Monitoring eating habits using a piezoelectric sensor-based necklace.
Maintaining appropriate levels of food intake and developing regularity in eating habits is crucial to weight loss and the preservation of a healthy lifestyle. Moreover, awareness of eating habits is an important step towards portion control and weight loss. In this paper, we introduce a novel food-...
| Published in: | Computers in Biology & Medicine Vol. 58; pp. 46 - 56 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Published: |
Elsevier B.V.
2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109702036&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109702036 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00104825 JC2 jtl: Computers in Biology & Medicine issn: 00104825 maglogo: N pubinfo: dt: 2015 vid: 58 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 109702036 NLM25616023 2012907755 10.1016/j.compbiomed.2015.01.005 NLM25616023 109702036 ppf: 46 ppct: 10 formats: tig: atl: Monitoring eating habits using a piezoelectric sensor-based necklace. aug: au: Kalantarian, Haik Alshurafa, Nabil Le, Tuan Sarrafzadeh, Majid sug: ab: Maintaining appropriate levels of food intake and developing regularity in eating habits is crucial to weight loss and the preservation of a healthy lifestyle. Moreover, awareness of eating habits is an important step towards portion control and weight loss. In this paper, we introduce a novel food-intake monitoring system based around a wearable wireless-enabled necklace. The proposed necklace includes an embedded piezoelectric sensor, small Arduino-compatible microcontroller, Bluetooth LE transceiver, and Lithium-Polymer battery. Motion in the throat is captured and transmitted to a mobile application for processing and user guidance. Results from data collected from 30 subjects indicate that it is possible to detect solid and liquid foods, with an F-measure of 0.837 and 0.864, respectively, using a naive Bayes classifier. Furthermore, identification of extraneous motions such as head turns and walking are shown to significantly reduce the false positive rate of swallow detection. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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