مقاله انگلیسی رایگان در مورد رونوشت حرکات دست در زمان واقعی – الزویر 2019

1. Introduction

2. Methodology

3. Experimental Outcome

4. Conclusion

5. Acknowledgement

References

In this paper, a real time application to replicate nine arm movements is proposed. The two important joints that are controlled are wrist and elbow. Electromyogram signals are recorded for four wrist positions and five elbow positions. These signals are enhanced and features pertaining to muscle movements are extracted. Dimension of these feature sets is reduced to obtain the optimal set of features. These feature sets are given as input to the classifier. Performance evaluation of Support Vector Machine (SVM), K-Nearest Neighbors, Random Forest and Relevant Vector Machine (RVM) classifiers, in recognizing different wrist and elbow positions, is discussed. As per the results, the best overall accuracy of 93.3% was obtained from SVM with radial basis function (RBF) kernel, in classifying both the wrist and elbow positions. Although, RVM as a classifier yielded the same accuracy in recognizing wrist positions, it resulted in the lowest accuracy of 88.67% in recognizing elbow positions. Therefore, SVM-RBF fared better in identifying the arm movements. Furthermore, these arm movements are used to control the actuators.

Introduction

The muscle attached to the bone is called the skeletal muscle. The contraction of this muscle is responsible for the movement of the limbs. This contraction leads to the generation of an electrical signal called electromyography (EMG) signal. Multiple strands called myofibrils present in the muscle are chemically activated by neurons which leads to the generation of charge whose motion in turn produces electromagnetic field. In a motor unit all the action potentials generated by myofibrils are combined together and hence this is termed as motor unit action potential (MUAP). Surface EMG electrodes collect several such MUAPs for each skeletal muscle. The physiology of the signal depends on the arm movement that led to its generation. This unique identity of each signal can be used to replicate specific arm movements in an orthotic arm using actuators.