Acquisition of vital parameters and classification of cognitive conditions via machine learning

Konferenz: Sensoren und Messsysteme - 19. ITG/GMA-Fachtagung
26.06.2018 - 27.06.2018 in Nürnberg, Deutschland

Tagungsband: ITG-Fb. 281: Sensoren und Messsysteme

Seiten: 4Sprache: EnglischTyp: PDF

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Bussas, Martin (TROUT GmbH, Parkstr. 28, 34119 Kassel, Germany)

The ability to acquire vital parameters and classify cognitive conditions opens doors to new technologies in diverse areas such as medical technology, automation, aerospace, fitness/wellness and security. TROUT gained considerable expertise in biometric data processing during automotive and medical technology developments which focused on machine learning and AI (Artificial Intelligence). With variations in the heartbeat, the organism can respond optimally to changing endogenous and exogenous influences and thus adapt to the current needs of the blood supply. Heart rate variability (HRV) provides not only information on the degree of stress on the cardiovascular system, but also on the quality of cardiovascular regulation and has also become established in other areas in recent years, due to ever smaller measuring instruments and lower costs, as well as applications in clinical research. If we add information about the activities of the individual in correlation with his vital data and process both data through a machine learning system, we are now able to achieve very good results concerning the individual’s cognitive state such as stress level and fatigue. The system is adjusted by a feed-back loop mapping the individual’s self-estimation about their cognitive state.