Summary of Personalized Weight Loss Management Through Wearable Devices and Artificial Intelligence, by Sergio Romero-tapiador et al.
Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence
by Sergio Romero-Tapiador, Ruben Tolosana, Aythami Morales, Blanca Lacruz-Pleguezuelos, Sofia Bosch Pastor, Laura Judith Marcos-Zambrano, Guadalupe X. Bazán, Gala Freixer, Ruben Vera-Rodriguez, Julian Fierrez, Javier Ortega-Garcia, Isabel Espinosa-Salinas, Enrique Carrillo de Santa Pau
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study uses wearable devices and Artificial Intelligence to predict changes in weight loss among overweight and obese individuals. By analyzing data from a 1-month trial involving over 100 subjects, researchers identify key differences between those who achieve significant weight loss (>= 2% of their initial weight) and those who do not. Feature selection techniques and classification algorithms, such as the Gradient Boosting classifier with an AUC of 84.44%, demonstrate promising results. The integration of multiple data sources, including vital signs, physical activity, and sleep patterns, enhances performance. This suggests the potential for wearable devices and AI in personalized healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wearable devices and Artificial Intelligence can help predict changes in weight loss among overweight and obese people. Researchers studied over 100 people who wore devices for a month to gather data on things like vital signs, physical activity, and sleep patterns. They found that people who lost more than 2% of their initial weight had different readings from those who didn’t lose weight. Using special techniques and computer programs, the researchers were able to predict which people would lose weight with an accuracy rate of about 84%. This suggests that wearable devices and AI could be used to help people in a personalized way. |
Keywords
» Artificial intelligence » Auc » Boosting » Classification » Feature selection
