Imperial College London
Alternate

Professor Yiannis Demiris

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1011Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Wang:2019:10.1109/IROS.2018.8594305,
author = {Wang, R and Amadori, P and Demiris, Y},
doi = {10.1109/IROS.2018.8594305},
publisher = {IEEE},
title = {Real-time workload classification during driving using hyperNetworks},
url = {http://dx.doi.org/10.1109/IROS.2018.8594305},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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 artifacts. In this work, we propose an end-to-end framework for real-time cognitive workload classification with mixture Hyper Long Short Term Memory Networks (m-HyperLSTM), a novelvariant 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-basedmethods
AU  - Wang,R
AU  - Amadori,P
AU  - Demiris,Y
DO  - 10.1109/IROS.2018.8594305
PB  - IEEE
PY  - 2019///
SN  - 2153-0866
TI  - Real-time workload classification during driving using hyperNetworks
UR  - http://dx.doi.org/10.1109/IROS.2018.8594305
UR  - https://ieeexplore.ieee.org/document/8594305
UR  - http://hdl.handle.net/10044/1/62675
ER  -
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