Toward reducing the effect of delay on motion transmission to a remote place, methods of forecasting human motion with subsecond preceding time have been studied. In this paper, we verified whether the prediction of single joint motion could be improved by using surface electromyography (EMG) signals. We used a recurrent neural network to predict the flexion and extension movement of a thigh, and compared the results between the prediction using only the angle and that using both the angle and EMG signals of two muscles. As a result, in the prediction of motion of about 0.5 Hz, the accuracy and delay of the prediction tended to be improved by using the EMG signals (e.g., in 0.3 s ahead prediction, the mean of the rootmean- square error between participants and trials is improved by 0.7°, and that of the prediction delay is reduced by 0.045 s). Such motion forecasting using EMG signals may be useful for improving the operability and stability of medical robots in telerehabilitation and telesurgery.