Abstract
We previously developed a gesture interface for people with motor dysfunction using an RGB-D camera. We collected 226 gesture data from 58 individuals with motor dysfunction and classified the data. We then developed multiple recognition modules based on the data. The interface has nine modules for recognizing various types of gestures. For this study, we had a person with a disability use this interface in combination with an input device he had been using trackball for a transcription task. We set two gesture-input switches from the movement of two sites on his body that were easy for him to move. The user performed character input in the on-screen keyboard by using the trackball and separately operated the sound player using our gesture interface. He continued this activity using this combination daily use for half a year. He was able to reduce the input time by half. We are now supplying AAGI for Japanese people with motor dysfunction freely. We will supply AAGI for foreign users through our home page in next year.
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Acknowledgments
This study was supported by the TATEISHI SCIENCE AND TECHNOLOGY FOUNDATION 2020 S and AMED 20dk0310095h0102 in Japan.
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Yoda, I., Nakayama, T., Itoh, K., Nishida, D., Mizuno, K. (2020). Application of Gesture Interface to Transcription for People with Motor Dysfunction. In: Miesenberger, K., Manduchi, R., Covarrubias Rodriguez, M., Peňáz, P. (eds) Computers Helping People with Special Needs. ICCHP 2020. Lecture Notes in Computer Science(), vol 12377. Springer, Cham. https://doi.org/10.1007/978-3-030-58805-2_40
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