dc.contributor.author |
Nakajima, Rio |
|
dc.contributor.author |
Rusydi, Muhammad Ilhamdi |
|
dc.contributor.author |
Ramadhani, Salisa Asyarina |
|
dc.contributor.author |
Muguro, Joseph |
|
dc.contributor.author |
Matsushita, Kojiro |
|
dc.contributor.author |
Sasaki, Minoru |
|
dc.date.accessioned |
2022-10-07T06:09:47Z |
|
dc.date.available |
2022-10-07T06:09:47Z |
|
dc.date.issued |
2022-09 |
|
dc.identifier.uri |
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/7025 |
|
dc.description.abstract |
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 camerabased 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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION |
en_US |
dc.title |
Image Presentation Method for Human Machine Interface Using Deep Learning Object Recognition and P300 Brain Wave |
en_US |
dc.type |
Article |
en_US |