Abstract
In this paper, we propose a novel method to estimate the elbow motion, through the features extracted from electromyography (EMG) signals. The features values are normalized and then compared to identify potential relationships between the EMG signal and the kinematic information as angle and angular velocity. We propose and implement a method to select the best set of features, maximizing the distance between the features that correspond to flexion and extension movements. Finally, we test the selected features as inputs to a non-linear support vector machine in the presence of non-idealistic conditions, obtaining an accuracy of 99.79% in the motion estimation results.
Highlights
The Electromyography (EMG) signal is a measure of the electrical activity produced by muscles during the movement
Regarding the approach to the classification of EMG signals, Oskoei and Huosheng [3] performed the classification of five movements of the hand, they reached an accuracy of 94%
Kinematic analysis and features selection we present the kinematic analysis of the upper limb, and we introduce the relation between the EMG signals and kinematic parameters. afterwards, we perform a feature extraction and we propose a new strategy to select the best couple of features
Summary
The Electromyography (EMG) signal is a measure of the electrical activity produced by muscles during the movement. Afterwards, we perform a correlation between the kinematic data and the EMG signals This relationship allows to propose a criterion for selecting the couple of feature that best separates the flexion and extension movements. Relationship between EMG and Kinematic The EMG signal is a measure of the electrical activity produced by muscle contractions during motion From these signals, it is possible to extract scalar values that could contain relevant information of the movement, and so on, establish a relationship between EMG and motion. These particularities make possible to model and describes a long range movement It shows that there is a relation between the features extracted from the EMG signals and the motion. −0.48 −0.58 1.17 −1.24 −0.77 −0.38 0.22 −1.12 0.53 0.31 −0.39 −0.52 1.64 −0.30 0.01 −0.54 −0.35 −1.25 0.45 −0.32 f [zS[SDDU]U] f z[S[SDDU]U]
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