Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions
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 impracti...
| Main Authors: | , , , , , , |
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| Format: | Book Section |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/63624/ |
| _version_ | 1848800042553442304 |
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| author | Huang, Zhentao Li, Rongze Jin, Wangkai Song, Zilin Zhang, Yu Peng, Xiangjun Sun, Xu |
| author_facet | Huang, Zhentao Li, Rongze Jin, Wangkai Song, Zilin Zhang, Yu Peng, Xiangjun Sun, Xu |
| author_sort | Huang, Zhentao |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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/. |
| first_indexed | 2025-11-14T20:45:16Z |
| format | Book Section |
| id | nottingham-63624 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:45:16Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-636242020-10-27T02:49:36Z https://eprints.nottingham.ac.uk/63624/ Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions Huang, Zhentao Li, Rongze Jin, Wangkai Song, Zilin Zhang, Yu Peng, Xiangjun Sun, Xu 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/. 2020-09-30 Book Section PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/63624/1/Face2Multi-modal.pdf 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 Human-Vehicle Interactions; Computer Vision; Ergonomics http://dx.doi.org/10.1145/3409251.3411716 10.1145/3409251.3411716 10.1145/3409251.3411716 10.1145/3409251.3411716 |
| spellingShingle | Human-Vehicle Interactions; Computer Vision; Ergonomics Huang, Zhentao Li, Rongze Jin, Wangkai Song, Zilin Zhang, Yu Peng, Xiangjun Sun, Xu Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions |
| title | Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions |
| title_full | Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions |
| title_fullStr | Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions |
| title_full_unstemmed | Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions |
| title_short | Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions |
| title_sort | face2multi-modal: in-vehicle multi-modal predictors via facial expressions |
| topic | Human-Vehicle Interactions; Computer Vision; Ergonomics |
| url | https://eprints.nottingham.ac.uk/63624/ https://eprints.nottingham.ac.uk/63624/ https://eprints.nottingham.ac.uk/63624/ |