@inproceedings{f25b9e85223d4f67b6521692a55650e4,
title = "Poster abstract: A beverage intake tracking system based on machine learning algorithms, and ultrasonic and color sensors",
abstract = "We present a novel approach for monitoring beverage intake. Our system is composed of an ultrasonic sensor, an RGB color sensor, and machine learning algorithms. The system not only measures beverage volume but also detects beverage types. The sensor unit is lightweight that can be mounted on the lid of any drinking botle. Our experimental results demonstrate that the proposed approach achieves more than 97% accuracy in beverage type classification. Furthermore, our regression-based volume measurement hasa nominal error of 3%.",
keywords = "Hydration monitoring, Machine learning, Mobile health, Nutrition monitoring",
author = "Mahdi Pedram and Rokni, {Seyed Ali} and Ramin Fallahzadeh and Hassan Ghasemzadeh",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017 ; Conference date: 18-04-2017 Through 20-04-2017",
year = "2017",
month = apr,
day = "18",
doi = "10.1145/3055031.3055065",
language = "English (US)",
series = "Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017",
publisher = "Association for Computing Machinery, Inc",
pages = "313--314",
booktitle = "Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017",
}