Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence
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arXiv
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| Format: | Preprint |
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2024
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| author | Romero-Tapiador, Sergio Tolosana, Ruben Morales, Aythami Lacruz-Pleguezuelos, Blanca Pastor, Sofia Bosch Marcos-Zambrano, Laura Judith Bazán, Guadalupe X. Freixer, Gala Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Espinosa-Salinas, Isabel Pau, Enrique Carrillo de Santa |
| author_facet | Romero-Tapiador, Sergio Tolosana, Ruben Morales, Aythami Lacruz-Pleguezuelos, Blanca Pastor, Sofia Bosch Marcos-Zambrano, Laura Judith Bazán, Guadalupe X. Freixer, Gala Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Espinosa-Salinas, Isabel Pau, Enrique Carrillo de Santa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence Romero-Tapiador, Sergio Tolosana, Ruben Morales, Aythami Lacruz-Pleguezuelos, Blanca Pastor, Sofia Bosch Marcos-Zambrano, Laura Judith Bazán, Guadalupe X. Freixer, Gala Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Espinosa-Salinas, Isabel Pau, Enrique Carrillo de Santa Machine Learning 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. |
| title | Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.08700 |