# Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence

arXiv: https://arxiv.org/abs/2409.08700 · HTML: https://mlsanity.com/paper/2409.08700/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations](https://mlsanity.com/paper/2305.19962/) (2023)
- [An Overview of Privacy-enhancing Technologies in Biometric Recognition](https://mlsanity.com/paper/2206.10465/) (2022)
- [Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device](https://mlsanity.com/paper/1711.00629/) (2017)
- [Posttraumatic Stress Disorder Hyperarousal Event Detection Using Smartwatch Physiological and Activity Data](https://mlsanity.com/paper/2109.14743/) (2021)
- [Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy](https://mlsanity.com/paper/2211.07440/) (2022)
- [AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours](https://mlsanity.com/paper/2309.06308/) (2023)
- [ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection](https://mlsanity.com/paper/2204.04236/) (2022)
- [Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing](https://mlsanity.com/paper/2303.14745/) (2023)

## Built on by (most cited first, 6 of 6)

- [Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognition](https://mlsanity.com/paper/2504.06925/) (2025)
- [Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond](https://mlsanity.com/paper/2607.02197/) (2026)
- [Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering](https://mlsanity.com/paper/2607.07179/) (2026)
- [Mobile Interaction for Assessing Fatigue, Sleep, and Activity in Neurodegenerative and Chronic Diseases](https://mlsanity.com/paper/2608.06380/) (2026)
- [VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running](https://mlsanity.com/paper/2608.19480/) (2026)
- [inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs](https://mlsanity.com/paper/2610.18051/) (2026)

## Abstract (arXiv)

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.