Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques. Issue 4 (April 2020)

• Record Type: Journal Article
• Authors: Batayneh, Wafa; Abdulhay, Enas; Alothman, Mohammad
• Is Part Of: Heliyon. Volume 6:Issue 4(2020)
• Publication Date: 2020-04
• Subjects: Biomedical engineering -- Computer science -- Mechanical engineering -- Applied computing in medical science -- Artificial intelligence -- Computational mechanics -- Process modeling -- Biomedical devices -- Surface electromyography -- Data visualization -- Correlation -- Mutual information -- Artificial neural networks -- Kinematics estimation; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2020.e03669
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: The inputs to the outputs of nonlinear systems can be modeled using machine and deep learning approaches, among which artificial neural networks (ANNs) are a promising option. However, noisy signals affect ANN modeling negatively; hence, it is important to investigate these signals prior to the modeling. Herein, two customized and simple approaches, visual inspection and absolute correlation, are proposed to examine the relationship between the inputs and outputs of a nonlinear system. The system under consideration uses biosignals from surface electromyography as inputs and human finger joint angles as outputs, acquired from eight intact participants performing movements and grasping tasks in dynamic conditions. Furthermore, the results of these approaches are tested using the standard mutual information measure. Hence, the system dimensionality is reduced, and the ANN learning (convergence) is accelerated, where the most informative inputs are selected for the next phase. Subsequently, four ANN types, i.e., feedforward, cascade-forward, radial basis function, and generalized regression ANNs, are used to perform the modeling. Finally, the performance of the ANNs is compared with findings from the signal analysis. Results indicate a high level of consistency among all the aforementioned signal pre-analysis techniques from one side, and they also indicate that these techniques match the ANN performances from the other side. As an example, for a certain movement set, the ANN models resulted in the rotation estimation accuracy of the joints in the following descending order: carpometacarpal, metacarpophalangeal, proximal interphalangeal, and distal interphalangeal. This information has been indicated in the signal pre-analysis step. Therefore, this step is crucial in input–output variable selections prior to machine-/deep-learning-based modeling approaches. Abstract : Biomedical engineering; Computer science; Mechanical engineering; Applied computing in medical science; Artificial intelligence; Computational mechanics; Process modeling; Biomedical devices; Surface electromyography; Data visualization; Correlation; Mutual information; Artificial neural networks; Kinematics estimation.