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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Peculiarities of Wave Surface of a SemiconductorDielectric Metamaterial

DOI:10.1109/CAOL46282.2019.9019447 出版年份:2019 更新时间:2025-09-16 10:30:52
摘要: We present GyroPen, a method to reconstruct the motion path for pen-like interaction from standard built-in sensors in modern smartphones. The key idea is to reconstruct a representation of the trajectory of the phone’s corner that is touching a writing or drawing surface from the measurements obtained from the phone’s gyroscopes and accelerometers. We propose to directly use the angular trajectory for this reconstruction, which removes the necessity for accurate absolute 3-D position estimation, a task that can be difficult using low-cost accelerometers. We connect GyroPen to a handwriting recognition system and perform two proof-of-concept experiments to demonstrate that the reconstruction accuracy of GyroPen is accurate enough to be a promising approach to text entry. In a first experiment, the average novice participant (n = 10) was able to write the first word only 37 s after the starting to use GyroPen for the first time. In a second experiment, experienced users (n = 2) were able to write at the speed of 3–4 s for one English word and with a character error rate of 18%.
作者: Thomas Deselaers,Daniel Keysers,Jan Hosang,Henry A. Rowley
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Investigating the use of gyroscopes and accelerometers in smartphones for reconstructing the motion path for pen-like interaction to enable text entry.

GyroPen is a promising approach for handwriting with smartphones, enabling text entry without the need for a stylus or touchscreen. The method reconstructs writing paths accurately enough for recognition by an off-the-shelf handwriting recognition engine. Users can learn to use GyroPen quickly, and experienced users can achieve acceptable accuracy and speed.

The method is sensitive to noise in sensor data, and the sensors built into current phones may be too noisy for accurate estimation of a writing path. The slant correction is designed with handwriting in mind and may fail for other types of drawings.

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