SOURCE-LINKED INTELLIGENCE

Skill Performance Estimation from cARdiac Signals

CORDIS · observation · Publication date unknown

nConquest should enable us to tackle this challenge. Indeed, in BrainConquest we explored such a personalized training approach for users of Brain-Computer Interfaces (BCI). In doing so, we developed Machine Learning (ML) and Signal Processing (SP) algorithms to estimate users’ mental states and predict their upcoming performances from their brain and physiological signals, including CS. In SPEARS, we thus aim at adapting, improving and assessing BrainConquest ML & SP algorithms, initially designed for BCI performance prediction from research grade brain and CS sensors in the lab, to predict cognitive and physical performance from consumer grade CS sensors in the wild. Such algorithms could be used for adaptive training apps in education, cognitive training for healthy aging or sport training. We will then explore a commercial application of this technology for sport training in particular, in collaboration with the startup Flit Sport, which sells an app for providing personalized training exercises for endurance sport athletes, based on their past performances and ML. By integrating

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR