ArticleScientific reports2025
Real-time fear emotion recognition in mice based on multimodal data fusion.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Research on improved models for facial expression recognition in mice with abnormal glucose metabolism.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
A multimodal emotion recognition method that utilizes facial expressions, body postures, and movement trajectories to detect fear in mice is proposed in this study. By integrating and analyzing these distinct data sources through feature encoders and attention classifiers, we developed a robust emotion classification model. The performance of the model was evaluated by comparing it with single-modal methods, and the results showed significant accuracy improvements. Our findings indicate that the multimodal fusion emotion recognition model enhanced the precision of emotion detection, achieving a fear recognition accuracy of 86.7%. Additionally, the impacts of different monitoring durations and frame sampling rates on the achieved recognition accuracy were investigated in this study. The proposed method provides an efficient and simple solution for conducting real-time, comprehensive emotion monitoring in animal research, with potential applications in neuroscience and psychiatric studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.