Explore our research. Biometrics. Fatigue. Drowsiness. Cardiac Status. Location and Monitoring in Real Time.
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With CardioID’s patented technology, we provide Heartmetrics Expertise for the benefit of the individual, in any situation. New research and development, looking to understand human biosignals, guides our design of innovative products that take into account human capabilities, limitations, and characteristics.
The ECG can be used for person identification, enabling systems with more secure procedures and personalized information.
ECG signals provide intrinsic aliveness detection and are continuously available, which are highly desirable properties in biometrics.
Differences in the shape of the ECG signal from person to person are visible to the naked eye, but it takes our expertise in signal processing and machine learning to make it a fully-working automated system. Our ECG biometrics technology is patented under number WO2013109154A1.
CardioID’s experience has been incremental, particularly due to the company’s participation in the Valu3s Project – Verification and Validation of Automated Systems’ Safety and Security, in partnership with 23 industrial companies, 16 research centers, under the ECSEL-JU Program of the Union European.
The company’s activities under Valu3s Project have the following scope:
– Fault and attack injection into simulation models using driving scenarios libraries in combination with fault and attack libraries
– The WSN Intrusion Detection System will detect anomalies in transmissions (e.g. jamming attempts, replay attacks etc.) and data inconsistencies in the currently running application (e.g. injection of malformed and/or malicious data) to prevent faults ahead of time and to notify those events for further analysis/reactions.
– Fault injection in hardware platform and its propagation to other layers. Relate the fault injection on the low-level hardware layer to potential faults on the higher layers to reduce test space.
– Application of pre-injection analysis techniques to reduce the fault and attack space.
– Improvement of fault and attack modelling suitable for evaluation of AI and machine learning based systems, e.g. using domain specific languages.
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