SEMG-based prediction of masticatory kinematics in rhythmic clenching movements

• EMG signals of two masseter and temporalis muscles are used to predict clenching movements.
• GA is employed to find optimal number of neurons in the hidden layer and total duration of delays.
• Validity of the proposed models is experimentally demonstrated.
• The performance of AR-TDANN is better than that of TDANN.
• The TDANN would be sufficiently efficient for controlling masticatory robots.

This paper investigated the ability of a hybrid time-delayed artificial neural network (TDANN)/autoregressive TDANN (AR-TDANN) to predict clenching movements during mastication from surface electromyography (SEMG) signals. Actual jaw motions and SEMG signals from the masticatory muscles were recorded and used as output and input, respectively. Three separate TDANNs/AR-TDANNs were used to predict displacement (in terms of position/orientation), velocity, and acceleration. The optimal number of neurons in the hidden layer and total duration of delays were obtained for each TDANN/AR-TDANN and each subject through a genetic algorithm (GA). The kinematic modeling of a human-like masticatory robot, based on a 6-universal-prismatic-spherical parallel robot, is described. The structure and motion variables of the robot were determined. The closed-form solution of the inverse kinematic problem (IKP) of the robot was found by vector analysis. Thereafter, the framework for an EMG-based human mastication robot interface is explained. Predictions by AR-TDANN were superior to those by TDANN. SEMG signals from mastication muscles contained important information about the mandibular kinematic parameters. This information can be employed to develop control systems for rehabilitation robots. Thus, by predicting the subject's movement and solving the IKP, we provide applicable tools for EMG-based masticatory robot control.