Landscape Architecture and Environmental Planning Faculty Publications
 

Title

Arousal Detection for Biometric Data in Built Environments using Machine Learning

Document Type

Conference Paper

Journal/Book Title/Conference

1st International Workshop on Affective Computing (AC)

Volume

66

Issue

1

Editor

Neil Lawrence and Mark Reid

Publication Date

8-20-2017

First Page

58

Last Page

72

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.

This document is currently not available here.

Share

COinS