Abstract
This paper deals with the various physiological parameters like ECG, EEG, PP and PPG that show deviation from normal values when a person is under stress. Electrocardiography (ECG) is the process of capturing the electrical activity of the heart for a period of time using electrical conductors placed over the skin. ECG waveform tells us about the electrical activity of the heart. Electroencephalography (EEG) is the process of capturing electrical activity of the brain. EEG measures changes in voltage fluctuations resulting from ionic current present inside the neurons of the brain from the scalp and the difference between systolic blood pressures also known as pulse pressure (PP). The stress can be of many types. These signals are monitored in order to comment on the overall stress life of humans. Real-time biofeedback may help us to understand an individual’s progression towards acute stress-induced performance decrement. We have recorded and analysed data of four prominent signals from the frontal region of brain to understand the brain activity, and we have also learnt its presentation on various time schedules. Participants are connected to various electrodes. The signals will be taken at various times. For example, the first set of signals will be captured in the morning when the person is fresh and the next set of readings will be taken in the evening after the completion of the day. Towards the end of the day, the person is mentally tired and hence the two signal sets will show the required deviations. In this project, we intend to capture biomedical signal of human and use two techniques for signal conditioning. After data acquisition, MATLAB and Arduino programming will help us in proper analysis of workload.
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Acknowledgements
The entire database made for the project purpose was recorded in Cummins College. The paper titled “Workload Assessment Based on Physiological Parameters” is an outcome of guidance and moral support bestowed on us throughout the project tenure, and for this we would like to acknowledge and express our profound sense of gratitude to our guide Dr. Revathi Shriram for her constant motivation.
We would also like to thank everybody in our Instrumentation and Control Department who have indirectly guided and helped us in completing this final-year project.
Last but not least, we would also like to thank all the people/subjects who volunteered and cooperated with us and helped us in collecting the database and results for our project.
All the subjects were willing volunteers who participated in the data collection, and they have given verbal consent for using the data for research purpose and for further publication (i.e. all the three signals ECG, EEG and PPG recorded and analysed). None of the ethical committee is involved in it.
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Dendage, T., Deoskar, V., Kulkarni, P., Shriram, R., Bhat, M. (2020). Workload Assessment Based on Physiological Parameters. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_54
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