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sEMG-angle estimation using feature engineering techniques for least square support vector machine.

Yongsheng Gao, Yang Luo, Jie Zhao

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|May 3, 2019

Summary

This summary is machine-generated.

This study introduces feature engineering techniques to enhance surface electromyography (sEMG) motion recognition for controlling devices. The improved least square support vector machine (LSSVM) achieved faster, more accurate wrist palmar angle estimation.

Keywords:

Least square support vector machine angle estimation electromyograph feature engineering

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Area of Science:

Biomedical Engineering
Machine Learning
Signal Processing

Background:

Accurate and fast human motion recognition from surface electromyography (sEMG) is crucial for controlling sEMG-driven devices.
Existing algorithms often face challenges in balancing speed and accuracy for real-time applications.

Purpose of the Study:

To propose and evaluate two novel feature engineering (FE) techniques: feature-vector resampling and time-lag.
To enhance the performance of the least square support vector machine (LSSVM) for wrist palmar angle estimation using sEMG data.
To improve both the accuracy and processing speed of motion recognition algorithms.

Main Methods:

Implementation of feature-vector resampling and time-lag techniques as feature engineering methods.
Application of these FE techniques to the least square support vector machine (LSSVM) for wrist palmar angle estimation from sEMG.
Comparison of the proposed LSSVM with FE against standard LSSVM, radial basis function (RBF) neural network, and RBF with FE.

Main Results:

LSSVM with FE achieved a root mean square error of 9.50 ± 2.32 degrees and a correlation coefficient of 0.971 ± 0.018.
The average training and execution times for LSSVM with FE were significantly reduced to 0.016 s and 0.053 s, respectively, for 12600 sEMG points.
The time-lag technique notably improved estimation accuracy for both RBF and LSSVM, while feature-vector resampling enhanced training and execution speeds.

Conclusions:

The proposed feature engineering techniques, particularly the combination with LSSVM, offer a superior approach for sEMG-based motion control.
LSSVM integrated with feature-vector resampling and time-lag techniques demonstrates the best performance in terms of speed and accuracy among the evaluated methods.
These advancements hold significant potential for improving the practical implementation and user experience of sEMG-driven devices.