Binary Classification of Arousal in Built Environments using Machine Learning

Heath Yates, Brent Chamberlain, William Hsu
Proceedings of IJCAI 2018 2nd Workshop on Artificial Intelligence in Affective Computing, PMLR 86:35-51, 2020.

Abstract

The goal of this paper is to develop a methodology and model to classify and characterize the arousal state of participants in a built environment. Demonstrating this showcases the potential of developing an intelligent system capable of both classifying and predicting biometric arousal state. This classification process is traditionally performed by human experts. Our approach can be leveraged to take advantage of the diversity of real-time sensor data to inform the development of smart(er) environments to improve human health.

Cite this Paper

BibTeX
@InProceedings{pmlr-v86-yates20a, title = {Binary Classification of Arousal in Built Environments using Machine Learning}, author = {Yates, Heath and Chamberlain, Brent and Hsu, William}, booktitle = {Proceedings of IJCAI 2018 2nd Workshop on Artificial Intelligence in Affective Computing}, pages = {35--51}, year = {2020}, editor = {Hsu, William and Yates, Heath}, volume = {86}, series = {Proceedings of Machine Learning Research}, month = {15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v86/yates20a/yates20a.pdf}, url = {http://proceedings.mlr.press/v86/yates20a.html}, abstract = {The goal of this paper is to develop a methodology and model to classify and characterize the arousal state of participants in a built environment. Demonstrating this showcases the potential of developing an intelligent system capable of both classifying and predicting biometric arousal state. This classification process is traditionally performed by human experts. Our approach can be leveraged to take advantage of the diversity of real-time sensor data to inform the development of smart(er) environments to improve human health.} }
Endnote
%0 Conference Paper %T Binary Classification of Arousal in Built Environments using Machine Learning %A Heath Yates %A Brent Chamberlain %A William Hsu %B Proceedings of IJCAI 2018 2nd Workshop on Artificial Intelligence in Affective Computing %C Proceedings of Machine Learning Research %D 2020 %E William Hsu %E Heath Yates %F pmlr-v86-yates20a %I PMLR %P 35--51 %U http://proceedings.mlr.press/v86/yates20a.html %V 86 %X The goal of this paper is to develop a methodology and model to classify and characterize the arousal state of participants in a built environment. Demonstrating this showcases the potential of developing an intelligent system capable of both classifying and predicting biometric arousal state. This classification process is traditionally performed by human experts. Our approach can be leveraged to take advantage of the diversity of real-time sensor data to inform the development of smart(er) environments to improve human health.
APA
Yates, H., Chamberlain, B. & Hsu, W.. (2020). Binary Classification of Arousal in Built Environments using Machine Learning. Proceedings of IJCAI 2018 2nd Workshop on Artificial Intelligence in Affective Computing, in Proceedings of Machine Learning Research 86:35-51 Available from http://proceedings.mlr.press/v86/yates20a.html.

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