A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

Abstract

Variation in the electromyogram pattern recognition (EMG-PR) performance with the muscle contraction force is a key limitation of the available prosthetic hand. To alleviate this problem, we propose a scheme to realize electromyogram signal normalization across channels before feature extraction. The proposed signal normalization scheme is validated over a dataset of nine transradial amputees that includes three force levels with six hand gestures. Moreover, we employ three classifiers, namely, linear discriminant analysis (LDA), support vector machine (SVM) and k-nearest neighbour (KNN), to evaluate the EMG-PR performance. In addition to the signal normalization scheme, we perform nonlinear transformation of the features by using the logarithm function. Both schemes facilitate merging of the muscle activation patterns of different force levels. The experimental results indicate that the force invariant EMG-PR performance (F1 score of at least 3.24% to 4.34%) of the proposed schemes is significantly enhanced compared to that obtained in recent studies. Therefore, we recommend using these features along with the proposed signal normalization scheme and nonlinear transformation of the features to improve the force invariant EMG-PR performance. The proposed feature extraction method achieves the highest F1 score of 91.28%, 91.39% and 90.56% when using the LDA, SVM and KNN classifiers, respectively.INDEX TERMS EMG Pattern recognition, Force invariant features, Muscle activation pattern, Signal normalization.

Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients

“…Health professionals are more enthusiastic about using their advantages of flexibility and self-learning capacity as an aiding system for reliable performance. Intelligent systems based on ML algorithms have been intensively investigated for various biomedical systems, focusing on disease detection and risk reduction [ 16 , 17 , 18 , 49 , 50 ]. Like other severe diseases, CKD has piqued the interest of researchers in creating ML-based diagnosis systems for CKD [ 3 , 13 , 18 , 20 , 21 , 22 ].…”

“…The use of machine learning (ML) algorithms in addressing various disease classification problems has recently expanded due to remarkable advancements in related technologies [ 16 , 17 , 18 ]. Although there are some examples of using ML tools in kidney disease prediction [ 13 , 19 , 20 , 21 , 22 ], their use in developing CKD prediction models for type 1 diabetes mellitus patients is scarce.…”

Forearm Orientation and Muscle Force Invariant Feature Selection Method for Myoelectric Pattern Recognition

“…Recently, Islam et al [15] proposed a non-linear scaling-based feature extraction method to resolve the muscle force variation of transradial amputees employing 84-dimensional feature space. Again, Islam et al [14] extended their previous work and introduced a novel signal normalization scheme to overlap the extracted features of different muscle force levels. In addition to these, Al-Timemy et al [24] proposed a feature extraction method based on orientation between a set of spectral moments descriptors.…”

“…Further, features are extracted, and the intended movements are predicted by a classifier [2], [11]. However, the available prosthetic arms are not commercially successful due to several limiting factors including electrode position shift [12], [13], variation of muscle contraction force [14]- [16], limb position [17], [18], forearm orientation [19], [20], mobility of subject [21], and multiday variation [22], [23]. These factors make significant alterations in EMG signal properties, i.e., time-domain and frequency-domain properties, and make changes in extracted features [14], [19], [23].…”

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

“…In terms of EMG pattern recognition, many classifiers have been used in recent studies. ese are convolutional neural networks (CNNs) [49,50], linear discriminant analysis (LDA) [51], artificial neural networks (ANNs) [52], fuzzy methods [53], support vector machines (SVMs) [54,55], and k-nearest neighbours (KNNs) [56]. Among these methods, the CNN provides very strong EMG recognition performance but is impossible to implement in cheap hardware for real-time operation [57].…”