Abstract
Preempting mental fatigue may cause decrease in the quality of life and the worst accidents. A system of electrocardiography and electromyography signals can enhance the detection of alertness and mental fatigue. This study determines the suitability of some computational intelligence, namely, artificial neural network (ANN), fuzzy logic system, and a Sugeno adaptive neuro-fuzzy inference system (ANFIS), in detecting mental alertness and fatigue of a person using neurophysiological signals of electrocardiogram (ECG) and electromyogram (EMG) only instead of using higher-dimensional array of physiological data. The usage of these neurophysiological signals was tested if it correlates with high detection rate as to the usual observable physiological parameters. Muscle contraction was also studied in parallel with varying heart rates. Moreover, a power-efficient off-body access network (oBAN) was materialized using Arduino microcontroller with Bluetooth wireless transmission medium. The system is composed of two major parts: the development of BAN and the implementation of soft algorithms. The data set was extracted from 20 university students of differing ages, genders, and sleep hours. Provided with the same training set, the system detection accuracy for ANN, FIS, and ANFIS is 97.800%, 99.529%, and 99.604%, respectively. An identical testing set was also employed to ANN, FIS, and ANFIS, yielding 71.000%, 99.553%, and 99.556% detection accuracy. Hence, with this physiological data set and purposive classification, ANFIS provides the paramount accuracy.
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Acknowledgments
This project is supported by the Engineering Research and Development Technology program of the Department of Science and Technology and the Department of Electronics Engineering of the University of Perpetual Help System DALTA, Las Piñas Campus.
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Concepcion, R.S. et al. (2020). Alertness and Mental Fatigue Classification Using Computational Intelligence in an Electrocardiography and Electromyography System with Off-Body Area Network. In: Beltran Jr., A., Lontoc, Z., Conde, B., Serfa Juan, R., Dizon, J. (eds) World Congress on Engineering and Technology; Innovation and its Sustainability 2018. WCETIS 2018. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-20904-9_12
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DOI: https://doi.org/10.1007/978-3-030-20904-9_12
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