Pervasive eating habits monitoring and recognition through a wearable acoustic sensor?

Yin Bi, Wenyao Xu, Nan Guan, Yangjie Wei, Wang Yi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Eating habits provide clinical diagnosis evidences of lifestyle related diseases, such as dysphagia and indigestion. However, it is costly to obtain eating habit information of common people in terms of both time and expenses. This paper presents a pervasive approach for eating habit monitoring and recognition by a necklace-like device and a smartphone communicating via bluetooth. The necklace-like device acquires acoustic signals from the throat, and the data are processed in the smartphone to recognize important features. With complex acoustic signals collected from the throat, our method comprehensively analyzes and recognizes different events including chewing, swallowing, and breathing in the smartphone. Experiments show that the proposed approach can recognize different acoustic events effectively, and the recognition accuracy with K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) is 86.82% and 98.35%, respectively. Finally, a real eating case study is conducted to validate the proposed approach.
Original languageEnglish
Title of host publicationProceedings - PERVASIVEHEALTH 2014
Subtitle of host publication8th International Conference on Pervasive Computing Technologies for Healthcare
PublisherICST
Pages174-177
Number of pages4
ISBN (Electronic)9781631900112
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event8th International Conference on Pervasive Computing Technologies for Healthcare, PERVASIVEHEALTH 2014 - Oldenburg, Germany
Duration: 20 May 201423 May 2014

Conference

Conference8th International Conference on Pervasive Computing Technologies for Healthcare, PERVASIVEHEALTH 2014
Country/TerritoryGermany
CityOldenburg
Period20/05/1423/05/14

Keywords

  • Eating habit
  • Feature extraction
  • KNN
  • SVM

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Health Informatics

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