IEICE TRANSACTIONS on Information
Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.11 pp.2795-2807
- Publication Date
- 2018/11/01
- Publicized
- 2018/08/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7076
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Ghulam HUSSAIN
Sungkyunkwan University
Kamran JAVED
Sungkyunkwan University
Jundong CHO
Sungkyunkwan University,North University of China
Juneho YI
Sungkyunkwan University
Keyword
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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
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@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},}
Copy
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 -
English
