SpeciFingers: Finger Identification and Error Correction on Capacitive Touchscreens

Zeyuan Huang, Cangjun Gao, Haiyan Wang, Xiaoming Deng, Yu-Kun Lai, Cuixia Ma, Sheng-feng Qin, Yong-Jin Liu, Hongan Wang
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Abstract

The inadequate use of finger properties has limited the input space of touch interaction. By leveraging the category of contacting fingers, finger-specific interaction is able to expand input vocabulary. However, accurate finger identification remains challenging, as it requires either additional sensors or limited sets of identifiable fingers to achieve ideal accuracy in previous works. We introduce SpeciFingers, a novel approach to identify fingers with the capacitive raw data on touchscreens. We apply a neural network of an encoder-decoder architecture, which captures the spatio-temporal features in capacitive image sequences. To assist users in recovering from misidentification, we propose a correction mechanism to replace the existing undo-redo process. Also, we present a design space of finger-specific interaction with example interaction techniques. In particular, we designed and implemented a use case of optimizing the performance in pointing on small targets. We evaluated our identification model and error correction mechanism in our use case.
SpeciFingers:电容式触摸屏上的手指识别和纠错
手指属性的使用不足限制了触摸交互的输入空间。通过利用接触手指的类别,特定手指的交互能够扩大输入词汇量。然而,准确识别手指仍然具有挑战性,因为这需要额外的传感器或有限的可识别手指集,才能达到以往工作中的理想精度。我们介绍的 SpeciFingers 是一种利用触摸屏上的电容原始数据识别手指的新方法。我们采用编码器-解码器架构的神经网络,捕捉电容式图像序列中的时空特征。为了帮助用户从错误识别中恢复过来,我们提出了一种纠正机制来取代现有的撤销重做过程。此外,我们还提出了手指特定交互的设计空间,并举例说明了交互技术。特别是,我们设计并实施了一个使用案例,以优化指向小目标的性能。我们在使用案例中评估了我们的识别模型和纠错机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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