A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study.

Qiang Zhang,Ziyue Sun,Kang Kim,Nitin Sharma,Ashwin Iyer

doi:10.1109/tnsre.2021.3106900

Abstract

For decades, surface electromyography (sEMG) has been a popular non-invasive bio-sensing technology for predicting human joint motion. However, cross-talk, interference from adjacent muscles, and its inability to measure deeply located muscles limit its performance in predicting joint motion. Recently, ultrasound (US) imaging has been proposed as an alternative non-invasive technology to predict joint movement due to its high signal-to-noise ratio, direct visualization of targeted tissue, and ability to access deep-seated muscles. This paper proposes a dual-modal approach that combines US imaging and sEMG for predicting volitional dynamic ankle dorsiflexion movement. Three feature sets: 1) a uni-modal set with four sEMG features, 2) a uni-modal set with four US imaging features, and 3) a dual-modal set with four dominant sEMG and US imaging features, together with measured ankle dorsiflexion angles, were used to train multiple machine learning regression models. The experimental results from a seated posture and five walking trials at different speeds, ranging from 0.50 m/s to 1.50 m/s, showed that the dual-modal set significantly reduced the prediction root mean square errors (RMSEs). Compared to the uni-modal sEMG feature set, the dual-modal set reduced RMSEs by up to 47.84% for the seated posture and up to 77.72% for the walking trials. Similarly, when compared to the US imaging feature set, the dual-modal set reduced RMSEs by up to 53.95% for the seated posture and up to 58.39% for the walking trials. The findings show that potentially the dual-modal sensing approach can be used as a superior sensing modality to predict human intent of a continuous motion and implemented for volitional control of clinical rehabilitative and assistive devices.

Highlights

Weakened function or dysfunction of the tibialis anterior (TA) muscle, which is the primary contributing muscle for ankle dorsiflexion, severely impedes normal walking and balance functions
The present study investigated the feasibility of a dualmodal approach by fusing TA muscle neuromuscular features from both Surface electromyography (sEMG) signal and US B-mode imaging for volitional ankle dorsiflexion movement prediction in both seated posture and walking at multiple speeds
The ankle joint motion prediction performance of the proposed dualmodal approach was evaluated through multiple regression models

Summary

Introduction

Weakened function or dysfunction of the tibialis anterior (TA) muscle, which is the primary contributing muscle for ankle dorsiflexion, severely impedes normal walking and balance functions. Humanin-the-loop control strategies encourage active involvement by providing assistance only when needed [6], [7]. These strategies, require accurate measurements of residual volitional intent to generate stable and effective assistance. Force or torque sensors installed on an exoskeletal frame can be used to measure human limb motion intent. Feedback from these sensors is reactive, i.e., a limb movement must precede for the sensor to register the applied force or torque. In severely weakened muscles, where it is often difficult to produce limb movements, predicting the user’s volitional intent with these sensors would be challenging. Instead of a reactionary method, a predictive sensing methodology would increase the assistive device’s synchronization and stability

Methods

Results

Conclusion