Heart rate variability (HRV) is an important sign because it reflects the activity of the autonomic nervous system (ANS), which controls most of the physiological activity of the subjects, including sleep. The balance between the sympathetic and parasympathetic branches of the nervous system is an effective indicator of heart rhythm and, indirectly, heart rhythm is related to a patient’s state of wakefulness or sleep. In this paper we present a research that models a fuzzy logic inference engine for early detection of the onset of sleep in people driving a car or a public transportation vehicle. ANS activity reflected in the HRV signal is measured by electrocardiogram (ECG). Power spectrum density (PSD) is computed from the HRV signal and ANS frequency activity is then measured. Crisp measurements such as very low, low, and high HRV and lowto-high frequency ratio variability are fuzzified and evaluated by a set of fuzzylogic rules that make inferences about the onset of sleep in automobile drivers. An experimental test environment has been developed to evaluate this method and its effectiveness.
Fuzzy-logic inference for early detection of sleep onset in car driver / M. Malcangi, S. Smirne - In: Engineering applications of neural networks : 13th International Conference, EANN 2012 : London, UK, september 20-23, 2012 : proceedings / [a cura di] C. Jayne, S. Yue, L. Iliadis. - Berlin : Springer, 2012. - ISBN 9783642329081. - pp. 41-50 (( Intervento presentato al 13. convegno Engineering Applications of Neural Networks (EANN) tenutosi a London nel 2012 [10.1007/978-3-642-32909-8_5].
Fuzzy-logic inference for early detection of sleep onset in car driver
M. MalcangiPrimo
;S. SmirneUltimo
2012
Abstract
Heart rate variability (HRV) is an important sign because it reflects the activity of the autonomic nervous system (ANS), which controls most of the physiological activity of the subjects, including sleep. The balance between the sympathetic and parasympathetic branches of the nervous system is an effective indicator of heart rhythm and, indirectly, heart rhythm is related to a patient’s state of wakefulness or sleep. In this paper we present a research that models a fuzzy logic inference engine for early detection of the onset of sleep in people driving a car or a public transportation vehicle. ANS activity reflected in the HRV signal is measured by electrocardiogram (ECG). Power spectrum density (PSD) is computed from the HRV signal and ANS frequency activity is then measured. Crisp measurements such as very low, low, and high HRV and lowto-high frequency ratio variability are fuzzified and evaluated by a set of fuzzylogic rules that make inferences about the onset of sleep in automobile drivers. An experimental test environment has been developed to evaluate this method and its effectiveness.Pubblicazioni consigliate
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