Abstract
In recent years, lack of effective communication method is one of the important issues that the people with physical disabilities, such as ALS (amyotrophic lateral sclerosis) face in social life. In this research we constructed an eye-gaze input system based on an image processing method using a PC and a web camera that are inexpensive and easy to install under natural light environment without the risk of illness due to the use of infrared radiation. In our proposed system, convolutional neural networks (CNN) is applied to improve the accuracy for practical use. The CNN in this study estimates the gaze position on the monitor screen from the image acquired from the web camera, and aims to obtain higher accuracy than the conventional system by learning for specific individuals.
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Acknowledgements
This work was supported by JSPS KAKENHI Grant-in-Aid for Scientific Research (B): Grant Number JP19H04228
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Kubo, K., Shibata, S., Karita, T., Yamamoto, T., Mu, S. (2022). Eye-Interface System Using Convolutional Neural Networks for People with Physical Disabilities. In: Mu, S., Yujie, L., Lu, H. (eds) 4th EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-70451-3_7
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DOI: https://doi.org/10.1007/978-3-030-70451-3_7
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