Wearable computing technologies for analysing the social interactions of people with depression

The design and development of wearable sensor systems for health and wellbeing monitoring has gained increasing attention in both the scientific community and practitioners' care routines. Wearable health and wellbeing sensors have recently advanced, making it easier to monitor and analyse human activities, with applications to, for example, mental care and psychological support. Beyond that, many clinical conditions are characterized by their behavioural phenotypes, which requires expert monitoring and analysis for diagnosis and tailored treatment programs.

There is a clear consensus that the current clinical practice of - typically rather sporadic - clinical behaviour assessments, often administered in artificial settings, does not provide a realistic impression of a patient’s condition and thus does not lead to sufficient assessments and care. Wearable behaviour monitors, however, have the potential for continuous, objective assessment of behaviour thereby allowing for monitoring in naturalistic environments, and thus for improving both diagnosis and treatment. In response to this, this project aims at monitoring and assessing the different aspects of psycho-social wellbeing with focus on naturalistic setting environments for monitoring. These aspects is represented by the social interaction analysis which includes the verbal interactions (acoustic communications) and physical activities (movement patterns).