论文标题
使用智能手机的基于细粒的基于振动的感测
Fine-grained Vibration Based Sensing Using a Smartphone
论文作者
论文摘要
根据其振动签名识别表面很有用，因为它可以启用不同位置的标记，而无需任何其他硬件，例如近场通信（NFC）标签。但是，以前基于振动的表面识别方案要么使用自定义硬件来创建和传感振动，这使得它们难以采用，或者在商业现成（COTS）智能手机中使用惯性（IMU）传感器来感知由于IMU传感器采样速率而产生的振动，从而使它们变得粗糙。基于主流的COTS智能手机方案也容易受到智能手机振动机制固有的不规则性的影响。此外，使用麦克风来感知振动的现有方案容易出现短期和恒定的背景噪声（例如间歇性说话，排气风扇等），因为麦克风不仅捕获了振动所产生的声音，而且还捕获了环境中存在的其他干扰声音。在本文中，我们提出了Vibrotag，这是一种可与具有不同硬件的智能手机配合使用的强大而实用的感应方案，可以提取不同表面的细粒度振动特征，并且对环境噪音和基于硬件的不规则性非常强大。我们在两台不同的Android手机上实施了vibretag，并在多个不同的环境中进行了评估，在这些环境中，我们连续5至20天从4个个体收集了数据。我们的结果表明，即使某些表面是用相似材料制成的，颤音的平均准确度为86.55％，同时识别24个不同的位置/表面。 Vobleotag的精度比最先进的IMUS方案之一实现了49.25％的平均准确性37％，我们实施了与VibroTag进行比较。
Recognizing surfaces based on their vibration signatures is useful as it can enable tagging of different locations without requiring any additional hardware such as Near Field Communication (NFC) tags. However, previous vibration based surface recognition schemes either use custom hardware for creating and sensing vibration, which makes them difficult to adopt, or use inertial (IMU) sensors in commercial off-the-shelf (COTS) smartphones to sense movements produced due to vibrations, which makes them coarse-grained because of the low sampling rates of IMU sensors. The mainstream COTS smartphones based schemes are also susceptible to inherent hardware based irregularities in vibration mechanism of the smartphones. Moreover, the existing schemes that use microphones to sense vibration are prone to short-term and constant background noises (e.g. intermittent talking, exhaust fan, etc.) because microphones not only capture the sounds created by vibration but also other interfering sounds present in the environment. In this paper, we propose VibroTag, a robust and practical vibration based sensing scheme that works with smartphones with different hardware, can extract fine-grained vibration signatures of different surfaces, and is robust to environmental noise and hardware based irregularities. We implemented VibroTag on two different Android phones and evaluated in multiple different environments where we collected data from 4 individuals for 5 to 20 consecutive days. Our results show that VibroTag achieves an average accuracy of 86.55% while recognizing 24 different locations/surfaces, even when some of those surfaces were made of similar material. VibroTag's accuracy is 37% higher than the average accuracy of 49.25% achieved by one of the state-of-the-art IMUs based schemes, which we implemented for comparison with VibroTag.
