Image Presentation Method for Human Machine Interface Using Deep Learning Object Recognition and P300 Brain Wave
| Autor: | Rio Nakajima, Muhammad Ilhamdi Rusydi, Salisa Asyarina Ramadhani, Joseph Muguro, Kojiro Matsushita, Minoru Sasaki |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | JOIV: International Journal on Informatics Visualization, Vol 6, Iss 3, Pp 736-742 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2549-9610 2549-9904 |
| DOI: | 10.30630/joiv.6.3.949 |
| Popis: | Welfare robots, as a category of robotics, seeks to improve the quality of life of the elderly and patients by availing a control mechanism to enable the participants to be self-dependent. This is achieved by using man-machine interfaces that manipulate certain external processes like feeding or communicating. This research aims to realize a man-machine interface using brainwave combined with object recognition applicable to patients with locked-in syndrome. The system utilizes a camera with pretrained object-detection system that recognizes the environment and displays the contents in an interface to solicit a choice using P300 signals. Being a camera-based system, field of view and luminance level were identified as possible influences. We designed six experiments by adapting the arrangement of stimuli (triangular or horizontal) and brightness/colour levels. The results showed that the horizontal arrangement had better accuracy than the triangular method. Further, colour was identified as a key parameter for the successful discrimination of target stimuli. From the paper, the precision of discrimination can be improved by adopting a harmonized arrangement and selecting the appropriate saturation/brightness of the interface. |
| Databáze: | Directory of Open Access Journals |
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