Comput Math Methods Med - Hypovigilance detection for UCAV operators based on a hidden Markov model.

Tópicos

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Resumo

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators' dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed.

Resumo Limpo

advanc militari technolog number unman combat aerial vehicl ucav rapid increas howev report accid rate ucav much higher man combat aerial vehicl one main reason high accid rate ucav hypovigil problem refer decreas vigil level ucav oper maneuv paper propos hypovigil detect model ucav oper base eeg signal minim number occurr hypovigil enabl detect appli hidden markov model hmms two use indic oper dual state normal vigil hypovigil oper hmms train detect model evalu efficaci effect propos model conduct two experi realworld data obtain use eegsign acquisit devic yield satisfactori result util propos detect model problem hypovigil ucav oper problem high accid rate ucav can address

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