An explainable deep fusion network for affect recognition using physiological signals

Lin, J; Pan, S; Lee, CS; Oviatt, S

An explainable deep fusion network for affect recognition using physiological signals

Lin, J Pan, S

Lee, CS Oviatt, S

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2019, pp. 2069-2072
Issue Date:: 2019-11-03

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Field	Value	Language
dc.contributor.author	Lin, J
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X
dc.contributor.author	Lee, CS
dc.contributor.author	Oviatt, S
dc.date.accessioned	2020-06-19T05:55:35Z
dc.date.available	2020-06-19T05:55:35Z
dc.date.issued	2019-11-03
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2019, pp. 2069-2072
dc.identifier.isbn	9781450369763
dc.identifier.uri	http://hdl.handle.net/10453/141564
dc.description.abstract	© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Affective computing is an emerging research area which provides insights on human's mental state through human-machine interaction. During the interaction process, bio-signal analysis is essential to detect human affective changes. Currently, machine learning methods to analyse bio-signals are the state of the art to detect the affective states, but most empirical works mainly deploy traditional machine learning methods rather than deep learning models due to the need for explainability. In this paper, we propose a deep learning model to process multimodal-multisensory bio-signals for affect recognition. It supports batch training for different sampling rate signals at the same time, and our results show significant improvement compared to the state of the art. Furthermore, the results are interpreted at the sensor- and signal- level to improve the explainaibility of our deep learning model.
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings
dc.relation.ispartof	CIKM '19: The 28th ACM International Conference on Information and Knowledge Management
dc.relation.isbasedon	10.1145/3357384.3358160
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	An explainable deep fusion network for affect recognition using physiological signals
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-06-19T05:55:28Z
pubs.publication-status	Published

Abstract:

© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Affective computing is an emerging research area which provides insights on human's mental state through human-machine interaction. During the interaction process, bio-signal analysis is essential to detect human affective changes. Currently, machine learning methods to analyse bio-signals are the state of the art to detect the affective states, but most empirical works mainly deploy traditional machine learning methods rather than deep learning models due to the need for explainability. In this paper, we propose a deep learning model to process multimodal-multisensory bio-signals for affect recognition. It supports batch training for different sampling rate signals at the same time, and our results show significant improvement compared to the state of the art. Furthermore, the results are interpreted at the sensor- and signal- level to improve the explainaibility of our deep learning model.

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http://hdl.handle.net/10453/141564