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Lilian Strobl


Raum: P.08.23
E-Mail: lstrobl1991{at}


Oct 13 – present

Bergische Universität Wuppertal

Schumpeter School of Business and Economics


Prof. Dr. Jarek Krajewski

Experimental Industrial Psychology

PhD Student

2012 – 2013

Queen Mary, University of London

Master of Science (M.Sc.) Mental Health: Psychological Therapies

Matriculation: December 2013 Graduation with Merit (Dissertation: Distinction)


University of Southampton, United Kingdom

Bachelor of Science (B.Sc) Psychology

1993 – 2009

Vienna International School

International Baccalaureate (IB)



Titel und Abstract des Dissertationsprojektes


GAD Screening using Multi-Sensory Smartphone data

Anti-anxiety aims for an advanced system for analysis to be developed that is able to detect and estimate user’s cognitive, affective, motivational, energetic, and behavioral states associated with anxiety through the process of information being extracted through a smart phone application equipped with multiple sensor modalities. The ultimate goal of this project is to capture the most important symptoms of Generalized Anxiety Disorder without clients using the app being interrupted or influenced through any of their daily tasks/ activities. Such symptoms include feelings of restlessness or feeling keyed up or on edge, being easily fatigued, difficulty concentrating or mind going blank, irritability, muscle tension or sleep disturbance. Through the use of innovative pattern recognition techniques that will be implemented on the smart phone application, this system will automatically allow for behavioural assessment to be provided. After this assessment, a data visualization software that will also be developed within this project, will be able to analyse all collected data through innovative analysis through medical personell. This application will provide general insight for conceptualizing and developing pattern analysis, classification and visualization algorithms for any application in the area of behavioural biometry implemented on such devices. The anxiety levels of the patient and smart-phone user will be measured over a long period of time through the use of high resolution sensors. These will consist of a Hardware sensor (detecting motion, pedometer measures, facial expression detection, phonetic voice analysis, video-based heart rate detection, sleep disturbance, touchpad control behavior) and a soft sensor (looks at social network activity, facebook profiles of users). Without disturbing the daily activities of the users, anxiety related information and behaviors will be recorded. Crucial for the avoidance of uncertainty in anxiety recognition is the linking of user behavior with context information. This derived from the smart sensor system context information including knowledge about the location, time, weekday and persons setting. For example, expression rules of social roles enforce behavior conformity that suppresses the manifestation of anxious symptoms. Only when familiar or anonymous contexts exist, these symptoms can be observed in such a modified course, the contents of an email, the vocal expression or an anxious face. Machine learning and pattern recognition method eventually allow the identification of anxiety multimodal recognition. Therapeutic interventions can thus be targeted and precisely adapted to the changed condition of the patient.




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