Work

Detection of Rat Pain-Related Grooming Behaviors Using Multistream Recurrent Convolutional Networks on Day-Long Video Recordings

Year: 2024

Type: article

Abstract: In experimental pain studies involving animals, subjective pain reports are not feasible. Current methods for detecting pain-related behaviors rely on human observation, which is time-consuming and labor-intensive, particularly for lengthy video recordings. Automating the quantification of these behaviors poses substantial challenges. In this study, we developed and evaluated a deep learning, multistream algorithm to detect pain-related grooming behaviors in rats. Pain-related grooming behaviors were induced by injecting small amounts of pain-inducing chemicals into the rats' hind limbs. Day-long video recordings were then analyzed with our algorithm, which initially filtered out non-grooming segments. The remaining segments, referred to as likely grooming clips, were used for model training and testing. Our model, a multistream recurrent convolutional network, learned to differentiate grooming from non-grooming behaviors within these clips through deep learning. The average validation accuracy across three evaluation methods was 88.5%. We further analyzed grooming statistics by comparing the duration of grooming episodes between experimental and control groups. Results demonstrated statistically significant changes in grooming behavior consistent with pain expression.

Source: Bioengineering

DOI: 10.3390/bioengineering11121180

Authors: Chien-Cheng Lee, Ping-Wing Lui, Wei-Wei Gao, Zhongjian Gao

Institutions: Yuan Ze University, Sanming University

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,069

Funder: National Science and Technology Council