Poster abstract: A beverage intake tracking system based on machine learning algorithms, and ultrasonic and color sensors

Mahdi Pedram, Seyed Ali Rokni, Ramin Fallahzadeh, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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%.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017
PublisherAssociation for Computing Machinery, Inc
Pages313-314
Number of pages2
ISBN (Electronic)9781450348904
DOIs
StatePublished - Apr 18 2017
Externally publishedYes
Event16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017 - Pittsburgh, United States
Duration: Apr 18 2017Apr 20 2017

Publication series

NameProceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017

Conference

Conference16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017
Country/TerritoryUnited States
CityPittsburgh
Period4/18/174/20/17

Keywords

  • Hydration monitoring
  • Machine learning
  • Mobile health
  • Nutrition monitoring

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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