Fine-Grained Vibration Based Sensing Using a Smartphone

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 intertial (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 four individuals for 5 to 20 consecutive days. Our results show that VibroTag achieves an average accuracy of 86.55 percent while recognizing 24 different locations/surfaces, even when some of those surfaces were made of similar material. VibroTag's accuracy is 37 percent higher than the average accuracy of 49.25 percent achieved by one of the state-of-the-art IMUs based schemes, which we implemented for comparison with VibroTag.

Automated summary

In this paper, the authors propose VibroTag, a robust and practical vibration based sensing scheme that can extract fine-grained vibration signatures of different surfaces using smartphones with different hardware. The authors implemented VibroTag on two different Android phones and evaluated its performance in various environments, achieving an average accuracy of 86.55 percent while recognizing 24 different locations/surfaces.