Muscle synergies enable accurate joint moment prediction using few electromyography sensors

Abstract

There is an increasing demand for accurate prediction of joint moments using wearable sensors for robotic exoskeletons to achieve precise control and for rehabilitation care to remotely monitor users’ condition. In this study, we used electromyography (EMG) signals to first identify muscle synergies, then used them to train of a long short-term memory network to predict knee joint moments during walking. Kinematics, ground reaction forces and EMG from 10 muscles on the right limb were collected from 6 able-bodied subjects during normal gait. Between 4 and 6 muscle synergies were extracted from the EMG signals, generating two outputs - the muscle synergies weight matrix and the time-dependent muscle synergies action signals. The muscle synergies action signals and measured knee joint moments from inverse dynamics were then used as inputs to train the joint moment prediction model using a long short-term memory network. For testing, between 4 and 7 EMG signals were used to estimate the muscle synergies action signals with the extracted muscle synergies weights matrix. The estimated muscle synergies action signals were then used to predict knee joint moments. Knee joint moments were also predicted directly from all 10 EMGs, then from 4-7 EMG signals using another long short-term memory network. Prediction accuracy from the synergies-trained network vs. the EMG-trained network were compared, using the same number of EMG signals in each. Prediction error with respect to moments measured via inverse dynamics was computed for both networks. Knee moments predicted with as few as 4 EMGs was at least as accurate as moments predicted from all 10 EMGs when muscle synergies were exploited. Predicted knee moments from muscle synergies achieved an average of 4.63% root mean square error from 4 EMG signals, which was lower than error when predicted directly from 4 EMG signals (5.63%).