上肢经肱骨截肢神经功能重建研究

表面肌电信号是一种安全、非侵入的电生理信息,作为实现直觉控制多功能肌电假肢系统的信息源而被广泛应用。由于经肱骨截肢者截肢的程度较高,残留的肢体肌肉少,缺乏足够的肌电信息源,无法实现多功能肌电假肢的直觉控制。目前现有技术是通过采用靶向肌肉神经功能重建的方法重建缺失肌电信息源。但目前国内尚未有关于截肢者残端神经功能重建方法的相关研究。因此,文章提出一种新型的神经吻合技术——目标神经功能替代术：采用靶向肌肉神经功能重建术与目标神经功能替代术相结合的方法,首次在国内对经肱骨截肢者成功实施了神经功能重建手术,成功建立了经肱骨截肢者神经功能重建模型,重建了因截肢而丧失的肌电信息。并采用高密度肌电技术对术前和术后的手-腕-肘部动作进行肌电信号采集,通过动作分类识别的准确率验证了该手术后肌电信息源重建的可靠性。这些结果初步验证了该方法可以为经肱骨截肢者残肢重建缺失肢体神经功能,并为直觉控制多功能肌电假肢提供潜在的信息源。     

Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease

In this study, fuzzy clustering complex-valued neural network (FCCVNN) was proposed to classify portal vein Doppler signals recorded from 54 patients with cirrhosis and 36 healthy subjects. This proposed neural network is a new model for biomedical pattern classification. The FCCVNN was composed of three phases: fuzzy clustering, calculation of FFT values and complex-valued neural network (CVNN). In first phase, fuzzy clustering was done to reduce the number of segments in training pattern. After that, FFT values of Doppler signals were calculated for pre-processing and then obtained values, which include real and imaginary components, were used as the inputs of the CVNN for classification of Doppler signals. Classification results of FCCVNN were evaluated by the different performance evaluation criterion in literature. It shows that Doppler signals were classified successfully with 100% correct classification rate using the proposed method. Moreover, the rates of sensitivity and specificity were calculated as 100% using FCCVNN method. These results were seen to be appropriate with the expected results that are derived from physician’s direct diagnosis. This method would be assisted the physician to make the final decision.     

Hand movement recognition based on biosignal analysis

This paper proposes a methodology that analyses and classifies the electromyographic (EMG) signals using neural networks to control multifunction prostheses. The control of these prostheses can be made using myoelectric signals taken from surface electrodes. Finger motions discrimination is the key problem in this study. Thus the emphasis, in the proposed work, is put on myoelectric signal processing approaches. The EMG signals classification system was established using the linear neural network. The experimental results show a promising performance in classification of motions based on biosignal patterns.     

The Development of a Virtual Myoelectric Prosthesis Controlled by an EMG Pattern Recognition System Based on Neural Networks

One of the major difficulties faced by those who are fitted with prosthetic devices is the great mental effort needed during the first stages of training. When working with myoelectric prosthesis, that effort increases dramatically. In this sense, the authors decided to devise a mechanism to help patients during the learning stages, without actually having to wear the prosthesis. The system is based on a real hardware and software for detecting and processing electromyografic (EMG) signals. The association of autoregressive (AR) models and a neural network is used for EMG pattern discrimination. The outputs of the neural network are then used to control the movements of a virtual prosthesis that mimics what the real limb should be doing. This strategy resulted in rates of success of 100% when discriminating EMG signals collected from the upper arm muscle groups. The results show a very easy-to-use system that can greatly reduce the duration of the training stages.     

基于递归图和卷积神经网络的脉象分析识别

在脉象信号分析识别中,时域、频域等分析方法难以挖掘脉象信号的非线性信息,且传统机器学习方法需要人工定义特征,无法进行特征的自学习。提出一种基于无阈值递归图和卷积神经网络的脉象分析识别方法。基于非线性动力学理论,将脉象信号转换为无阈值递归图,通过VGG-16卷积神经网络实现递归图非线性特征的自动提取,并建立脉象分类模型。实验结果表明,该方法分类准确率可达98.14%,与已有的脉象分类方法相比有所提升。该研究为脉象信号分类提供了一种新的思路和方法,对脉诊客观化具有一定的实用价值。     

A comparative study of wavelet families for classification of wrist motions

The selection of most suitable mother wavelet function is still an open research problem in various signal and image processing applications. This paper presents a comparative study of different wavelet families (Daubechies, Symlets, Coiflets, and Biorthogonal) for analysis of wrist motions from electromyography (EMG) signals. EMG signals are decomposed into three levels using discrete wavelet packet transform. From the decomposed EMG signals, root mean square (RMS) value, autoregressive (AR) model coefficients (4th order) and waveform length (WL) are extracted. Two data projection methods such as principal component analysis (PCA) and linear disciminant analysis (LDA) are used to reduce the dimensionality of the extracted features. Probabilistic neural network (PNN) and general regression neural network (GRNN) are employed to classify the different types of wrist motions, which gives a promising accuracy of above 99%. From the analysis, we inferred that ‘Biorthogonal’ and ‘Coiflets’ wavelet families are more suitable for accurate classification of EMG signals of different wrist motions.     

A Study on Kinematics and Workspace Determination of a General 6-<Emphasis Type="Underline">P</Emphasis> US Robot

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels.     

基于小波变换的多特征融合sEMG模式识别

针对单一特征值表征能力差的情况,根据小波变换的多分辨分析思想,采用基于多种母小波的多特征融合的特征提取方法对表面肌电信号进行特征提取。本实验对十名测试人员进行肌电信号的采集,对日常生活中的四个基本下肢动作进行测试。首先,分别基于DB、Dmey和Bior三种不同的母小波,采用离散小波变换通过不同的分析方法对表面肌电信号进行多尺度分解。然后,通过分析发现,不同肌肉在不同特征提取方式下表征效果存在差异,为了结合不同特征方式的特点对基于不同小波基的特征值进行融合分析并比较。最后,将特征值分别输入到Elman神经网络和BP神经网络进行模式识别并比较分析。实验结果表明：通过对不同特征值进行识别比较,融合处理的特征值可以达到98.7%的识别率,并且,BP神经网络相较于Elman神经网络识别效果更好。     

基于分级神经网络的Web文档模糊聚类技术

给出了一种多层向量空间模型，该模型将一篇文档的相关信息从逻辑上划分为多个相对独立的文本段，按照不同位置的文本段确定相应的索引项权重．然后提出了一种简明而有效的基于分级神经网络的模糊聚类算法．与现有方法不同，该模糊聚类方法采用自组织神经网络和模糊聚类网络两部分组成的3层神经网络来实现．首先采用自组织神经网络从原始数据产生一个初始聚类结果，然后运用FCM方法对初始聚类的数目进行优化．实验结果表明，提出的Web文档聚类算法具有较好的聚类特性，它能将与一个主题相关的web文档较完全和准确地聚成一类．     

General fuzzy min-max neural network for clustering andclassification

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given