Detecting tapping motion on the side of mobile devices by probabilistically combining hand postures

William McGrath, Yang Li
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引用次数: 24

Abstract

We contribute a novel method for detecting finger taps on the different sides of a smartphone, using the built-in motion sensors of the device. In particular, we discuss new features and algorithms that infer side taps by probabilistically combining estimates of tap location and the hand pose--the hand holding the device. Based on a dataset collected from 9 participants, our method achieved 97.3% precision and 98.4% recall on tap event detection against ambient motion. For detecting single-tap locations, our method outperformed an approach that uses inferred hand postures deterministically by 3% and an approach that does not use hand posture inference by 17%. For inferring the location of two consecutive side taps from the same direction, our method outperformed the two baseline approaches by 6% and 17% respectively. We discuss our insights into designing the detection algorithm and the implication on side tap-based interaction behaviors.
通过概率组合手势来检测移动设备侧面的敲击动作
我们提供了一种新的方法来检测手指敲击智能手机的不同侧面,使用设备的内置运动传感器。特别是,我们讨论了新的特征和算法,通过概率结合对点击位置和手姿势的估计来推断侧击。基于9个参与者的数据集,我们的方法在针对环境运动的轻拍事件检测上达到了97.3%的准确率和98.4%的召回率。对于检测单次点击位置,我们的方法比确定使用推断手势的方法高出3%,比不使用推断手势的方法高出17%。对于从同一方向推断两个连续侧钻的位置,我们的方法分别比两种基线方法高出6%和17%。我们讨论了我们对设计检测算法的见解以及对基于侧击的交互行为的影响。
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