Design and analysis of signal processing algorithms for mental load estimation from heart rate

OData support
Supervisor:
Dr. Forstner Bertalan
Department of Automation and Applied Informatics

The examination of methods for measuring and processing of physiological data has been a rich field of study. The interpretation and usage of this kind of data along with the creation of models for analysis do not only exist in the medical field, but they can also contribute to the estimation regarding the examined person’s concentration and mental workload.

While examining the ECG signals, a correlation between the mental state and the heart rate variability can be observed and this can vary for certain frequency bands. There are various methods for the analysis and transformation of the raw signals.

Nowadays, these experiments are also feasible using portable, easily usable devices. If the people under examination are doing a task, it is possible to relate to the difficulty that the task causes for them. In certain cases, even the applicability of softwares can be seen, which can help development and improvement. This information can also be used for educational purposes.

The usage and comparison of certain algorithms and the determination of the circumstances under which they can be used gives a more complex background for finding the correlation between the variation of ECG singals and the reasons behind them.

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