支持向量机的多类分类器,对握拳、展拳、腕内旋、腕外旋4类动作模式进行分类识别。实验结果表明,该方法不仅具有比 Rosenstein 算法更强的抗干扰能力,而且在肌电信号的特征提取与分类应用中取得96.0%以上的识别率,适合于分析信噪比较低的混沌信号。     

融合脑电-肌电信号的人机协作装配意图识别方法

针对人机协作装配场景下基于单源生理电信号识别协作意图准确率不高、稳定性不好的问题,首先采用支持向量机方法分别从脑电信号和肌电信号识别单源协作装配意图,然后采用D-S证据理论对多源协作装配意图识别结果进行融合,提出了一种融合EEG-EMG信号的人机协作装配意图识别方法。实验结果表明,所提方法可以有效提高人机协作装配意图识别的准确率和稳定性。     

基于小波包的表面肌电信号特征表示与识别

为提高智能轮椅人机接口中表面肌电信号的正确识别率和识别效率,主要研究了基于小波包多尺度分解的特征表示及识别。把采集的表面肌电信号在指定尺度及核函数的同一组正交小波包基下进行分解,用小波包多尺度分解的系数构造表面肌电信号的特征基向量。考虑到多通道表面肌电信号可能存在特征信息冗余,为消除这些冗余信息,对多通道表面肌电信号的特征空间通过正交规范化进行重构,并且用重构特征向量的对偶坐标向量作为表面肌电信号的最终特征表示。用非线性自回归神经网络实现了双通道表面肌电信号四种不同动作模式的分类。实验结果表明,小波包多尺度分解系数的重构对偶坐标向量不仅可作为表面肌电信号的特征表示,并能有效简化分类器的结构。     

基于人体动作反馈的上肢康复机器人主动感知系统

提出了基于人体动作命令反馈的主动康复训练方法．首先,对采集到的4通道上肢肌电信号进行小波包分解,提取小波系数的log特征,输入到神经网络辨识,对上肢的8种常用动作取得了92.86%的辨识成功率．随后,将辨识结果动作以虚拟动画的形式展示给患者,患者针对辨识动作进行点头确认或者摇头拒绝．采用Kinect获取患者的动作视频,经过人脸辨识缩小检测范围,利用光流特征与先验经验结合,对比分析了投票法、Sigmoid法与tanh法的动作辨识结果,在人为干扰的条件下,Sigmoid法仍然表现优良．设计了Sigmoid法的参数修正方法,最终准确度达87.25%,无危险性误判率为12.75%．经过主动康复实验,本文提出的基于人体动作命令反馈的主动康复训练意图感知方法具有良好效果,适用于主动康复训练场合．