Recognition of Unseen Combined Motions via Convex Combination-based EMG Pattern Synthesis for Myoelectric Control

TL;DR

This paper introduces a convex combination-based EMG pattern synthesis method that enables recognition of unseen combined motions with reduced training data, improving classification accuracy in myoelectric control.

Contribution

It presents a novel synthetic data generation approach for recognizing complex combined motions without exhaustive data collection.

Findings

Improved classification accuracy for unseen combined motions by approximately 17%.

Efficient recognition of complex motions with limited training data.

Validated on upper limb motion classification with eight subjects.

Abstract

Electromyogram (EMG) signals recorded from the skin surface enable intuitive control of assistive devices such as prosthetic limbs. However, in EMG-based motion recognition, collecting comprehensive training data for all target motions remains challenging, particularly for complex combined motions. This paper proposes a method to efficiently recognize combined motions using synthetic EMG data generated through convex combinations of basic motion patterns. Instead of measuring all possible combined motions, the proposed method utilizes measured basic motion data along with synthetically combined motion data for training. This approach expands the range of recognizable combined motions while minimizing the required training data collection. We evaluated the effectiveness of the proposed method through an upper limb motion classification experiment with eight subjects. The experimental results demonstrated that the proposed method improved the classification accuracy for unseen combined motions by approximately 17%.