磁传感器:基于磁传感器的字符输入系统

Yuyang Ke, Yan Xiong, Yiqing Hu, Xudong Gong, Wenchao Huang
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引用次数: 0

摘要

我们提出了Magemite,一个细粒度的输入系统,利用周围设备空间(ADS)作为有限输入区域的扩展。Magemite背后的关键洞察是,集成在智能设备中的磁传感器可以感知附近的磁场强度。使用永磁体,用户可以在ADS中“写入”与匹配的设备通信。与以往的磁感测方案只能识别粗粒度的手势不同,Magemite可以识别用户的细粒度输入,如字符。然而,个体不同的书写方式影响了识别的准确性。为了解决这一问题,我们对输入轨迹进行预处理,提取轨迹的不同特征来唯一识别用户的输入,然后使用这些特征向量来训练几个模式识别模型来进行字符识别。在不同场景下对Magemite进行了评估,实验结果表明,Magemite的平均识别准确率在85%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Magemite: Character inputting system based on magnetic sensor
We propose Magemite, a fine-grained input system that exploits the around device space (ADS) as an expansion of the limited input area. The key insight underlying Magemite is, magnetic sensor integrated in smart devices can sense nearby magnetic field strength. Using a permanent magnet, users could “write” in ADS to communicate with matched devices. Different from previous magnetic-sensing schemes that recognize only coarse-grained gestures, Magemite can recognize user's fine-grained input like characters. However, individual's diverse writing patterns affect the recognition accuracy. To address this challenge, we preprocess the input trajectories and abstract different features of trajectories to uniquely identify user's input, then use these feature vectors to train several pattern recognition models for character recognition. We evaluate Magemite in various scenarios, and experimental results show Magemite can achieve average recognition accuracy over 85%.
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