Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions

Huang, Zhentao, Li, Rongze, Jin, Wangkai, Song, Zilin, Zhang, Yu, Peng, Xiangjun and Sun, Xu (2020) Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions. In: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM . UNSPECIFIED, pp. 30-33. ISBN 9781450380669

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution.
Download (1MB) | Preview


Towards intelligent Human-Vehicle Interaction systems and innovative Human-Vehicle Interaction designs, in-vehicle drivers' physiological data has been explored as an essential data source. However, equipping multiple biosensors is considered the limited extent of user-friendliness and impractical during the driving procedure. The lack of a proper approach to access physiological data has hindered wider applications of advanced biosignal-driven designs in practice (e.g. monitoring systems and etc.). Hence, the demand for a user-friendly approach to measuring drivers' body statuses has become more intense. In this Work-In-Progress, we present Face2Multi-modal, an In-vehicle multi-modal Data Streams Predictors through facial expressions only. More specifically, we have explored the estimations of Heart Rate, Skin Conductance, and Vehicle Speed of the drivers. We believe Face2Multi-modal provides a user-friendly alternative to acquiring drivers' physiological status and vehicle status, which could serve as the building block for many current or future personalized Human-Vehicle Interaction designs. More details and updates about the project Face2Multi-modal is online at https://github.com/unnc-ucc/Face2Multimodal/.

Item Type: Book Section
Additional Information: Date of acceptance estimated
Keywords: Human-Vehicle Interactions; Computer Vision; Ergonomics
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1145/3409251.3411716
Depositing User: Yu, Tiffany
Date Deposited: 27 Oct 2020 02:49
Last Modified: 27 Oct 2020 02:49
URI: https://eprints.nottingham.ac.uk/id/eprint/63624

Actions (Archive Staff Only)

Edit View Edit View