Abstract
We used passive, high-functional radiofrequency identification (RFID) tags with magnetic sensors to detect front of face touch positions without the requirement for a battery. We implemented a prototype system consisting of a goggle-type device equipped with passive high-functional RFID tags with magnetic sensor, a ring with permanent magnets, and touch detection software for machine-learning. We evaluated the classification accuracy of the six front of face touch positions and a ‘no-touch’ case. The discrimination rate when using the learning data was 83% but the real-time discrimination was only 65%. In future, we will aim to improve the accuracy, and define more touch points and gesture inputs.
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Takayama, Y., Ichikawa, Y., Kitagawa, T., Shengmei, S., Shizuki, B., Takahashi, S. (2020). Touch Position Detection on the Front of Face Using Passive High-Functional RFID Tag with Magnetic Sensor. In: Kurosu, M. (eds) Human-Computer Interaction. Multimodal and Natural Interaction. HCII 2020. Lecture Notes in Computer Science(), vol 12182. Springer, Cham. https://doi.org/10.1007/978-3-030-49062-1_35
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DOI: https://doi.org/10.1007/978-3-030-49062-1_35
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