# Real-Time Workload Classification during Driving using HyperNetworks

arXiv: https://arxiv.org/abs/1810.03145 · HTML: https://mlsanity.com/paper/1810.03145/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Gaze Training by Modulated Dropout Improves Imitation Learning](https://mlsanity.com/paper/1904.08377/) (2019)
- [Behavioral Research and Practical Models of Drivers' Attention](https://mlsanity.com/paper/2104.05677/) (2021)
- [Meta-Learning over Time for Destination Prediction Tasks](https://mlsanity.com/paper/2206.14801/) (2022)
- [Continuous Non-Invasive Eye Tracking In Intensive Care](https://mlsanity.com/paper/2108.01439/) (2021)

## Abstract (arXiv)

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.