poster

SipBit: A Sensing Platform to Recognize Beverage Type, Volume, and Sugar Content Using Electrical Impedance Spectroscopy and Deep Learning

Authors:

Chamath Amarasinghe,

Nimesha RanasingheAuthors Info & Claims

CHI EA '22: CHI Conference on Human Factors in Computing Systems Extended Abstracts

Article No.: 371, Pages 1 - 8

https://doi.org/10.1145/3491101.3519713

Published: 28 April 2022 Publication History

Abstract

We present SipBit, a sensing platform that digitally recognizes beverages and their attributes, an essential component in facilitating novel human-food interactions. SipBit consists of an electrical impedance measurement unit and a recognition method based on deep learning techniques. First, impedance measurements of a beverage are acquired using Electrical Impedance Spectroscopy. Then, a multi-task network cascade algorithm was employed to identify eight different beverage types in various volume levels and sugar concentrations. Results show that the multi-task network cascade discriminates beverage types with an accuracy of 96.32%, and estimates volumes with a root mean square error of 13.74ml and sugar content with a root mean square error of 7.99gdm− 3. Future work will include: 1) developing utensils embedded with SipBit for automatic beverage and attribute recognition, and 2) further developing SipBit to recognize additional beverage types and their attributes, thus enabling a new avenue for designing human-food interactive technologies.

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Cited By

Amarasinghe CRanasinghe N(2024)Digital Food Sensing and Ingredient Analysis Techniques to Facilitate Human-Food Interface DesignsACM Computing Surveys10.1145/368567557:1(1-39)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3685675
Mayumi DNakamura YMatsuda YMisaki SYasumoto K(2024)Aromug: A Mug-Type Olfactory Interface to Enhance the Sweetness Perception of BeveragesIEEE Access10.1109/ACCESS.2024.340139212(78366-78378)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3401392
Liu MZhou BRey VBian SLukowicz P(2024)iEat: automatic wearable dietary monitoring with bio-impedance sensingScientific Reports10.1038/s41598-024-67765-514:1Online publication date: 2-Aug-2024
https://doi.org/10.1038/s41598-024-67765-5

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cover image ACM Conferences

CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

3066 pages

ISBN:9781450391566

DOI:10.1145/3491101

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA

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Published: 28 April 2022

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Cited By

Amarasinghe CRanasinghe N(2024)Digital Food Sensing and Ingredient Analysis Techniques to Facilitate Human-Food Interface DesignsACM Computing Surveys10.1145/368567557:1(1-39)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3685675
Mayumi DNakamura YMatsuda YMisaki SYasumoto K(2024)Aromug: A Mug-Type Olfactory Interface to Enhance the Sweetness Perception of BeveragesIEEE Access10.1109/ACCESS.2024.340139212(78366-78378)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3401392
Liu MZhou BRey VBian SLukowicz P(2024)iEat: automatic wearable dietary monitoring with bio-impedance sensingScientific Reports10.1038/s41598-024-67765-514:1Online publication date: 2-Aug-2024
https://doi.org/10.1038/s41598-024-67765-5

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