TY - JOUR
T1 - Monitoring eating habits using a piezoelectric sensor-based necklace
AU - Kalantarian, Haik
AU - Alshurafa, Nabil
AU - Le, Tuan
AU - Sarrafzadeh, Majid
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - 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.
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.
KW - Necklace
KW - Nutrition
KW - Piezoelectric sensor
KW - Wireless health
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U2 - 10.1016/j.compbiomed.2015.01.005
DO - 10.1016/j.compbiomed.2015.01.005
M3 - Article
C2 - 25616023
AN - SCOPUS:84921461226
SN - 0010-4825
VL - 58
SP - 46
EP - 55
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
ER -