@inproceedings{bethge2023interpretable,
author = {Bethge, David and Patsch, Constantin and Hallgarten, Philipp and Kosch, Thomas},
title = {Interpretable Time-Dependent Convolutional Emotion Recognition with Contextual Data Streams},
year = {2023},
isbn = {9781450394222},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544549.3585672},
doi = {10.1145/3544549.3585672},
abstract = {Emotion prediction is important when interacting with computers. However, emotions are complex, difficult to assess, understand, and hard to classify. Current emotion classification strategies skip why a specific emotion was predicted, complicating the user’s understanding of affective and empathic interface behaviors. Advances in deep learning showed that convolutional networks can learn powerful time-series patterns while showing classification decisions and feature importances. We present a novel convolution-based model that classifies emotions robustly. Our model not only offers high emotion-prediction performance but also enables transparency on the model decisions. Our solution thereby provides a time-aware feature interpretation of classification decisions using saliency maps. We evaluate the system on a contextual, real-world driving dataset involving twelve participants. Our model achieves a mean accuracy of in 5-class emotion classification on unknown roads and outperforms in-car facial expression recognition by . We conclude how emotion prediction can be improved by incorporating emotion sensing into interactive computing systems.},
booktitle = {Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems},
articleno = {186},
numpages = {9},
keywords = {Affective Computing, Emotion Classification, Explainable AI, Time-Series Classification},
location = {Hamburg, Germany},
series = {CHI EA '23}
}