Unintrusive eating recognition using Google Glass

Shah Atiqur Rahman, Christopher Merck, Yuxiao Huang, Samantha Kleinberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

63 Scopus citations

Abstract

Activity recognition has many health applications, from helping individuals track meals and exercise to providing treatment reminders to people with chronic illness and improving closed-loop control of diabetes. While eating is one of the most fundamental health-related activities, it has proven difficult to recognize accurately and unobtrusively. Body-worn and environmental sensors lack the needed specificity, while acoustic and accelerometer sensors worn around the neck may be intrusive and uncomfortable. We propose a new approach to identifying eating based on head movement data from Google Glass. We develop the Glass Eating and Motion (GLEAM) dataset using sensor data collected from 38 participants conducting a series of activities including eating. We demonstrate that head movement data are sufficient to allow recognition of eating with high precision and minimal impact on privacy and comfort.

Original languageEnglish
Title of host publicationProceedings of the 2015 9th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2015
Pages108-111
Number of pages4
ISBN (Electronic)9781631900457
DOIs
StatePublished - 8 Dec 2015
Event9th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2015 - Istanbul, Turkey
Duration: 20 May 201523 May 2015

Publication series

NameProceedings of the 2015 9th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2015

Conference

Conference9th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2015
Country/TerritoryTurkey
CityIstanbul
Period20/05/1523/05/15

Keywords

  • activity recognition
  • eating
  • sensor data

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