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.
@article{arxiv.1810.03145,
title = {Real-Time Workload Classification during Driving using HyperNetworks},
author = {Ruohan Wang and Pierluigi V. Amadori and Yiannis Demiris},
journal= {arXiv preprint arXiv:1810.03145},
year = {2018}
}
Comments
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)