Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment 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

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Wang,
author = {Wang, R and Amadori, P and Demiris, Y},
publisher = {IEEE},
title = {Real-time workload classification during driving using hyperNetworks},
url = {http://hdl.handle.net/10044/1/62675},
}

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
PB - IEEE
SN - 2153-0866
TI - Real-time workload classification during driving using hyperNetworks
UR - http://hdl.handle.net/10044/1/62675
ER -