Evidence map›Paper›PMID 40218607›Full record

ArticleSensors (Basel, Switzerland)2025

Segmentation-Based Blood Blurring: Examining Eye-Response Differences in Gory Video Viewing.

Jiwon Son, Minjeong Cha, Sangkeun Park

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jiwon SonDepartment of Software Convergence, Kyung Hee University, Yongin 17104, Republic of Korea.ORCID 0009-0001-4739-134X
Minjeong ChaDepartment of Software Convergence, Kyung Hee University, Yongin 17104, Republic of Korea.ORCID 0009-0003-9291-5581
Sangkeun ParkDepartment of Software Convergence, Kyung Hee University, Yongin 17104, Republic of Korea.ORCID 0000-0001-8263-5509

Funding

National Research Foundation of Korea No. RS-2023-00253723
6 · The paper itself

Abstract

Online video platforms have enabled unprecedented access to diverse content, but minors and other vulnerable viewers can also be exposed to highly graphic or violent materials. This study addresses the need for a nuanced method of filtering gore by developing a segmentation-based approach that selectively blurs blood. We recruited 37 participants to watch both blurred and unblurred versions of five gory video clips. Eye-based physiological and gaze data, including eye openness ratio, blink frequency, and eye fixations, were recorded via a webcam and eye tracker. Our results demonstrate that partial blood blurring substantially lowers perceived gore in more brutal scenes. Additionally, participants exhibited distinctive physiological reactions when viewing clips with higher gore, such as decreased eye openness and more frequent blinking. Notably, individuals with a stronger fear of blood showed an even greater tendency to blink, suggesting that personal sensitivities shape responses to graphic content. These findings highlight the potential of segmentation-based blurring as a balanced content moderation strategy, reducing distress without fully eliminating narrative details. By allowing users to remain informed while minimizing discomfort, this approach could prove valuable for video streaming services seeking to accommodate diverse viewer preferences and safeguard vulnerable audiences.

Indexed as

BloodVideo RecordingAdultBlinkingEye MovementsFemaleFixation, OcularHumansMaleYoung Adulteye trackinggory videoHCIphysiological responsessegmentation-based blurringviewing experience

Identifiers

PMID40218607
PMCPMC11991354

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.