Arousal Detection for Biometric Data in Built Environments using Machine Learning
1st International Workshop on Affective Computing (AC)
Neil Lawrence and Mark Reid
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.
Yates, Heath; Chamberlin, Brent C.; Norman, Greg; and Hsu, William H., "Arousal Detection for Biometric Data in Built Environments using Machine Learning" (2017). Landscape Architecture and Environmental Planning Faculty Publications. Paper 153.