Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence
It addresses personalized weight loss management for overweight individuals, but is incremental as it applies existing AI methods to new wearable data.
This study used wearable device data from 100 subjects to predict weight loss in overweight and obese individuals, achieving an 84.44% AUC with a Gradient Boosting classifier.
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.