VibePhone: efficient surface recognition for smartphones using vibration

Cho, Jungchan; Hwang, Inhwan; Oh, Songhwai

doi:10.1007/s10044-015-0460-8

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Vibrations carry rich information of locations [8-10, 12, 17], identifications [4,26,57,57,62], gestures [5-7, 14-16, 28, 67], activities [11,13,33], speech [26], materials [3,46], weights [69] and so on for sensing technology. Kunze et al [37] and Cho et al [18] find the location of a mobile phone by measuring acceleration (and also sound [37]) using internal sensors after imposing a vibration. Such hand-transmitted vibrations are generated and measured by surface transducers and contact microphones.…”

Section: Vibration Based Sensingmentioning

confidence: 99%

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ViObject

Chen,

Lin,

Peng

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Knowing the object grabbed by a hand can offer essential contextual information for interaction between the human and the physical world. This paper presents a novel system, ViObject, for passive object recognition that uses accelerometer and gyroscope sensor data from commodity smartwatches to identify untagged everyday objects. The system relies on the vibrations caused by grabbing objects and does not require additional hardware or human effort. ViObject's ability to recognize objects passively can have important implications for a wide range of applications, from smart home automation to healthcare and assistive technologies. In this paper, we present the design and implementation of ViObject, to address challenges such as motion interference, different object-touching positions, different grasp speeds/pressure, and model customization to new users and new objects. We evaluate the system's performance using a dataset of 20 objects from 20 participants and show that ViObject achieves an average accuracy of 86.4%. We also customize models for new users and new objects, achieving an average accuracy of 90.1%. Overall, ViObject demonstrates a novel technology concept of passive object recognition using commodity smartwatches and opens up new avenues for research and innovation in this area.

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Section: Vibration Based Sensingmentioning

confidence: 99%

“…6.2.1 Data Collection. We recruited 10 new users (6 males) in the age range between [18,35], which had a mean wrist diameter of 6.7 cm (SD=1.5). Their body mass indexes (BMIs) range from 17.12 (lean) to 28.87 (obese).…”

Section: Study 2: New Users and New Objectsmentioning

confidence: 99%

ViObject

Chen,

Lin,

Peng

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…Several vibration based sensing schemes have been proposed in the past to realize different kinds of applications [6], [7], [8], [9], [10], [3], [11], [12], [13], [2], [14], [15], [16]. However, none of them satisfies all the above three requirements.…”

Section: Limitations Of Prior Artmentioning

confidence: 99%

“…However, these schemes are incompatible with COTS smartphones and are not easily generalizable to different hardware because most COTS smartphones have limited sensing capabilities and control over the hardware installed in them. Most COTS smartphones based schemes rely on motion based features extracted from built-in IMU sensors [1], [4], [6], [7], [10]. However, these features are coarse-grained because the sampling frequencies of IMU sensors on COTS and less than 4kHz even after driver modifications [11]) compared to microphones (usually above 44kHz), which naturally leads to lower classification accuracies.…”

Section: Limitations Of Prior Artmentioning

confidence: 99%

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Fine-grained Vibration Based Sensing Using a Smartphone

Ali¹,

Liu²

2020

Preprint

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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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