Automated Eating Activity Tracking System for Anorexia Nervosa Using AI and Wearable Sensors

The PhD researcher will be embedded within the EAST-STADIUS Division of KU Leuven, specifically in the eMedia Research Lab, and will work in close collaboration with the Mind-Body Research Group within the Division of Psychiatry, Department of Neurosciences. The eMedia Research Lab is an interdisciplinary research environment focusing on the development and application of intelligent technologies for human-centered applications, with expertise in artificial intelligence, machine learning, multimodal data analysis, and digital health. The lab investigates how advanced computational methods and sensing technologies can be leveraged to understand human behavior, support healthcare innovation, and develop personalized solutions for complex societal and medical challenges. The Mind-Body Research Group, embedded within the Division of Psychiatry of the Department of Neurosciences, conducts clinical and translational research focusing on the interaction between psychological, behavioral, and physiological processes in mental health. The group has extensive expertise in eating disorders, clinical assessment, longitudinal monitoring, and the development of novel approaches to improve diagnosis, treatment evaluation, and personalized care. This PhD project is positioned at the intersection of artificial intelligence, wearable sensing, and clinical neuroscience, bringing together complementary expertise from engineering and healthcare. Through close collaboration between the eMedia Research Lab and the Mind-Body Research Group, the researcher will have access to advanced technological infrastructure, clinical expertise, and opportunities for meaningful translation of AI-based methods into real-world healthcare applications. The interdisciplinary setting provides an excellent environment for developing innovative digital biomarkers and advancing objective, continuous monitoring approaches for eating disorders.

Project

Anorexia nervosa (AN) is one of the most severe and persistent mental health disorders, characterized by profound disturbances in eating behavior and food-related cognition. Behavioral symptoms such as restrictive eating, meal avoidance, rigid eating patterns, prolonged meal duration, and excessive control over food intake are central features of the disorder and are closely linked to illness severity, treatment response, and relapse risk. Despite the critical role of eating behavior in AN, current clinical assessment remains largely dependent on self-report measures, retrospective questionnaires, food diaries, and clinical interviews. Although these approaches provide valuable insights into patients’ experiences, they are limited by recall bias, social desirability effects, and the considerable cognitive and emotional burden they place on individuals with eating disorders. Importantly, they provide only intermittent snapshots of eating behavior and do not capture how patients actually eat in their daily lives. Recent advances in artificial intelligence (AI) and wearable sensing technologies create new opportunities for objective, continuous, and ecologically valid assessment of eating behavior. Wrist-worn inertial measurement unit (IMU) sensors offer a promising approach to unobtrusively monitor hand-to-mouth movements associated with eating without the privacy concerns, stigma, or practical limitations associated with camera
- or audio-based monitoring systems. However, existing wearable-based eating detection approaches have primarily been developed in healthy populations and controlled environments, with a strong focus on classification accuracy rather than clinical applicability, uncertainty estimation, and behavioral interpretability. This PhD project aims to address this critical gap by developing an AI-enhanced wearable system for automated tracking and characterization of eating behavior in individuals with AN. The project will combine wearable sensing, advanced signal processing, machine learning, and longitudinal behavioral analysis to establish clinically meaningful digital biomarkers of eating behavior. These biomarkers will quantify fine-grained characteristics of eating patterns, including eating rate, temporal organization, behavioral rigidity, variability, and changes over time. By integrating technology development with clinical expertise, this project seeks to enable objective monitoring of eating behavior in real-world settings and provide new tools for early identification of behavioral deterioration, treatment response, and recovery trajectories in AN.

As a PhD Researcher, You Will

• Design, optimize, and validate data acquisition protocols for wearable-based eating behavior monitoring.
• Develop robust signal processing and machine learning pipelines for extracting eating-related behavioral patterns from IMU sensor data.
• Develop interpretable digital biomarkers that capture key micro-structural properties of eating behavior in AN, inclu