Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees

Abstract

A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.

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 ].…”

“…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 ]. However, their application in developing prediction models for CKD in type 1 diabetic mellitus patients is rare.…”

“…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

“…to addressing multiple challenging factors, several pieces of research are found on muscle force variation. 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.…”

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

“…Moreover, short-time Fourier transforms or wavelet transforms extract features both in time and frequency domains. However, in the literature, time-domain features are used more frequently than frequency-or time-frequency-domain features since these features do not require any transformations and hence large computing resources, such as processing power and memory [25,36]. Consequently, this research is carried out to determine a further contribution to the time-domain feature set.…”