Automatic Food Intake Monitoring Based on Chewing Activity: A Survey

• Main Authors: Nur Asmiza Selamat, Sawal Hamid Md. Ali
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Food intake monitoring; automatic food intake detection; chewing sensor; chewing detection; chewing classification; chewing count
• Online Access: https://ieeexplore.ieee.org/document/9024026/

Good nutrition is essential for optimal growth, development, and prevention of disease. Due to the importance of nutrition in human life, researchers have been interested in understanding the science of assessing food intake episodes for decades. With the advancement of technology, automated food monitoring tool develops with the help of sensors to address issues related to self-reporting methods. Food monitoring technology is evolving rapidly due to the advancement of sensors; however, automatic monitoring of food intake remains open problems to be solved. For food intake episode detection and monitoring, the sensors used to detect bites, chew, swallow, and hand gestures movement. This survey will be focusing on chewing activity detection during eating episodes. In this survey, a wide range of chewing activity detection explored to outline the sensing design, classification methods, performances, chewing parameters, chewing data analysis as well as the challenges and limitations associated with them..