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      Real-Time Workload Classification during Driving using HyperNetworks

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          Abstract

          Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics. The problem is challenging due to the data variability among individual users, and sensor artefacts. In this work, we propose an end-to-end framework for real-time cognitive workload classification with mixture Hyper Long Short Term Memory Networks, a novel variant of HyperNetworks. Evaluating the proposed approach on an eye-gaze pattern dataset collected from simulated driving scenarios of different cognitive demands, we show that the proposed framework outperforms previous baseline methods and achieves 83.9\% precision and 87.8\% recall during test. We also demonstrate the merit of our proposed architecture by showing improved performance over other LSTM-based methods.

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          Most cited references 7

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          Detecting Stress During Real-World Driving Tasks Using Physiological Sensors

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            Anticipating Human Activities Using Object Affordances for Reactive Robotic Response

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              Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines

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                Author and article information

                Journal
                07 October 2018
                Article
                1810.03145

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)
                cs.HC cs.LG stat.ML

                Machine learning, Artificial intelligence, Human-computer-interaction

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