Performance enhancement is key in several domains, e.g. sports, work. Especially in the sports domain, in the past research and training have focused mainly on the physical aspect. In the last years it has become clear that to deliver an optimal performance both physical and mental state need to be on point. It remains however extremely difficult to measure the mental state and it is even more difficult to give advice based on it.
Physiological signals such as heart rate (HR) and skin conductance (SC) have shown to be correlated with stress, emotions and other mental factors. With the use of high-quality and low power wearables, these signals can now be measured continuously in daily life, work and sports settings. By combining these signals with sleep schedules (e.g. to measure recovery), calendars, location information etc. a context-based feedback system could be used to inform athletes and/or employees on whether or not they are ‘in the zone’ of optimal performance and if not, how they can work towards it.
This PhD will focus on the research towards a model that can link physiological signals, mental state and context information to define the point of optimal performance (flow, ‘in the zone’). The main part of the PhD will consist of data collection and data analysis. The outcome should be a model that can advise athletes/employees on their current performance state and actions they can take to reach optimal performance. The PhD will consist of interdisciplinary research, where collaboration with psychologists, sociologists and data scientists is crucial.
Required background: bio-engineering, biomedical engineering, computer science, with background in data science
Type of work: 40% data collection, 60% data analysis
Supervisor: Chris Van Hoof
Daily advisor: Elena Smets
The reference code for this PhD position is SE1712-33. Mention this reference code on your application form.