A multi-view multi-task ML method detects real-world driving-induced affective states using physiological signals by modeling inter-drive variability, with results showing performance gains on three datasets.
A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals
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Detection of Real-world Driving-induced Affective State Using Physiological Signals and Multi-view Multi-task Machine Learning
A multi-view multi-task ML method detects real-world driving-induced affective states using physiological signals by modeling inter-drive variability, with results showing performance gains on three datasets.