A review of chewing detection for automated dietary monitoring

Minhad, Khairun Nisa’ and Selamat, Nur Asmiza and Yanxin, Wei and Md Ali, Sawal Hamid and Sobhan Bhuiyan, Mohammad Arif and Kelvin Jian, Aun Ooi and Samdin, Siti Balqis (2022) A review of chewing detection for automated dietary monitoring. Journal of the Chinese Institute of Engineers, 45 (4). pp. 331-341. ISSN 0253-3839

Text A REVIEW OF CHEWING DETECTION FOR AUTOMATED DIETARY MONITORING.PDF Download (2MB)

Abstract

A healthy dietary lifestyle prevents diseases and leads to good physical conditions. Poor dietary habits, such as eating disorders, emotional eating and excessive unhealthy food consumption, may cause health complications. People’s eating habits are monitored through automated dietary monitoring (ADM), which is considered a part of our daily life. In this study, the Google Scholar database from the last 5 years was considered. Articles that reported chewing activity characteristics and various wearable sensors used to detect chewing activities automatically were reviewed. Key challenges, including chew count, various food types, food classification and a large number of samples, were identified for further chewing data analysis. The chewing signal’s highest reported classification accuracy value was 99.85%, which was obtained using a piezoelectric contactless sensor and multistage linear SVM with a decision tree classifier. The decision tree approach was more robust and its classification accuracy (75%–93.3%) was higher than those of the Viterbi algorithm-based finite-state grammar approach, which yielded 26%–97% classification accuracy. This review served as a comparative study and basis for developing efficient ADM systems.

Item Type:	Article
Uncontrolled Keywords:	Automated dietary monitoring, chewing, wearable sensors
Divisions:	Faculty of Electrical Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	12 Apr 2023 10:58
Last Modified:	12 Apr 2023 10:58
URI:	http://eprints.utem.edu.my/id/eprint/26573
Statistic Details:	View Download Statistic

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