FIT-Eve and ADAM: Estimation of velocity and energy for automated diet activity monitoring

Junghyo Lee, Prajwal Paudyal, Ayan Banerjee, Sandeep Gupta

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

5 Citations (Scopus)

Abstract

State-of-the-art techniques for eating activities analysis in dietary monitoring require significant user intervention, which is reported to be one of the major reasons for low adherence. There are limited works using wearables for fine-grained analysis of eating activities in terms of the eating speed, the type of food consumed, and the portion sizes. In this paper, we propose FIT-EVE&ADAM, an armband based diet monitoring system that provides such fine-grained analysis, triggered by a single hand gesture. The system collects the user's gesture using sensors such as electromyogram embedded in the armband device, along with food image data using color and thermal cameras. Finally, a novel feature selection method is applied on the data features to estimate eating speed and caloric intake with high accuracy (0.96 F1 score).

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1071-1074
Number of pages4
Volume2018-January
ISBN (Electronic)9781538614174
DOIs
StatePublished - Jan 16 2018
Event16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
Duration: Dec 18 2017Dec 21 2017

Other

Other16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
CountryMexico
CityCancun
Period12/18/1712/21/17

Fingerprint

Nutrition
Monitoring
Feature extraction
Cameras
Color
Sensors
Energy Intake
Hot Temperature

Keywords

  • Diet Monitoring
  • Gesture Recognition
  • Wearable

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Lee, J., Paudyal, P., Banerjee, A., & Gupta, S. (2018). FIT-Eve and ADAM: Estimation of velocity and energy for automated diet activity monitoring. In Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 (Vol. 2018-January, pp. 1071-1074). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2017.000-7

FIT-Eve and ADAM : Estimation of velocity and energy for automated diet activity monitoring. / Lee, Junghyo; Paudyal, Prajwal; Banerjee, Ayan; Gupta, Sandeep.

Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1071-1074.

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

Lee, J, Paudyal, P, Banerjee, A & Gupta, S 2018, FIT-Eve and ADAM: Estimation of velocity and energy for automated diet activity monitoring. in Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1071-1074, 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, Cancun, Mexico, 12/18/17. https://doi.org/10.1109/ICMLA.2017.000-7
Lee J, Paudyal P, Banerjee A, Gupta S. FIT-Eve and ADAM: Estimation of velocity and energy for automated diet activity monitoring. In Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1071-1074 https://doi.org/10.1109/ICMLA.2017.000-7
Lee, Junghyo ; Paudyal, Prajwal ; Banerjee, Ayan ; Gupta, Sandeep. / FIT-Eve and ADAM : Estimation of velocity and energy for automated diet activity monitoring. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1071-1074
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