Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence

Sergio Romero-Tapiador, Ruben Tolosana, Aythami Morales, Blanca Lacruz-Pleguezuelos, Sofia Bosch Pastor +8 more
9/13/2024
cs.LG

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Abstract

Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores the application of wearable devices and Artificial Intelligence (AI) in order to predict weight loss changes in overweight and obese individuals. Using wearable data from a 1-month trial involving around 100 subjects from the AI4FoodDB database, including biomarkers, vital signs, and behavioral data, we identify key differences between those achieving weight loss (>= 2% of their initial weight) and those who do not. Feature selection techniques and classification algorithms reveal promising results, with the Gradient Boosting classifier achieving 84.44% Area Under the Curve (AUC). The integration of multiple data sources (e.g., vital signs, physical and sleep activity, etc.) enhances performance, suggesting the potential of wearable devices and AI in personalized healthcare.

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Cite this paper

@article{romerotapiador2024personalized,
  title  = {Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence},
  author = {Sergio Romero-Tapiador and Ruben Tolosana and Aythami Morales and Blanca Lacruz-Pleguezuelos and Sofia Bosch Pastor and Laura Judith Marcos-Zambrano and Guadalupe X. Bazán and Gala Freixer and Ruben Vera-Rodriguez and Julian Fierrez and Javier Ortega-Garcia and Isabel Espinosa-Salinas and Enrique Carrillo de Santa Pau},
  year   = {2024},
  eprint = {2409.08700},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.08700v1}
}

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