Abstract
Liquid identification plays an essential role in our daily lives. However, existing RF sensing approaches still require dedicated hardware such as RFID readers and UWB transceivers, which are not readily available to most users. In this article, we propose Akte-Liquid, which leverages the speaker on smartphones to transmit acoustic signals, and the microphone on smartphones to receive reflected signals to identify liquid types and analyze the liquid concentration. Our work arises from the acoustic intrinsic impedance property of liquids, in that different liquids have different intrinsic impedance, causing reflected acoustic signals of liquids to differ. Then, we discover that the amplitude-frequency feature of reflected signals may be utilized to represent the liquid feature. With this insight, we propose new mechanisms to eliminate the interference caused by hardware and multi-path propagation effects to extract the liquid features. In addition, we design a new Siamese network-based structure with a specific training sample selection mechanism to reconstruct the extracted feature to container-irrelevant features. Our experimental evaluations demonstrate that Akte-Liquid is able to distinguish 20 types of liquids at a higher accuracy, and to identify food additives and measure protein concentration in the artificial urine with a 92.3% accuracy under 1 mg/100 mL as well.
- [1] . 2016. Fully-convolutional Siamese Networks for Object Tracking. Springer, Cham.Google Scholar
- [2] . 2019. Fusing MFCC and LPC features using 1D triplet CNN for speaker recognition in severely degraded audio signals. IEEE Transactions on Information Forensics and Security 15 (2019), 1616–1629.Google ScholarDigital Library
- [3] J. Y. Chung and D. A. Blaser. 1980. Transfer function method of measuring in-duct acoustic properties. II. Experiment. The Journal of the Acoustical Society of America 68, 3 (1980), 914–921.Google Scholar
- [4] . 2018. Liquid: A wireless liquid identifier. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services. 442–454.Google ScholarDigital Library
- [5] . 2012. Fingerprinting food: Current technologies for the detection of food adulteration and contamination. Chemical Society Reviews 41, 17 (2012), 5706–5727.Google ScholarCross Ref
- [6] . 2021. RF-Identity: Non-intrusive person identification based on commodity RFID devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (2021), 1–23.Google ScholarDigital Library
- [7] . 2019. EarEcho: Using ear canal echo for wearable authentication. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (2019), 1–24.Google ScholarDigital Library
- [8] . 2020. Food and liquid sensing in practical environments using RFIDs. In Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI’20).Google Scholar
- [9] . 2018. Learning food quality and safety from wireless stickers. In Proceedings of the 17th ACM Workshop on Hot Topics in Networks. 106–112.Google ScholarDigital Library
- [10] . 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778.Google ScholarCross Ref
- [11] . 2021. Vi-liquid: Unknown liquid identification with your smartphone vibration. In MobiCom. 174–187.Google Scholar
- [12] . 1998. Acoustics-determination of sound absorption coefficient and impedance in impedance tubes-Part 2: Transfer-function method. (1998).Google Scholar
- [13] . 1987. Acoustic Waves: Devices, Imaging, and Analog Signal Processing. Vol. 107. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
- [14] . 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. 1097–1105.Google ScholarDigital Library
- [15] . 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 11 (1998), 2278–2324.Google ScholarCross Ref
- [16] . 2020. SCANet: Sensor-based continuous authentication with two-stream convolutional neural networks. ACM Transactions on Sensor Networks 16, 3 (2020), 1–27.Google ScholarDigital Library
- [17] . 2021. BlinkListener: “Listen” to your eye blink using your smartphone. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 5, 2 (2021), 1–27.Google ScholarDigital Library
- [18] . 2016. CAT: High-precision acoustic motion tracking. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. 69–81.Google ScholarDigital Library
- [19] . 2016. Nutrilyzer: A mobile system for characterizing liquid food with photoacoustic effect. In Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM. 123–136.Google ScholarDigital Library
- [20] . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.Google Scholar
- [21] . 2018. Cross-platform support for rapid development of mobile acoustic sensing applications. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services. 455–467.Google ScholarDigital Library
- [22] U.S. FOOD and DRUG ADMINISTRATION. 2004. Overview of Food Ingredients, Additives, Colors. https://www.fda.gov/food/food-ingredients-packaging/overview-food-ingredients-additives-colors/.Google Scholar
- [23] Hideo Utsuno, Toshimitsu Tanaka, Takeshi Fujikawa, and A. F. Seybert. 1989. Transfer function method for measuring characteristic impedance and propagation constant of porous materials. The Journal of the Acoustical Society of America 86, 2 (1989), 637–643.Google Scholar
- [24] . 2016. Gated siamese convolutional neural network architecture for human re-identification. European Conference on Computer Vision. (2016).Google Scholar
- [25] . 2013. Dude, where’s my card?: RFID positioning that works with multipath and non-line of sight. Computer Communication Review 43, 4 (2013), 51–62.Google ScholarDigital Library
- [26] . 2017. TagScan: Simultaneous target imaging and material identification with commodity RFID devices. In Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking. 288–300.Google ScholarDigital Library
- [27] 2007. Microalbuminuria and cardiovascular disease. Clinical Journal of the American Society of Nephrology 2, 3 (2007), 581–590.Google ScholarCross Ref
- [28] . 2019. Tagtag: Material sensing with commodity RFID. In Proceedings of the 17th Conference on Embedded Networked Sensor Systems. 338–350.Google ScholarDigital Library
- [29] . 2019. Liquid testing with your smartphone. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. 275–286.Google ScholarDigital Library
- [30] . 2016. Study on CNN in the recognition of emotion in audio and images. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS’16). IEEE, 1–5.Google ScholarCross Ref
- [31] . 2020. Your smart speaker can “hear” your heartbeat! Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 4 (2020), 1–24.Google ScholarDigital Library
- [32] . 2019. Pdvocal: Towards privacy-preserving Parkinson’s disease detection using non-speech body sounds. In The 25th Annual International Conference on Mobile Computing and Networking. 1–16.Google ScholarDigital Library
Index Terms
- Akte-Liquid: Acoustic-based Liquid Identification with Smartphones
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