Combined influence of forearm orientation and muscular contraction on EMG pattern recognition

The performance of intelligent electromyogram (EMG)-driven prostheses, functioning as artificial alternatives to missing limbs, is influenced by several dynamic factors including: electrode position shift, varying muscle contraction level, forearm orientation, and limb position. The impact of these factors on EMG pattern recognition has been previously studied in isolation, with the combined effect of these factors being understudied. However, it is likely that a combination of these factors influences the accuracy. We investigated the combined effect of two dynamic factors, namely, forearm orientation and muscle contraction levels, on the generalizability of the EMG pattern recognition. A number of recent time- and frequency-domain EMG features were utilized to study the EMG classification accuracy. Twelve intact-limbed and one bilateral transradial (below-elbow) amputee subject were recruited. They performed six classes of wrist and hand movements at three muscular contraction levels with three forearm orientations (nine conditions). Results indicate that a classifier trained by features that quantify the angle, rather than amplitude, of the muscle activation patterns perform better than other feature sets across different contraction levels and forearm orientations. In addition, a classifier trained with the EMG signals collected at multiple forearm orientations with medium muscular contractions can generalize well and achieve classification accuracies of up to 91%. Furthermore, inclusion of an accelerometer to monitor wrist movement further improved the EMG classification accuracy. The results indicate that the proposed methodology has the potential to improve robustness of myoelectric pattern recognition.     

Classification of EMG signals using combined features and soft computing techniques

The motor unit action potentials (MUPs) in an electromyographic (EMG) signal provide a significant source of information for the assessment of neuromuscular disorders. Since recently there were different types of developments in computer-aided EMG equipment, different methodologies in the time domain and frequency domain has been followed for quantitative analysis of EMG signals. In this study, the usefulness of the different feature extraction methods for describing MUP morphology is investigated. Besides, soft computing techniques were presented for the classification of intramuscular EMG signals. The proposed method automatically classifies the EMG signals into normal, neurogenic or myopathic. Also, multilayer perceptron neural networks (MLPNN), dynamic fuzzy neural network (DFNN) and adaptive neuro-fuzzy inference system (ANFIS) based classifiers were compared in relation to their accuracy in the classification of EMG signals. Concerning the impacts of features on the EMG signal classification, different results were obtained through analysis of the soft computing techniques. The comparative analysis suggests that the ANFIS modelling is superior to the DFNN and MLPNN in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability.     

Real‐time and user‐independent feature classification of forearm using EMG signals

Electromyography (EMG) signals contain various information about human motion. How to extract the EMG signals of the human body by appropriate methods for classification is a hot issue in current research. Unfortunately, the main problem with the classification of EMG signals is that only certain actions can be identified. Once the individual is changed, the recognition accuracy rate will be greatly reduced. This study introduces a method for classifying the forearm using back propagation (BP) neural networks. This mode extracted five features of the EMG signals. Participants were required to train their own actions during the test. Six participants selected four to six actions to identify them, and the average accuracy was more than 90%. The results suggest that the method can be used among different individuals and provides a good classification method.     

EMG Based Control of Transhumeral Prosthesis Using Machine Learning Algorithms

This research presents work on control of a prosthetic arm using surface electromyography (sEMG) signals acquired from triceps and biceps of fifteen healthy and four amputated subjects. Myo armband was used to acquire sEMG signals corresponding to four different arm motions: elbow extension, elbow flexion, wrist pronation, and wrist supination. Ten time-domain features were extracted and considered for classification to recognize the four-arm motions. These features and their various combinations were used to train four different classifiers, in both offline and real-time settings. It was found that the combination of signal mean and waveform length as a feature and k-nearest neighbors as classifier performed significantly better (p < 0.05) than all other combinations in both offline and real-time settings. The offline accuracies of 95.8% and 68.1% and real-time accuracies of 91.9% and 60.1% were obtained for healthy and amputated subjects, respectively. Results obtained using the presented scheme successfully demonstrate that using suitable features and classifier, classification accuracies can be significantly improved for transhumeral prosthesis, thereby, providing better, wearable and non-invasive control of prostheses using sEMG signals.

     

Diversity-based combination of non-parametric classifiers for EMG signal decomposition

Non-parametric classification procedures based on a certainty measure and nearest neighbour rule for motor unit potential classification (MUP) during electromyographic (EMG) signal decomposition were explored. A diversity-based classifier fusion approach is developed and evaluated to achieve improved classification performance. The developed system allows the construction of a set of non-parametric base classifiers and then automatically chooses, from the pool of base classifiers, subsets of classifiers to form candidate classifier ensembles. The system selects the classifier ensemble members by exploiting a diversity measure for selecting classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between base classifier outputs, i.e., to measure the degree of decision similarity between base classifiers. The pool of base classifiers consists of two kinds of classifiers: adaptive certainty-based classifiers (ACCs) and adaptive fuzzy k-NN classifiers (AFNNCs) and both utilize different types of features. Once the patterns are assigned to their classes, by the classifier fusion system, firing pattern consistency statistics for each class are calculated to detect classification errors in an adaptive fashion. Performance of the developed system was evaluated using real and simulated EMG signals and was compared with the performance of the constituent base classifiers and the performance of the fixed ensemble containing the full set of base classifiers. Across the EMG signal data sets used, the diversity-based classifier fusion approach had better average classification performance overall, especially in terms of reducing classification errors.     

Identification of EMG signals using discriminant analysis and SVM classifier

The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. In this paper, a classification technique is proposed to classify signals required for a prosperous arm prosthesis control by using surface EMG signals. This work uses recorded EMG signals generated by biceps and triceps muscles for four different movements. Each signal has one single pattern and it is essential to separate and classify these patterns properly. Discriminant analysis and support vector machine (SVM) classifier have been used to classify four different arm movement signals. Prior to classification, proper feature vectors are derived from the signal. The feature vectors are generated by using mean absolute value (MAV). These feature vectors are provided as inputs to the identification/classification system. Discriminant analysis using five different approaches, classification accuracy rates achieved from very good (98%) to poor (96%) by using 10-fold cross validation. SVM classifier gives a very good average accuracy rate (99%) for four movements with the classification error rate 1%. Correct classification rates of the applied techniques are very high which can be used to classify EMG signals for prosperous arm prosthesis control studies.     

Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals

A fundamental component of many modern prostheses is the myoelectric control system, which uses the electromyogram (EMG) signals from an individual’s muscles to control the prosthesis movements. Despite the extensive research focus on the myoelectric control of arm and gross hand movements, more dexterous individual and combined fingers control has not received the same attention. The main contribution of this paper is an investigation into accurately discriminating between individual and combined fingers movements using surface EMG signals, so that different finger postures of a prosthetic hand can be controlled in response. For this purpose, two EMG electrodes located on the human forearm are utilized to collect the EMG data from eight participants. Various feature sets are extracted and projected in a manner that ensures maximum separation between the finger movements and then fed to two different classifiers. The second contribution is the use of a Bayesian data fusion postprocessing approach to maximize the probability of correct classification of the EMG data belonging to different movements. Practical results and statistical significance tests prove the feasibility of the proposed approach with an average classification accuracy of ≈90% across different subjects proving the significance of the proposed fusion scheme in finger movement classification.     

Hierarchical projection regression for online estimation of elbow joint angle using EMG signals

Exoskeletons offer great facilities to the elderly and disabled people with respect to extending their moving ranges and reacting to certain physical activities. Electromyogram (EMG) signals, which are derived from the neuromuscular system, provide an important access to the human-robot interface. On one hand, EMG signals can be used for real-time estimation of the motion intention of human body, e.g., the current joint angle status. On the other hand, however, the process of the mass EMG data, which are captured instantaneously from skin surface, challenges the state-of-the-art technology. Because its non-stationary and randomness, it is difficult to extract the valuable and stable features from the raw EMG signals. This paper investigates into the learning process of high dimensional EMG signals with a hierarchical mechanism that projects the original data into a lower feature space to achieve a local refined mapping from the EMG signals to the motion states of the human body. This hierarchically projected regression algorithm constructs incrementally a tree-based knowledge library, whose components represent local regression models. The components will be retrieved online efficiently and contribute to the estimation of the motion states. A great number of experiments are carried out to evaluate the accuracy of this novel algorithm.     

基于虚拟现实和动态加权决策融合的恐高情绪识别

目前恐高情绪分类中的生理信号主要涉及脑电、心电、皮电等, 考虑到脑电在采集和处理上的局限性以及多模态信号间的融合问题, 提出一种基于6种外周生理信号的动态加权决策融合算法. 首先, 通过虚拟现实技术诱发被试不同程度的恐高情绪, 同步记录心电、脉搏、肌电、皮电、皮温和呼吸这6种外周生理信号; 其次, 提取信号的统计特征和事件相关特征构建恐高情感数据集; 再次, 根据分类性能、模态和跨模态信息提出一种动态加权决策融合算法, 从而对多模态信号进行有效整合以提高识别精度. 最后, 将实验结果与先前相关研究进行对比, 同时在开源的WESAD情感数据集进行验证. 结论表明, 多模态外周生理信号有助于恐高情绪分类性能的提升, 提出的动态加权决策融合算法显著提升了分类性能和模型鲁棒性.     

EMG pattern classification using SOFMs for hand signal recognition

 We propose a method of pattern classification of electromyographic (EMG) signals using a set of self- organizing feature maps (SOFMs). The proposed method is simple to apply in that the EMG signals are directly input to the SOFMs without preprocessing. Experimental results are presented that show the effectiveness of the SOFM based classifier for the recognition of the hand signal version of the Korean alphabet from EMG signal patterns.     

Fractal analysis features for weak and single-channel upper-limb EMG signals

Electromyography (EMG) signals are the electrical manifestations of muscle contractions. EMG signals may be weak or at a low level when there is only a small movement in the major corresponding muscle group or when there is a strong movement in the minor corresponding muscle group. Moreover, in a single-channel EMG classification identifying the signals may be difficult. However, weak and single-channel EMG control systems offer a very convenient way of controlling human–computer interfaces (HCIs). Identifying upper-limb movements using a single-channel surface EMG also has a number of rehabilitation and HCI applications. The fractal analysis method, known as detrended fluctuation analysis (DFA), has been suggested for the identification of low-level muscle activations. This study found that DFA performs better in the classification of EMG signals from bifunctional movements of low-level and equal power as compared to other successful and commonly used features based on magnitude and other fractal techniques.     

Classification of surface EMG signal using relative wavelet packet energy

Features can be classified into interferential features and discriminable features according to their contribution to pattern recognition. In this paper, a novel and simple method based on wavelet packet transform is proposed to extract the features from surface EMG signal. In this method, the features are relative wavelet packet energy (RWPE), which is evaluated from several selected frequency bands of surface EMG signal. Compared with a conventional method, which is of the best performance in previous applications, the method can compress the interferential features and enhance the discriminable features more effectively. In consequence, the RWPE features calculated by the method represent different patterns of surface EMG signal more accurately and the accuracy of surface EMG signal pattern classification is improved greatly.     

基于小波变换的肌电信号识别方法研究

针对肌电信号的非平稳特性,采用小波变换方法对表面肌电信号进行分析,提取小波系数最大值构造特征矢量输入神经网络分类器进行模式识别,经过训练能够成功地从掌长肌和肱桡肌采集的两道表面肌电信号中识别展拳、握拳、前臂内旋、前臂外旋四种运动模式。实验表明,基于小波变换的神经网络分类方法所需的数据短、运算快,对于肌电假肢的控制具有良好的应用前景。     

Boosting image classification with LDA-based feature combination for digital photograph management

Image classification is of great importance for digital photograph management. In this paper we propose a general statistical learning method based on boosting algorithm to perform image classification for photograph annotation and management. The proposed method employs both features extracted from image content (i.e., color moment and edge direction histogram) and features from the EXIF metadata recorded by digital cameras. To fully utilize potential feature correlations and improve the classification accuracy, feature combination is needed. We incorporate linear discriminant analysis (LDA) algorithm to implement linear combinations between selected features and generate new combined features. The combined features are used along with the original features in boosting algorithm for improving classification performance. To make the proposed learning algorithm more efficient, we present two heuristics for selective feature combinations, which can significantly reduce training computation without losing performance. The proposed image classification method has several advantages: small model size, computational efficiency and improved classification performance based on LDA feature combination.     

应用小波变换和ICA方法的肌电信号分解

基于单通道、短时真实肌电（EMG）记录和模拟EMG信号，提出一种改进的肌电信号分解方法。首先应用小波滤波、硬阈值估计等方法去除背景噪声和白噪声，并将独立成分分析（ICA）方法和小波滤波方法相结合去除工频干扰信号，然后再进行幅度滤波，从而提高了系统的速度和强健性。在运动单元动作电位（MUAP）聚类以及从原始信号中去除已识别的MUAP波形等方面也进行了改进。与已有的EMG分解方法相比，本文方法更快速、稳定。     

Wearing-independent hand gesture recognition method based on EMG armband

Electromyographic (EMG) armband with electrodes mounted around the user’s forearm is one of the most ergonomic wearable EMG devices and is used to recognize fine hand gesture with great popularity. Definitely, the distributions of signal differ greatly in different wearing positions of armband based on the physiological characters of EMG, which will cause the performance decline and even the inapplicability of the recognition model built in one position. Hence, this paper proposes a wearing-independent hand gesture recognition method based on EMG armband. To eliminate the influence of wearing position, Standard Space is proposed in this paper. Based on the sequential features of EMG in different scales, the wearing position of armband is predicted and helps unify the original features to the proposed space. Then, with the unified signals, fine hand gesture can be recognized accurately and robustly with lightweight Random Forest (RF). The experimental results showed that the recognition accuracy of the proposed method was 91.47% approximately. And compared with the method without fine feature extraction and feature space unification, the performance was improved by 10.12%.     

Feature reduction and selection for EMG signal classification

Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.     

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG,EOG, AND EMG EXTRACTED FEATURES

A new system for sleep multistage level scoring by employing extracted features from twenty five polysomnographic recording is presented. For the new system, an adaptive neuro-fuzzy inference system (ANFIS) is developed for each sleep stage. Initially, three types of electrophysiological signals including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were collected from twenty five healthy subjects. The input pattern used for training the ANFIS subsystem is a set of extracted features based on the entropy measure which characterize the recorded signals. Finally an output selection subsystem is utilized to provide the appropriate sleep stage according to the ANFIS stage subsystems outputs. The developed system was able to provide an acceptable estimation for six sleep stages with an average accuracy of about 76.43% which confirmed its ability for multistage sleep level scoring based on the extracted features from the EEG, EOG and EMG signals compared to other approaches.     

Affect prediction from physiological measures via visual stimuli

This study aims to predict different affective states via physiological measures with three types of computational models. An experiment was designed to elicit affective states with standardized affective pictures when multiple physiological signals were measured. Three data mining methods (i.e., decision rules, k-nearest neighbours, and decomposition tree) based on the rough set technique were then applied to construct prediction models from the extracted physiological features. We created three types of prediction models, i.e., gender-specific (male vs. female), culture-specific (Chinese vs. Indian vs. Western), and general models (participants with different genders and cultures as samples), and direct comparisons were made among these models. The best average prediction accuracies in terms of the F₁ measures (the harmonic mean of precision and recall) were 60.2%, 64.9%, 63.5% for the general models with 14, 21, and 42 samples, 78.0% for the female models, 75.1% for the male models, 72.0% for the Chinese models, 73.0% for the Indian models, and 76.5% for the Western models, respectively. These results suggested that the specific models performed better than did the general models.     

Expert systems for time-varying biomedical signals using eigenvector methods

In this paper, we present the expert systems for time-varying biomedical signals classification and determine their accuracies. The combined neural network (CNN), mixture of experts (ME), and modified mixture of experts (MME) were tested and benchmarked for their performance on the classification of the studied time-varying biomedical signals (ophthalmic arterial Doppler signals, internal carotid arterial Doppler signals and electroencephalogram signals). Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The inputs of these expert systems composed of diverse or composite features were chosen according to the network structures. The present study was conducted with the purpose of answering the question of whether the expert system with diverse features (MME) or composite feature (CNN, ME) improve the capability of classification of the time-varying biomedical signals. The purpose was to determine an optimum classification scheme for the problem and also to infer clues about the extracted features. Our research demonstrated that the power levels of power spectral density (PSD) estimations obtained by the eigenvector methods are the valuable features which are representing the time-varying biomedical signals and the CNN, ME, and MME trained on these features achieved high classification accuracies.