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The human body gets energy from the three macronutrients: carbohydrates, proteins, and fats. Energy is released when the chemical bonds in the organic compounds present in the food are broken down. The energy content of food is measured in kilocalories (kcal), defined as the amount of heat required to raise the temperature of one kilogram of water by one degree Celsius. This value is determined by measuring the temperature change of the water surrounding a calorimeter after the complete...
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Towards interactive explanation-based nutrition virtual coaching systems.

Berk Buzcu1,2, Melissa Tessa3, Igor Tchappi4

  • 1Computer Science, Özyeğin University, Istanbul, Turkey.

Autonomous Agents and Multi-Agent Systems
|January 23, 2024
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Summary
This summary is machine-generated.

Personalized nutrition virtual coaching systems (NVC) explain recommendations, boosting user trust and engagement. This interactive approach enhances dietary advice effectiveness compared to traditional methods.

Keywords:
Explainable AIInteractiveNutrition virtual coachRecommender systems

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Nutritional Science

Background:

  • Growing awareness of healthy lifestyles fuels demand for personalized health coaching applications.
  • Nutrition Virtual Coaching (NVC) systems are emerging to bridge human-machine interaction with recommendation and argumentation systems.
  • Opaque data-driven mechanisms in NVC necessitate explainability for user trust and acceptance.

Purpose of the Study:

  • To develop and evaluate an NVC system that provides personalized food recommendations with explanations.
  • To implement a user-agent negotiation process with run-time feedback for interactive dietary advice.
  • To assess the acceptability and effectiveness of the proposed NVC system through user studies.

Main Methods:

  • Designing NVC agents to generate personalized food recommendations based on user-specific factors (allergies, habits, preferences).
  • Developing a negotiation process with real-time feedback mechanisms for user interaction with recommendations and explanations.
  • Conducting experiments with diverse participants to evaluate the system's acceptability and effectiveness.

Main Results:

  • Participants valued receiving explanations and personalized information for food recommendations.
  • The interactive recommendation system demonstrated superior effectiveness over traditional systems.
  • User feedback mechanisms and tailored information significantly improved engagement and acceptance.

Conclusions:

  • Explainable NVC systems enhance user acceptance, transparency, and engagement in personalized nutrition.
  • Interactive negotiation and feedback loops are crucial for effective intelligent health coaching.
  • The proposed system offers a promising approach for improving dietary adherence and health outcomes.