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Jul 14, 2017 · In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and ...
In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We ...
In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We ...
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This paper proposes an EEG-based multi-dimensional feature selection and fusion method to recognise mental fatigue in drivers.
Mar 9, 2023 · This paper proposes an approach to identify the mental stress of automotive drivers based on selected biosignals, namely, ECG, EMG, GSR, and respiration rate.
To assess a driver's readiness, these systems need to monitor the driver's physical, emotional, and physiological state and communicate relevant information ...
Machine learning methods that have been used for detecting the driver state, e.g., cognitive load include SVM [17][18][19][20][21][22][23][24][25], artificial ...
Dec 5, 2018 · Affectiva Automotive AI leverages deep learning to identify, in real time, complex emotional & cognitive states of a driver in a vehicle.
Missing: Automatic | Show results with:Automatic
Apr 18, 2024 · This paper endeavors to explore an approach to driver emotion recognition with a specific focus on the cognitive and decision layers.
This paper aims to address this research gap by evaluating the mental states of drivers across different age groups and driving experience levels.