Despite the advent of touchscreens, typing on physical keyboards remains most efficient for entering text, because users can leverage all fingers across a full-size keyboard for convenient typing. As users increasingly type on the go, text input on mobile and wearable devices has had to compromise on full-size typing. In this paper, we present TapType, a mobile text entry system for full-size typing on passive surfaces--without an actual keyboard. From the inertial sensors inside a band on either wrist, TapType decodes and relates surface taps to a traditional QWERTY keyboard layout. The key novelty of our method is to predict the most likely character sequences by fusing the finger probabilities from our Bayesian neural network classifier with the characters' prior probabilities from an n-gram language model. In our online evaluation, participants on average typed 19 words per minute with a character error rate of 0.6% after 30 minutes of training. Expert typists thereby consistently achieved more than 25 WPM at a similar error rate. We demonstrate applications of TapType in mobile use around smartphones and tablets, as a complement to interaction in situated Mixed Reality outside visual control, and as an eyes-free mobile text input method using an audio feedback-only interface.

翻译：尽管触摸屏技术已经普及，但在物理键盘上打字仍然是最高效的文本输入方式，因为用户可以在全尺寸键盘上运用所有手指进行便捷输入。随着移动场景下的输入需求日益增长，移动与可穿戴设备的文本输入不得不对全尺寸打字体验作出妥协。本文提出TapType——一种可在无实体键盘的被动表面上实现全尺寸打字的移动文本输入系统。该系统通过佩戴在双腕的传感带内置惯性传感器，解码表面敲击动作并将其映射至传统QWERTY键盘布局。本方法的核心创新在于：通过贝叶斯神经网络分类器生成的手指概率与n-gram语言模型提供的字符先验概率相融合，预测最可能的字符序列。在线评估中，参与者经过30分钟训练后平均打字速度达到每分钟19个单词，字符错误率为0.6%。熟练打字者在此错误率水平下持续实现每分钟25词以上的输入速度。我们展示了TapType在智能手机和平板电脑周边的移动应用场景，作为视觉不可控环境下混合现实交互的补充方案，以及通过纯音频反馈界面实现的无视觉移动文本输入方法。