9.1 - Telemetric acquisition of vitality parameters and classification of cognitive condition via machine learning
- ettc2018 - European Test and Telemetry Conference
2018-06-26 - 2018-06-28
- 9. Data Acquisition
- M. Bussas - Trout GmbH, Kassel (Germany)
- 179 - 184
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 an individual in correlation with their 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 of their cognitive state.