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Applying evolutionary methods for early prediction of sleep onset

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Abstract

Driving safety can be achieved by predicting imminent falling asleep at the wheel. Several methods of early detection have been investigated by continuous monitoring of physiological and behavioral parameters. Requirements for noninvasive, unattended, personal adaptation need to be met, along with the effectiveness of the detection method, in order to perform reliably when applied. Because wakefulness and sleep are reflected in several human physiological conditions, such as cardiac activity, breathing, movement, and galvanic skin conductance, captured bioelectric signal features were extracted. A fuzzy decision-fusion logic was tuned to make inferences about oncoming driver fatigue and drowsiness. The evolving fuzzy neural network paradigm was applied to the previous developed framework to improve reliability while keeping target system complexity low.

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  1. Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, 20135, Milan, Italy

    Mario Malcangi

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  1. Mario Malcangi
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Correspondence to Mario Malcangi.

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Malcangi, M. Applying evolutionary methods for early prediction of sleep onset. Neural Comput & Applic 27, 1165–1173 (2016). https://doi.org/10.1007/s00521-015-1928-6

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