Type-hover-swipe in 96 bytes: a motion sensing mechanical keyboard
Type-hover-swipe in 96 bytes: a motion sensing mechanical keyboard
复制标题
96 字节的打字-悬停-滑动：动作感应机械键盘
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
J. Helmes
中科院分区：
文献类型：
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作者：
Stuart Taylor;Cem Keskin;Otmar Hilliges;S. Izadi;J. Helmes
我们提出了一种新型的增强机械键盘，能够感应丰富和富有表现力的运动手势上和直接在设备上执行。我们的硬件包括低分辨率矩阵的红外（IR）接近传感器散布在一个普通的机械键盘的键之间。这导致粗糙但高帧率的运动数据。我们扩展了传统上只用于静态分类的机器学习算法，以鲁棒地支持动态的时间手势。我们建议使用运动签名的技术，利用对运动历史图像和随机森林分类器，以鲁棒地识别一个大的运动手势集和直接在键盘上。我们的技术在leave-one-subject-out中实现了75.6%的平均每帧分类准确率，在半测试/半训练交叉验证中达到了89.9%。我们详细介绍了我们的硬件和手势识别算法，提供了性能和准确性数据，并展示了大量的手势设计与我们的设备执行。最后，我们从用户的定性反馈，讨论的局限性和未来的工作领域。
We present a new type of augmented mechanical keyboard, capable of sensing rich and expressive motion gestures performed both on and directly above the device. Our hardware comprises of low-resolution matrix of infrared (IR) proximity sensors interspersed between the keys of a regular mechanical keyboard. This results in coarse but high frame-rate motion data. We extend a machine learning algorithm, traditionally used for static classification only, to robustly support dynamic, temporal gestures. We propose the use of motion signatures a technique that utilizes pairs of motion history images and a random forest based classifier to robustly recognize a large set of motion gestures on and directly above the keyboard. Our technique achieves a mean per-frame classification accuracy of 75.6% in leave-one-subject-out and 89.9% in half-test/half-training cross-validation. We detail our hardware and gesture recognition algorithm, provide performance and accuracy numbers, and demonstrate a large set of gestures designed to be performed with our device. We conclude with qualitative feedback from users, discussion of limitations and areas for future work.