Distinguishing the activity of flexor digitorum brevis and soleus across standing postures with deep learning models

Abstract

Background

Electromyographic (EMG) recordings indicate that both the flexor digitorum brevis and soleus muscles contribute significantly to the control of standing balance, However, less is known about the adjustments in EMG activity of these two muscles across different postures.

Research question

The purpose of our study was to use deep-learning models to distinguish between the EMG activity of the flexor digitorum brevis and soleus muscles across four standing postures.

Methods

Deep convolutional neural networks were employed to classify standing postures based on the temporal and spatial features embedded in high-density surface EMG signals. The EMG recordings were obtained with grid electrodes placed over the flexor digitorum brevis and soleus muscles of healthy young men during four standing tasks: bipedal, tandem, one-leg, and tip-toe.

Results and significance

Two-way repeated-measures analysis of variance demonstrated that the model achieved significantly greater classification accuracy, particularly during tandem stance, using EMG data from flexor digitorum brevis compared with soleus muscle. Average classification accuracy was 84.6 % for flexor digitorum brevis and 79.1 % for soleus. The classification accuracy of both muscles varied across the four postures. There were significant differences in classification accuracy for flexor digitorum brevis between bipedal and tandem stances compared with one-leg and tip-toe stances. In contrast, the EMG data for soleus were only significantly different between bipedal stance and one-leg stance. These findings indicate that flexor digitorum brevis exhibited more distinct adjustments than soleus in the temporo-spatial features of EMG activity across the four postures.