用手腕传感器在任意表面实现十指输入，准确率高且无需看屏。
TapType: Ten-finger text entry on everyday surfaces via Bayesian inference
- 基于贝叶斯神经网络融合手指概率与语言模型预测字符序列。
- 平均输入速度19词/分钟，错误率仅0.6%，专家超25词/分钟。
- 适合移动场景、混合现实交互及无视觉依赖的语音反馈输入。
尽管触屏普及，物理键盘仍是高效文本输入方式，因可使用全部手指进行全尺寸打字。随着用户移动中输入需求增加，移动端和可穿戴设备不得不牺牲全尺寸输入体验。本文提出TapType，一种可在被动表面（如桌面、大腿）上实现全尺寸打字的移动输入系统，仅通过佩戴在任一手腕上的惯性传感器捕捉敲击信号，并将其解码为传统QWERTY键盘布局对应的字符。方法核心在于利用贝叶斯神经网络分类器生成各手指敲击概率，再结合n-gram语言模型中的字符先验概率，联合推理最可能的字符序列。在线评估中，参与者经过30分钟训练后，平均达到19词/分钟的输入速度，字符错误率为0.6%；熟练用户持续实现超过25词/分钟的速率且错误率相近。我们展示了该系统在智能手机/平板操作、脱离视觉控制的混合现实交互，以及仅通过音频反馈的盲打场景中的应用潜力。
原文摘要 · Abstract (English)
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.
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