Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6944397 | EMBC |
Keywords | Field | DocType |
mouse controllers (computers),biomechanics,pc game preferences,surface electromyography,medical control systems,medical disorders,mechanical strength,swallowing motion classification,tongue-training motion types,long-term tongue motor training,brain-computer interfaces,tongue motor training support system,training,data analysis,motion classification accuracy,medical signal processing,pc game selection,intentional tongue motions,biomedical equipment,psychology,patient rehabilitation,game-like tongue training organization,patient motivation,signal classification,dysphagia,mouse cursor control,tongue muscle strength,consumer behaviour,electromyography,tongue motion range,emg signal recognition,tongue-training motion classification,biological organs,mind state,emg signal analysis,suprahyoid muscles,computer games | Computer vision,Computer science,Support system,Speech recognition,Human–computer interaction,Artificial intelligence,Tongue | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 1 |
PageRank | References | Authors |
0.35 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Makoto Sasaki | 1 | 12 | 3.87 |
| Kohei Onishi | 2 | 1 | 0.35 |
| Atsushi Nakayama | 3 | 10 | 2.27 |
| Katsuhiro Kamata | 4 | 1 | 0.35 |
| Dimitar H. Stefanov | 5 | 34 | 4.61 |
| Masaki Yamaguchi | 6 | 1 | 0.69 |