The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Le contrôle automatique de la consommation alimentaire dans des conditions de vie libres reste un problème ouvert à résoudre. Cet article présente un nouveau système portable de type collier intégré à un capteur piézoélectrique pour surveiller le comportement ingestion en détectant les mouvements de la peau à partir de la trachée inférieure. Les événements détectés sont incorporés pour la classification des aliments. Contrairement au précédent système basé sur un capteur piézoélectrique de pointe qui utilisait des fonctionnalités de spectrogramme, nous avons essayé d'exploiter pleinement les signaux basés sur le domaine temporel pour des fonctionnalités optimales. Grâce à de nombreuses évaluations sur la longueur d'une image, nous avons trouvé les meilleures performances avec une longueur d'image de 70 échantillons (3.5 secondes). Cela démontre que la séquence de mastication contient des informations importantes pour la classification des aliments. Les résultats expérimentaux montrent la validité de l'algorithme proposé pour la détection de la consommation alimentaire et la classification des aliments dans des scénarios réels. Notre système donne une précision de 89.2 % pour la détection de la consommation alimentaire et de 80.3 % pour la classification des aliments sur 17 catégories d'aliments. De plus, notre système est basé sur une application pour smartphone, qui aide les utilisateurs à vivre sainement en leur fournissant des informations en temps réel sur les épisodes et les types d'aliments ingérés.
Ghulam HUSSAIN
Sungkyunkwan University
Kamran JAVED
Sungkyunkwan University
Jundong CHO
Sungkyunkwan University,North University of China
Juneho YI
Sungkyunkwan University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Ghulam HUSSAIN, Kamran JAVED, Jundong CHO, Juneho YI, "Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 11, pp. 2795-2807, November 2018, doi: 10.1587/transinf.2018EDP7076.
Abstract: Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7076/_p
Copier
@ARTICLE{e101-d_11_2795,
author={Ghulam HUSSAIN, Kamran JAVED, Jundong CHO, Juneho YI, },
journal={IEICE TRANSACTIONS on Information},
title={Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System},
year={2018},
volume={E101-D},
number={11},
pages={2795-2807},
abstract={Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.},
keywords={},
doi={10.1587/transinf.2018EDP7076},
ISSN={1745-1361},
month={November},}
Copier
TY - JOUR
TI - Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System
T2 - IEICE TRANSACTIONS on Information
SP - 2795
EP - 2807
AU - Ghulam HUSSAIN
AU - Kamran JAVED
AU - Jundong CHO
AU - Juneho YI
PY - 2018
DO - 10.1587/transinf.2018EDP7076
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2018
AB - Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.
ER -