Arousal Detection for Biometric Data in Built Environments using Machine Learning

Heath Yates, Brent Chamberlain, Greg Norman, William H. Hsu
Proceedings of IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing, PMLR 66:58-72, 2017.

Abstract

This paper describes an approach using wearables to demonstrate the viability of measuring physiometric arousal indicators such as heart rate in assessing how urban built environments can induce physiometric arousal indicators in a subject. In addition, a machine learning methodology is developed to classify sensor inputs based on annotated arousal output as a target. The results are then used as a foundation for designing and implementing an affective intelligent systems framework for arousal state detection via supervised learning and classification.

Cite this Paper

BibTeX
@InProceedings{pmlr-v66-yates17a, title = {Arousal Detection for Biometric Data in Built Environments using Machine Learning}, author = {Yates, Heath and Chamberlain, Brent and Norman, Greg and Hsu, William H.}, booktitle = {Proceedings of IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing}, pages = {58--72}, year = {2017}, editor = {Lawrence, Neil and Reid, Mark}, volume = {66}, series = {Proceedings of Machine Learning Research}, month = {20 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v66/yates17a/yates17a.pdf}, url = {https://proceedings.mlr.press/v66/yates17a.html}, abstract = {This paper describes an approach using wearables to demonstrate the viability of measuring physiometric arousal indicators such as heart rate in assessing how urban built environments can induce physiometric arousal indicators in a subject. In addition, a machine learning methodology is developed to classify sensor inputs based on annotated arousal output as a target. The results are then used as a foundation for designing and implementing an affective intelligent systems framework for arousal state detection via supervised learning and classification.} }
Endnote
%0 Conference Paper %T Arousal Detection for Biometric Data in Built Environments using Machine Learning %A Heath Yates %A Brent Chamberlain %A Greg Norman %A William H. Hsu %B Proceedings of IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing %C Proceedings of Machine Learning Research %D 2017 %E Neil Lawrence %E Mark Reid %F pmlr-v66-yates17a %I PMLR %P 58--72 %U https://proceedings.mlr.press/v66/yates17a.html %V 66 %X This paper describes an approach using wearables to demonstrate the viability of measuring physiometric arousal indicators such as heart rate in assessing how urban built environments can induce physiometric arousal indicators in a subject. In addition, a machine learning methodology is developed to classify sensor inputs based on annotated arousal output as a target. The results are then used as a foundation for designing and implementing an affective intelligent systems framework for arousal state detection via supervised learning and classification.
APA
Yates, H., Chamberlain, B., Norman, G. & Hsu, W.H.. (2017). Arousal Detection for Biometric Data in Built Environments using Machine Learning. Proceedings of IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing, in Proceedings of Machine Learning Research 66:58-72 Available from https://proceedings.mlr.press/v66/yates17a.html.

Related Material