Machine learning for healthy lifestyle habits

Maintaining awareness of dietary habits is important for health, but traditional methods, such as manual food journaling, are time-consuming and often inaccurate. Advances in computer vision offer opportunities to automate this process, making camera-based dietary assessment more convenient and reliable.

I am currently developing different tools based on machine learning connected to healthy lifestyles, including dietary habits. Currently, I have run several thesis projects and other student projects on the topic, including:

• Esaias Bevegård and Eugén Bevegård, Automated Dietary Analysis Using Computer Vision and Large Language Models: An iOS Prototype, BSc Thesis, KTH, 2025.
• Jonas Stylbäck, A Step Towards a Multi-Stage, Visual-Based Dietary Assessment Smartphone Application: A Computational Approach to Food Volume Estimation Using Smartphone-Based Augmented Reality, MSc Thesis, KTH, 2025.