EMG-based Force Estimation with a CNN Deep Model during Biceps Curl Exercise

This paper attempts to estimate muscle contraction force from surface electromyography (EMG) signals during biceps curl movements. A data-driven EMG-force estimation model has been developed in this study, based on deep convolutional neural networks (CNNs). Information of EMG signals from both time and frequency domains has been utilized as input data to two CNN branches of the estimation model. Raw time series of EMG signals are directly input into one CNN branch in order to extract time domain characteristics, while frequency information is fed into the other CNN branch. Then these two branches are summarized at a concatenation layer, which is followed by three full-connected layers to estimate force levels. End point force at hand, considering the dumbbell curl exercises, is measured in the experiments with a testing apparatus using a single point load cell. This force data is used as training data in order to train the deep CNN EMG-force model. Eight subjects participated in the data acquisition experiments. For validation, different structures of the CNN model are examined with an estimation index of coefficient of determination. Then cross-validation with an EMG-force database of eight subjects has been conducted to investigate the estimation performance of the proposed method.