Physiologically Driven Storytelling: Concept and Software Tool
March 20, 2020 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
JΓ©rΓ©my Frey, Gilad Ostrin, May Grabli, Jessica Cauchard
arXiv ID
2003.09333
Category
cs.HC: Human-Computer Interaction
Citations
13
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
We put forth Physiologically Driven Storytelling, a new approach to interactive storytelling where narratives adaptively unfold based on the reader's physiological state. We first describe a taxonomy framing how physiological signals can be used to drive interactive systems both as input and output. We then propose applications to interactive storytelling and describe the implementation of a software tool to create Physiological Interactive Fiction (PIF). The results of an online study (N=140) provided guidelines towards augmenting the reading experience. PIF was then evaluated in a lab study (N=14) to determine how physiological signals can be used to infer a reader's state. Our results show that breathing, electrodermal activity, and eye tracking can help differentiate positive from negative tones, and monotonous from exciting events. This work demonstrates how PIF can support storytelling in creating engaging content and experience tailored to the reader. Moreover, it opens the space to future physiologically driven systems within broader application areas.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted