Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals

Abstract

Objective: To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements. Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-extension movements. After that, a myoelectric pattern recognition algorithm based on a multilayer perceptron artificial neural network (ANN) was implemented. Three different groups were used: Time domain characteristics, autoregressive (AR) model parameters, and representation of time frequency using Wavelet transform (WT). Results and Discussion: The experimental results of 10 healthy subjects indicate that the coefficients of the AR models offer the best parameters for classification, with a differentiation rate of 78 % for the five angular positions studied. The combination of frequency and time frequency resulted in a differentiation rate that reached 82 %. Conclusions: An algorithm based on pattern recognition of EMG signals was used to carry out a comparative study of groups of features that allow for the differentiation of the angular position of the wrist in terms of flexion-extension movements. The method has the potential for application in the field of rehabilitation engineering to detect the user’s movement intent.

Predicting Wrist Joint Angles from the Kinematics of the Arm: Application to the Control of Upper Limb Prostheses

“…In ref. [ 10 ], classifications of wrist flexion positions were achieved with accuracy using neural networks and accuracy using SVM. In ref.…”

“…Some works focus on signal classification techniques to assign them to a discrete movement or a specific wrist angle. For example, Fajardo-Perdomo et al [ 10 ] used techniques such as support vector machine (SVM) and multilayer perceptron (MLP) neural networks to classify five different static positions between the maximum flexion and extension of the wrist based on EMG signals recorded in the forearm. Yang et al [ 7 ] used dimensional reduction techniques, such as subclass discriminant analysis (SDA) and principal component analysis (PCA), to classify different hand postures and wrist pronation/supination based on ultrasound signals.…”