Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Area of Science:

• Nutritional Science
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Traditional dietary assessment relies on self-reports, which are burdensome and prone to errors.
• Technological advancements enable more accurate and less effortful assessment tools for food intake and eating behavior.
• Existing methods often depend on memory or estimations, leading to inaccuracies.

Purpose of the Study:

• To develop and evaluate a novel sensor-based device for objective assessment of food intake and eating behavior.
• To provide a more accurate and less burdensome alternative to conventional dietary assessment methods.
• To explore the potential for real-time, personalized dietary feedback.

Main Methods:

• A dining tray integrated with a video camera and three weighing stations was developed.
• Weighing stations continuously monitored food and beverage weight.
• AI-based facial expression software analyzed eating behaviors (chews, bites) from video data.
• Data was wirelessly transmitted in real-time to a PC for analysis.

Main Results:

• The device accurately calculates food intake amount, eating rate, and bite size.
• Direct measurement of food intake within a meal eliminates reliance on memory or estimation.
• The system provides objective, real-time data on eating patterns.

Conclusions:

• The sensor-based dining tray offers a direct, objective, and efficient method for assessing daily food intake and eating behavior.
• This technology has significant potential for research applications and future integration with health apps for personalized guidance.
• It represents a substantial advancement over traditional, self-report-based dietary assessment techniques.