Wearable Eye-Tracking System for Synchronized Multimodal Data Acquisition

Eye-tracking technology is extensively utilized in affective computing research, enabling the investigation of emotional responses through the analysis of eye movements. Integration of eye-tracking with other modalities, allows for the collection of multimodal data, leading to a more comprehensive u...

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Published inIEEE transactions on circuits and systems for video technology Vol. 34; no. 6; pp. 5146 - 5159
Main Authors Yang, Minqiang, Gao, Yujie, Tang, Longzhe, Hou, Jian, Hu, Bin
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Abstract Eye-tracking technology is extensively utilized in affective computing research, enabling the investigation of emotional responses through the analysis of eye movements. Integration of eye-tracking with other modalities, allows for the collection of multimodal data, leading to a more comprehensive understanding of emotions and their relationship with physiological responses. This paper presents a novel head-mounted eye-tracking system for multimodal data acquisition with a completely redesigned structure and improved performance. We propose a novel method for pupil-fitting with high efficiency and robustness based on deep learning and RANSAC, which gets better performance of pupil segmentation when it is partially occluded, and build a 3D model to obtain gaze points. Existing eye trackers for multi-modal synchronous data collection either have limited device support or suffer from significant synchronization delays. Our proposed hard real-time synchronization mechanism implements microsecond level latency with low cost, which facilitates multimodal analysis for affective computing research. The uniquely designed exterior effectively reduces facial occlusion, making it more comfortable for the wearer while facilitating the capture of facial expressions.
AbstractList Eye-tracking technology is extensively utilized in affective computing research, enabling the investigation of emotional responses through the analysis of eye movements. Integration of eye-tracking with other modalities, allows for the collection of multimodal data, leading to a more comprehensive understanding of emotions and their relationship with physiological responses. This paper presents a novel head-mounted eye-tracking system for multimodal data acquisition with a completely redesigned structure and improved performance. We propose a novel method for pupil-fitting with high efficiency and robustness based on deep learning and RANSAC, which gets better performance of pupil segmentation when it is partially occluded, and build a 3D model to obtain gaze points. Existing eye trackers for multi-modal synchronous data collection either have limited device support or suffer from significant synchronization delays. Our proposed hard real-time synchronization mechanism implements microsecond level latency with low cost, which facilitates multimodal analysis for affective computing research. The uniquely designed exterior effectively reduces facial occlusion, making it more comfortable for the wearer while facilitating the capture of facial expressions.
Author Gao, Yujie
Hou, Jian
Yang, Minqiang
Tang, Longzhe
Hu, Bin
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Snippet Eye-tracking technology is extensively utilized in affective computing research, enabling the investigation of emotional responses through the analysis of eye...
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SubjectTerms Affective computing
Cameras
Cost analysis
Data acquisition
Data collection
Emotional factors
Eye movements
Gaze tracking
hard real-time synchronization
Occlusion
Physiological responses
Solid modeling
Synchronization
Three dimensional models
Three-dimensional displays
Time synchronization
Tracking
Tracking systems
Wearable devices
Wearable eye tracker
Title Wearable Eye-Tracking System for Synchronized Multimodal Data Acquisition
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