Neural Network-Based Muscle Torque Estimation Using Mechanomyography During Electrically-Evoked Knee Extension and Standing in Spinal Cord Injury

Abstract

This study sought to design and deploy a torque monitoring system using an artificial neural network (ANN) with mechanomyography (MMG) for situations where muscle torque cannot be independently quantified. The MMG signals from the quadriceps were used to derive knee torque during prolonged functional electrical stimulation (FES)-assisted isometric knee extensions and during standing in spinal cord injured (SCI) individuals. Three individuals with motor-complete SCI performed FES-evoked isometric quadriceps contractions on a Biodex dynamometer at 30° knee angle and at a fixed stimulation current, until the torque had declined to a minimum required for ANN model development. Two ANN models were developed based on different inputs; Root mean square (RMS) MMG and RMS-Zero crossing (ZC) which were derived from MMG. The performance of the ANN was evaluated by comparing model predicted torque against the actual torque derived from the dynamometer. MMG data from 5 other individuals with SCI who performed FES-evoked standing to fatigue-failure were used to validate the RMS and RMS-ZC ANN models. RMS and RMS-ZC of the MMG obtained from the FES standing experiments were then provided as inputs to the developed ANN models to calculate the predicted torque during the FES-evoked standing. The average correlation between the knee extension-predicted torque and the actual torque outputs were 0.87 ± 0.11 for RMS and 0.84 ± 0.13 for RMS-ZC. The average accuracy was 79 ± 14% for RMS and 86 ± 11% for RMS-ZC. The two models revealed significant trends in torque decrease, both suggesting a critical point around 50% torque drop where there were significant changes observed in RMS and RMS-ZC patterns. Based on these findings, both RMS and RMS-ZC ANN models performed similarly well in predicting FES-evoked knee extension torques in this population. However, interference was observed in the RMS-ZC values at a time around knee buckling. The developed ANN models could be used to estimate muscle torque in real-time, thereby providing safer automated FES control of standing in persons with motor-complete SCI.

Keywords: functional electrical stimulation; mechanomyography; neural network; spinal cord injuries; torque estimation.

Figure 1

FES electrodes and MMG sensor placement on the quadriceps muscle.

Figure 2

Standing Experiment. (A) At the beginning of the experiment the legs were straight due to FES stimulation. (B) The knee approaching 30° flexion “knee-buckle”.

Figure 3

Normalized MMG RMS and Normalized MMG ZC against time used to be as training data for ANN development from Subject 4 Session 1, Left leg trial 1.

Figure 4

Normalized torque measurement from Biodex dynamometer and the predicted torque from two ANN models from Subject 4 Session 1, Left leg trial 1.

Figure 5

Normalized predicted torque for a standing protocol for Subject 5 Trial 1.

Figure 6

Biomechanics of Standing. Left: non- fatigued, quiet standing motion. Small knee extension moment. Right: fatigued, 30° knee angle bend. Large knee flexion moment due to gravity.

Figure 7

Graph of MMG RMS against Knee Bend Angle during FES Standing in SCI subjects (Mohd Rasid, 2017).