基于智能手机的细粒度振动传感
Fine-grained Vibration Based Sensing Using a Smartphone
摘要：
基于表面的振动特征识别表面是有用的，因为它可以在不需要任何附加硬件（例如近场通信（NFC）标签）的情况下实现对不同位置的标记。然而，先前的基于振动的表面识别方案要么使用定制硬件来创建和感测振动，这使得它们难以采用，要么使用商业现货（COTS）智能手机中的惯性（IMU）传感器来感测由于振动而产生的移动，这使得它们由于IMU传感器的低采样率而变得粗粒度。基于主流COTS智能手机的方案也容易受到智能手机振动机制中基于固有硬件的不规则性的影响。此外，使用麦克风来感测振动的现有方案易于受到短期和恒定的背景噪声（例如，间歇性谈话、排气扇等）的影响。因为麦克风不仅捕获由振动产生的声音，而且还捕获存在于环境中的其它干扰声音。在本文中，我们提出了VibroTag，一个强大的和实用的基于振动的传感方案，与智能手机具有不同的硬件，可以提取不同表面的细粒度的振动特征，是鲁棒的环境噪声和基于硬件的不规则性。我们在两款不同的Android手机上实现了VibroTag，并在多个不同的环境中进行了评估，我们连续5到20天从4个人那里收集数据。我们的研究结果表明，VibroTag在识别24个不同位置/表面时达到了86.55%的平均准确率，即使其中一些表面是由类似的材料制成的。VibroTag的准确度比最先进的基于IMU的方案之一所实现的平均准确度49.25%高出37%，我们实施了该方案以与VibroTag进行比较。
英文摘要：
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.
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