Application Of Gabor Transform In The Classification Of Myoelectric Signal

In recent day, Electromyography (EMG) signal are widely applied in myoelectric control. Unfortunately, most of studies focused on the classification of EMG signals based on healthy subjects. Due to the lack of study in amputee subject, this paper aims to investigate the performance of healthy and amputee subjects for the classification of multiple hand movement types. In this work, Gabor transform (GT) is used to transform the EMG signal into time-frequency representation. Five time-frequency features are extracted from GT coefficient. Feature extraction is an effective way to reduce the dimensionality, as well as keeping the valuable information. Two popular classifiers namely k-nearest neighbor (KNN) and support vector machine (SVM) are employed for performance evaluation. The developed system is evaluated using the EMG data acquired from the publicy available NinaPro Database. The results revealed that the extracting GT features can achieve promising performance in the classification of EMG signals

A Comparative Analysis Of Wavelet Families For The Classification Of Finger Motions

Wavelet transform (WT) has been widely used in biomedical, rehabilitation and engineering applications. Due to the natural characteristic of WT, its performance is mostly depending on the selection of mother wavelet function. A proper mother wavelet ensures the optimum performance; however, the selection of mother wavelet is mostly empirical and varies according to dataset. Hence, this paper aims to investigate the best mother wavelet of discrete wavelet transform (DWT) and wavelet packet transform (WPT) in the classification of different finger motions. In this study, twelve mother wavelets are evaluated for both DWT and WPT. The electromyography (EMG) data of 12 finger motions are acquired from online database. Four useful features are extracted from each recorded EMG signal via DWT and WPT transformation. Afterward, support vector machine (SVM) and linear discriminate analysis (LDA) are employed for performance evaluation. Our experimental results demonstrate Bior3.3 to be the most suitable mother wavelet in DWT. On the other hand, WPT with Bior2.2 overtakes other mother wavelets in the classification of finger motions. The results obtained suggest that Biorthogonal families are more suitable for accurate EMG signals classification

Classification Of Hand Movements Based On Discrete Wavelet Transform And Enhanced Feature Extraction

Extraction of potential electromyography (EMG) features has become one of the important roles in EMG pattern recognition. In this paper, two EMG features, namely, enhanced wavelength (EWL) and enhanced mean absolute value (EMAV) are proposed. The EWL and EMAV are the modified version of wavelength (WL) and mean absolute value (MAV), which aims to enhance the prediction accuracy for the classification of hand movements. Initially, the proposed features are extracted from the EMG signals via discrete wavelet transform (DWT). The extracted features are then fed into the machine learning algorithm for classification process. Four popular machine learning algorithms include k-nearest neighbor (KNN), linear discriminate analysis (LDA), Naïve Bayes (NB) and support vector machine (SVM) are used in evaluation. To examine the effectiveness of EWL and EMAV, several conventional EMG features are used in performance comparison. In addition, the efficacy of EWL and EMAV when combine with other features are also investigated. Based on the results obtained, the combination of EWL and EMAV with other features can improve the classification performance. Thus, EWL and EMAV can be considered as valuable tools for rehabilitation and clinical application

A Multiple Instance Learning Approach to Electrophysiological Muscle Classification for Diagnosing Neuromuscular Disorders Using Quantitative EMG

Neuromuscular disorder is a broad term that refers to diseases that impair muscle functionality either by affecting any part of the nerve or muscle. Electrodiagnosis of most neuromuscular disorders is based on the electrophysiological classification of involved muscles which in turn, is performed by inferring the structure and function of the muscles by analyzing electromyographic (EMG) signals recorded during low to moderate levels of contraction. The functional unit of muscle contraction is called a motor unit (MU). The morphology and physiology of the MUs of an examined muscle are inferred by extracting motor unit potentials (MUPs) from the EMG signals detected from the muscle. As such, electrophysiological muscle classification is performed by first characterizing extracted MUPs and then aggregating these characterizations. The task of classifying muscles can be represented as an instance of a multiple instance learning (MIL) problem. In the MIL paradigm, a bag of instances shares a label and the instance labels are hidden, contrary to standard supervised learning, where each training instance is labeled. In MIL-based muscle classification, the instances are the MUPs extracted from the EMG signals of the analyzed muscle and the bag is the muscle. Detecting and counting the MUPs indicating a specific category of a neuromuscular disorder can result in accurately classifying the examined muscle. As such, three major issues usually arise: how to infer MUP labels without full supervision; how the cardinality relationships between MUP labels contribute to predict the muscle label; and how the muscle as a whole entity is classified. In this thesis, these three challenges are addressed. To this end, an MIL-based muscle classification system is proposed that has five major steps: 1) MUPs are represented using morphological, stability, and novel near fiber parameters as well as spectral features extracted from wavelet coefficients. This representation helps to analyze MUPs from a variety of aspects. 2) MUP feature selection using unsupervised similarity preserving Laplacian score which is independent of any learning algorithm. Hence, the features selected in this work can be used in other electrophysiological muscle classification systems. 3) MUP clustering using a novel clustering algorithm called Neighbourhood Distance Entropy Consistency (NDEC) which contributes to solve the traditional problem of finding representations of MUP normality and abnormality and provides a dynamic number of MUP characterization classes which will be used instead of the conventional three classes (i.e. normal, myopathic, and neurogenic). This clustering was performed to highlight the effects of disease on both fiber spatial distributions and fiber diameter distributions, which lead to a continuity of MUP characteristics. These clusters can potentially represent several concepts of MUP normality and abnormality. 4) Muscle representation by embedding its MUP cluster associations in a feature vector, and 5) Muscle classification using support vector machines or random forests. Quantitative results obtained by applying the proposed method to four electrophysiologically different groups of muscles including proximal arm, proximal leg, distal arm, and distal leg show the superior and stable performance of the proposed muscle classification system compared to previous works. Additionally, modelling electrophysiological muscle classification as an instance of the MIL can solve the traditional problem of characterizing MUPs without full supervision. The proposed clustering algorithm in this work, can be used as an effective technique in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots