EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee

Abstract

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm's prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.

Keywords: EMG; joint angle; machine learning; prediction.

Figure 1

Delsys Trigno sensor and retroreflective motion capture marker placement.

Figure 2

Example of how four algorithms (algorithm 2–50/100/150/200 ms) were trained for one side (right or left) for one subject with two randomly selected training trials (Trial 1 and Trial 4 in this example) out of ten training trials total. This was conducted for both right and left legs for all ten subjects.

Figure 3

Regression model of logarithmically transformed error for prediction times.

Figure 4

Regression model of error for prediction times.

Figure 5

Number of training trials effects on average degrees of RMSE in left knee flexion prediction.

Figure 6

Number of training trials effects on average degrees of RMSE in right knee flexion prediction.

Figure 7

Number of training trials effects on average standard deviation of degrees of RMSE in left knee flexion prediction.

Figure 8

Number of training trials effects on average standard deviation of degrees of RMSE in right knee flexion prediction.

Figure 9

Prediction times effects on average degrees of RMSE in knee flexion prediction for one training trial and for ten training trials. “+” symbol represents outlier data points.