Real-time estimation of tongue movement based on suprahyoid muscle activity

In this study, we introduce a real-time method for tongue movement estimation based on the analysis of the surface electromyography (EMG) signals from the suprahyoid muscles, which usual function is to open the mouth and to control the position of the hyoid, the base of the tongue. Nine surface electrodes were affixed to the underside of the jaw and their signals were processed via multi-channel EMG system. The features of the EMG signals were extracted by using a root mean square (RMS) method. The dimension of the variables was reduced additionally from 108 to 10 by applying the Principal Component Analysis (PCA). The feature quantities of the reduced dimension set were associated with the tongue movements by using an artificial neural network. Results showed that the proposed method allows precise estimation of the tongue movements. For the test data set, the identification rate was greater than 97 % and the response time was less than 0.7 s. The proposed method could be implemented to facilitate novel approaches for alternative communication and control of assistive technology for supporting the independent living of people with severe quadriplegia.