Heart on the road: HRV analysis for monitoring a driver's affective state
Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Affectively intelligent and adaptive car interfaces
Information Sciences: an International Journal
CalmMeNow: exploratory research and design of stress mitigating mobile interventions
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Recording affect in the field: towards methods and metrics for improving ground truth labels
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
AffectAura: an intelligent system for emotional memory
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
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Stress has a wide range of negative impacts on people, ranging from declines in real-time task performance to development of chronic health conditions. Despite the increasing availability of sensors and methods for detecting stress, little work has focused on automated stress interventions and their effect. We present MoodWings: a wearable butterfly that mirrors a user's real-time stress state through actuated wing motion. We designed MoodWings to function both as an early-stress-warning system as well as a physical interface through which users could manipulate their affective state. Accordingly, we hypothesized that MoodWings would help users both calm down and perform better during stressful tasks. We tested our hypotheses on a common stressful task: driving. While users drove significantly more safely with MoodWings, they experienced higher stress levels (physiologically and self-perceived). Despite this, users were enthusiastic about MoodWings, expressing several alternative contexts in which they would find it useful. We discuss these results and future design implications for building externalized manifestations of real-time affective state.