Practical method for predicting intended gait speed via soleus surface EMG signals

Abstract

The lack of patient effort during robot-assisted gait training (RAGT) is thought to be the main factor behind unsatisfactory rehabilitative efficacy among hemiparetic stroke patients. A key milestone to implement patient-driven RAGT is to predict gait intent prior to actual joint movement. Here, the authors propose a method of predicting step speed intent via surface electromyogram (EMG) signals from the soleus. Six lower-limb muscles were initially evaluated on a treadmill, and the results suggest that the soleus EMG signals correlate well with step speed. The authors further propose a simple linear regression model which predicts subsequent step speed via current soleus EMG signals with over-ground gait sessions, R 2 of ~0.6. The proposed experimental results and simple prediction model should be applicable for RAGT without significant modifications.

 

Practical method for predicting intended gait speed via soleus surface EMG signals