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

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

KU Leuven is an autonomous university. It was founded in 1425. It was born of and has grown within the Catholic tradition.

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.
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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 eatin