A Low Latency On-Body Typing System through Single Vibration Sensor
A Low Latency On-Body Typing System through Single Vibration Sensor
复制标题
通过单个振动传感器的低延迟人体打字系统
DOI：
10.1109/tmc.2019.2928549
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发表时间：
2020-11
影响因子：
7.9
通讯作者：
Kaishun Wu
中科院分区：
文献类型：
--
作者：
Wenqiang Chen;Maoning Guan;Y;ao Huang;Lu Wang;Rukhsana Ruby;Wen Hu;Kaishun Wu
如今，智能腕带已经成为最流行的可穿戴设备之一，因为它们体积小，便携。然而，由于触摸屏的尺寸有限，智能腕带通常具有较差的交互体验。目前有一些将人体作为打字表面的研究，但由于采用多个传感器，采样率高，因此在实际应用中不便于携带，而且能耗大。为了打破这一僵局，我们提出了一种便携式的，具有成本效益的文本输入系统，称为ViType，它首先利用一个小的形状因子传感器，以实现实际的用户输入低得多的采样率。为了提高输入精度，同时降低采样率带来的振动信息，ViType设计了一套新颖的机制，包括细粒度特征提取来处理振动信号，以及运行时校准和自适应方案来从时间不稳定性引起的错误中恢复。在30名人类受试者身上进行了广泛的实验。结果表明，ViType对各种混杂因素具有稳健性。平均识别准确率为95%，每个键的初始训练样本大小为20。准确率是最先进的人体打字系统的1.54倍。此外，当打开运行时校准和自适应系统来更新和扩大训练样本量时，在一个月内平均准确率可以达到98%左右。
Nowadays, smart wristbands have become one of the most prevailing wearable devices, as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to type on. Yet, by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which first leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rates, ViType designs a set of novel mechanisms, including a fine-grained feature extraction to process the vibration signals, and a runtime calibration and adaptation scheme to recover from the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust against various confounding factors. The average recognition accuracy is 95 percent with an initial training sample size of 20 for each key. The accuracy is 1.54 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98 percent on average during one month.
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DOI：
10.1145/2642918.2647376
发表时间：
2014-10
期刊：
Proceedings of the 27th annual ACM symposium on User interface software and technology
影响因子：
--
作者：
W. Kienzle;K. Hinckley
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W. Kienzle;K. Hinckley
DOI：
10.1007/978-3-319-22723-8_34
发表时间：
2015-09
期刊：
SIGCAS Comput. Soc.
影响因子：
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Manuel Prätorius;A. Scherzinger;K. Hinrichs
影响因子：
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T. Sekitani;M. Kaltenbrunner;T. Yokota;T. Someya
DOI：
10.1145/3131672.3136984
发表时间：
2017-11
期刊：
Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems
影响因子：
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作者：
Wenqiang Chen;Y. Lian;Lu Wang;Rukhsana Ruby;Wen Hu;Kaishun Wu
通讯作者：
Wenqiang Chen;Y. Lian;Lu Wang;Rukhsana Ruby;Wen Hu;Kaishun Wu
DOI：
10.1145/2047196.2047255
发表时间：
2011-10
期刊：
Proceedings of the 24th annual ACM symposium on User interface software and technology
影响因子：
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作者：
Chris Harrison;Hrvoje Benko;Andrew D. Wilson
通讯作者：
Chris Harrison;Hrvoje Benko;Andrew D. Wilson