Real-Time Workload Classification during Driving using HyperNetworks
This addresses the problem of monitoring driver cognitive states for applications like robotics, though it is incremental with a novel variant of HyperNetworks.
The paper tackled real-time cognitive workload classification from eye-gaze patterns during driving by proposing a mixture Hyper Long Short Term Memory Networks framework, achieving 83.9% precision and 87.8% recall in tests.
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.