The research described herein was undertaken to develop and test a novel tongue interface based on classification of tongue motions from the surface electromyography (EMG) signals of the suprahyoid muscles detected at the underside of the jaw. The EMG signals are measured via 22 active surface electrodes mounted onto a special flexible boomerang-shaped base. Because of the sensor's shape and flexibility, it can adapt to the underjaw skin contour. Tongue motion classification was achieved using a support vector machine (SVM) algorithm for pattern recognition where the root mean square (RMS) features and cepstrum coefficients (CC) features of the EMG signals were analyzed. The effectiveness of the approach was verified with a test for the classification of six tongue motions conducted with a group of five healthy adult volunteer subjects who had normal motor tongue functions. Results showed that the system classified all six tongue motions with high accuracy of 95.1 ± 1.9 %. The proposed method for control of assistive devices was evaluated using a test in which a computer simulation model of an electric wheelchair was controlled using six tongue motions. This interface system, which weighs only 13.6 g and which has a simple appearance, requires no installation of any sensor into the mouth cavity. Therefore, it does not hinder user activities such as swallowing, chewing, or talking. The number of tongue motions is sufficient for the control of most assistive devices.

The ability to ne-tune the movement of swallowing-related organs and change the swallowing pattern to t the volume of a bolus, texture and the physical properties of the food to be swallowed is referred to as the swallowing reserve. In other words, it is the response capability of food swallowing to avoid choking and aspiration. Herein, we focus on the coordination of the suprahyoid and infrahyoid muscles activities, which are closely related to swallowing movement, as a rst step to develop a method to evaluate swallowing reserve, which declines due to neuromuscular disease, muscle weakness caused by aging, to mention a few. First, using two 22-channel electrodes, we measured the surface electromyography (sEMG) signals of suprahyoid and infrahyoid muscles during the following four swallowing conditions: combining two bolus volumes (3 and 15 mL water) and two techniques (normal and effortful swallow). Then, we veri ed whether the difference in swallowing patterns based on swallowing conditions can be classi ed from sEMG signals using three machine learning methods; namely, the real-time classi cation, comprehensive classi cation, and image recognition method. In the real-time classi cation method, the mean classi cation accuracy (MCA) for the four swallowing conditions was as low as 81.5%, indicating that the difference between swallowing conditions performed in a period of approximately 1 s cannot be classi ed suf ciently by this method. In the comprehensive classi cation method that applies a majority decision to all the classi cation results from the start to the end of swallowing, which can be obtained every 16 ms, MCA was 95.1%. Furthermore, in the image recognition method, the change of a series of sEMG signals in the swallowing movement was converted into swallowing pattern image, and the images were classi ed using a combination of deep convolutional neural networks and support vector machine (SVM). Compared with the comprehensive classi cation method, the number of training samples for the image recognition method was only 1/26, but the MCA reached 95.7%. This method, which can noninvasively evaluate swallowing patterns that change slightly based on swallowing conditions, could be applied to early detection of reduced swallowing function or a state of frailty (dysphagia potential) in aged individuals.

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In this paper, we introduce a new tongue-training system that can be used for improvement of the tongue's range of motion and muscle strength after dysphagia. The training process is organized in game-like manner. Initially, we analyzed surface electromyography (EMG) signals of the suprahyoid muscles of five subjects during tongue-training motions. This test revealed that four types tongue training motions and a swallowing motion could be classified with 93.5% accuracy. Recognized EMG signals during tongue motions were designed to allow control of a mouse cursor via intentional tongue motions. Results demonstrated that simple PC games could be played by tongue motions, achieving in this way efficient, enjoyable and pleasant tongue training. Using the proposed method, dysphagia patients can choose games that suit their preferences and/or state of mind. It is expected that the proposed system will be an efficient tool for long-term tongue motor training and maintaining patients' motivation.