We investigate personalised feedback mechanisms to help drivers regulate their emotions, aiming to improve road safety. We systematically evaluate driver-preferred feedback modalities and their impact on emotional states. Using unobtrusive vision-based emotion detection and self-labeling, we captured the emotional states and feedback preferences of 21 participants in a simulated driving environment. Results show that in-car feedback systems effectively influence drivers’ emotional states, with participants reporting positive experiences and varying preferences based on their emotions. We also developed a machine learning classification system using facial marker data to demonstrate the feasibility of our approach for classifying emotional states. Our contributions include design guidelines for tailored feedback systems, a systematic analysis of user reactions across three feedback channels with variations, an emotion classification system, and a dataset with labeled face landmark annotations for future research.
%0 Journal Article
%1 mti8070054
%A Mwaita, Kevin Fred
%A Bhaumik, Rahul
%A Ahmed, Aftab
%A Sharma, Adwait
%A De Angeli, Antonella
%A Haller, Michael
%D 2024
%J Multimodal Technologies and Interaction
%K audio emotion feedback in-car vibrotactile visual
%N 7
%R 10.3390/mti8070054
%T Emotion-Aware In-Car Feedback: A Comparative Study
%U https://www.mdpi.com/2414-4088/8/7/54
%V 8
%X We investigate personalised feedback mechanisms to help drivers regulate their emotions, aiming to improve road safety. We systematically evaluate driver-preferred feedback modalities and their impact on emotional states. Using unobtrusive vision-based emotion detection and self-labeling, we captured the emotional states and feedback preferences of 21 participants in a simulated driving environment. Results show that in-car feedback systems effectively influence drivers’ emotional states, with participants reporting positive experiences and varying preferences based on their emotions. We also developed a machine learning classification system using facial marker data to demonstrate the feasibility of our approach for classifying emotional states. Our contributions include design guidelines for tailored feedback systems, a systematic analysis of user reactions across three feedback channels with variations, an emotion classification system, and a dataset with labeled face landmark annotations for future research.
@article{mti8070054,
abstract = {We investigate personalised feedback mechanisms to help drivers regulate their emotions, aiming to improve road safety. We systematically evaluate driver-preferred feedback modalities and their impact on emotional states. Using unobtrusive vision-based emotion detection and self-labeling, we captured the emotional states and feedback preferences of 21 participants in a simulated driving environment. Results show that in-car feedback systems effectively influence drivers’ emotional states, with participants reporting positive experiences and varying preferences based on their emotions. We also developed a machine learning classification system using facial marker data to demonstrate the feasibility of our approach for classifying emotional states. Our contributions include design guidelines for tailored feedback systems, a systematic analysis of user reactions across three feedback channels with variations, an emotion classification system, and a dataset with labeled face landmark annotations for future research.},
added-at = {2024-06-26T19:14:11.000+0200},
article-number = {54},
author = {Mwaita, Kevin Fred and Bhaumik, Rahul and Ahmed, Aftab and Sharma, Adwait and De Angeli, Antonella and Haller, Michael},
biburl = {https://www.bibsonomy.org/bibtex/2fa3c9e55d7b124dbd941be258abd0804/mh4ller},
doi = {10.3390/mti8070054},
interhash = {8b92d3cb02e321e196b003295a9cdae5},
intrahash = {fa3c9e55d7b124dbd941be258abd0804},
issn = {2414-4088},
journal = {Multimodal Technologies and Interaction},
keywords = {audio emotion feedback in-car vibrotactile visual},
number = 7,
timestamp = {2024-06-26T19:14:11.000+0200},
title = {Emotion-Aware In-Car Feedback: A Comparative Study},
url = {https://www.mdpi.com/2414-4088/8/7/54},
volume = 8,
year = 2024
}