TapNet：一个用于屏幕外移动输入的多任务学习CNN的设计、训练、实现和应用
TapNet: The Design, Training, Implementation, and Applications of a Multi-Task Learning CNN for Off-Screen Mobile Input
摘要：
为了在没有专门硬件的情况下实现屏幕外交互，我们研究使用深度学习方法将手机上常见的内置IMU传感器（加速度计和陀螺仪）处理为一组有用的单手交互事件。我们介绍了TapNet的设计、训练、实现和应用，TapNet是一个多任务网络，可以检测智能手机上的窃听。通过手机形状因子作为辅助信息，TapNet可以跨设备联合学习数据，同时识别多个敲击属性，包括敲击方向和敲击位置。我们开发了两个数据集，包括超过135 K的训练样本，38 K的测试样本和32个参与者。实验评估证明了TapNet设计的有效性及其对最先进技术的显著改进。随着数据集（https：//sites.google.com/site/michaelxlhuang/tagets/tapnet-dataset）和广泛的实验，TapNet为屏幕外移动输入建立了新的技术基础。
英文摘要：
To make off-screen interaction without specialized hardware practical, we investigate using deep learning methods to process the common built-in IMU sensor (accelerometers and gyroscopes) on mobile phones into a useful set of one-handed interaction events. We present the design, training, implementation and applications of TapNet, a multi-task network that detects tapping on the smartphone. With phone form factor as auxiliary information, TapNet can jointly learn from data across devices and simultaneously recognize multiple tap properties, including tap direction and tap location. We developed two datasets consisting of over 135K training samples, 38K testing samples, and 32 participants in total. Experimental evaluation demonstrated the effectiveness of the TapNet design and its significant improvement over the state of the art. Along with the datasets, (https://sites.google.com/site/michaelxlhuang/datasets/tapnet-dataset), and extensive experiments, TapNet establishes a new technical foundation for off-screen mobile input.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
