Estimation of user interest using time delay features between proactive content presentation and eye movements

Dodane, Jean-Baptiste; Hirayama, Takatsugu; Kawashima, Hiroyuki; Matsuyama, Takashi

doi:10.1109/acii.2009.5349258

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…However, these are not suitable for use in storytelling. Although Dodane et al estimated a degree of interest from users' eye movements [4], gaze analyzing devices are expensive, and their casual use for events like storytelling is not realistic.…”

Section: Research On Automatic Estimations Of Human Intentionsmentioning

confidence: 99%

“…If the reaction evaluation is performed automatically using sensors, the improvement in educational activities is promoted with this improvement cycle. Conventionally, the automatic estimation of curiosity has been attempted by eyetracking [4] and multi-modal sensing using cameras and microphones [5,6]. However, these require expensive devices or strong restrictions on the location of users/devices.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Curiosity Estimation in Storytelling with Picture Books for Children Using Wearable Sensors

Ohnishi,

Kosaka,

Hama

et al. 2024

Sensors

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Storytelling is one of the most important learning activities for children since reading aloud from a picture book stimulates children’s curiosity, emotional development, and imagination. For effective education, the procedures for storytelling activities need to be improved according to the children’s level of curiosity. However, young children are not able to complete questionnaires, making it difficult to analyze their level of interest. This paper proposes a method to estimate children’s curiosity in picture book reading activities at five levels by recognizing children’s behavior using acceleration and angular velocity sensors placed on their heads. We investigated the relationship between children’s behaviors and their levels of curiosity, listed all observed behaviors, and clarified the behavior for estimating curiosity. Furthermore, we conducted experiments using motion sensors to estimate these behaviors and confirmed that the accuracy of estimating curiosity from sensor data is approximately 72%.

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Section: Research On Automatic Estimations Of Human Intentionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Curiosity Estimation in Storytelling with Picture Books for Children Using Wearable Sensors

Ohnishi,

Kosaka,

Hama

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…However, their results are not directly applied to an interaction in a rapidly changing environment while driving where eye fixations are unusual activities. Estimation of user intention from eye gaze trajectory is done in several studies [4], usually conducted in indoor simulation where estimation of eye gaze is easy. Fig.…”

Section: Related Workmentioning

confidence: 99%

Identification of the Driver's Interest Point using a Head Pose Trajectory for Situated Dialog Systems

Kim

Misu

2014

Proceedings of the 16th International Conference on Multimodal Interaction

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This paper addresses issues existing in situated language understanding in a moving car. Particularly, we propose a method for understanding user queries regarding specific target buildings in their surroundings based on the driver's head pose and speech information. To identify a meaningful head pose motion related to the user query that is among spontaneous motions while driving, we construct a model describing the relationship between sequences of a driver's head pose and the relative direction to an interest point using the Gaussian process regression. We also consider time-varying interest point using kernel density estimation. We collected situated queries from subject drivers by using our research system embedded in a real car. The proposed method achieves an improvement in the target identification rate by 14% in the user-independent training condition and 27% in the user-dependent training condition over the method that uses the head motion at the start-of-speech timing.

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Estimation of user interest using time delay features between proactive content presentation and eye movements

Cited by 2 publications

References 16 publications

A Curiosity Estimation in Storytelling with Picture Books for Children Using Wearable Sensors

A Curiosity Estimation in Storytelling with Picture Books for Children Using Wearable Sensors

Identification of the Driver's Interest Point using a Head Pose Trajectory for Situated Dialog Systems

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