Design of AI-Enabled Application to Detect Ayurvedic Nutritional Values of Edible Items and Suggest a Diet

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Abstract

People these days primarily focus on having a well-measured calorific diet which results in a healthy body but lacks mindfulness. The Ayurvedic diet system, which is more than thousands of years old, focuses on balancing different energies in the body which in turn promotes wellness of both body and mind. Due to lack of information available, most of the youth is unaware of its benefits and hence struggles to follow a proper dietary system. From the initial interviews with N = 16 participants, it was identified that many people are interested to know more about the Ayurvedic dietary system but lack the means to do so. The findings indicate a need to design an AI-based mobile application that presents information about the Ayurvedic dietary system in an actionable way, giving users the ability to track and compare diet with their peers and identify what to consume consciously according to their body type, life-situation & season. Users can scan any food item using their phone’s camera to understand the ayurvedic dietary & nutritional values associated with it. Using an Artificial Intelligence, their app will suggest alternatives and recipes related to that food item based upon the user’s needs like weight loss/gain, immunity improvement, taste preference, etc. This research is interdisciplinary utilizing the knowledge from fields of Ayurveda, Human Food Interaction, Quantified Self, Artificial Intelligence to make young Indians aware of traditional ayurvedic diet system. Lastly, gamification elements have been used to provide intrinsic and extrinsic motivation to the users to follow the diet system.

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APA

Dutta, K., Rajput, A., Srivastava, S., Chidambaram, A., & Srivastava, A. (2022). Design of AI-Enabled Application to Detect Ayurvedic Nutritional Values of Edible Items and Suggest a Diet. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13336 LNAI, pp. 245–256). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-05643-7_16

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