Work

Real-Time Rodent Pupillometry on an Embedded Platform for Neuromodulation

Year: 2025

Type: article

Abstract: Pupillometry has recently emerged as a sensitive biomarker for autonomic and cortical activity, offering a non-invasive readout for neuromodulation research. However, existing commercial and deep learning-based pupillometry systems are primarily designed for humans, rendering them unsuitable for rodent experiments due to differences in ocular morphology, fur-induced optical artifacts, and the computational demands of high-resolution imaging. In this study, we present a low-cost, integrated real-time rodent pupillometry system built on an embedded platform. The proposed rule-based pupillometry algorithm was optimized with adaptive ellipse fitting, RGB masking artifact suppression, and greedy tracking, achieving robust performance under infrared illumination without requiring GPU acceleration. By minimizing errors caused by complex fur patterns and rodent-specific ocular features, our approach achieved an 86.0% detection rate in rat pupillometry, substantially surpassing the 63.1% attained by existing approach. The system was validated in vivo through vagus nerve stimulation (VNS) experiments in Long-Evans rats, where dynamic changes in pupil size reliably reflected stimulation intensity. By enabling an effective evaluation of VNS, these findings highlight the utility of our system as a practical preclinical tool and underscore the broader potential of pupillometry as a non-invasive biomarker for neuromodulation.

Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering

DOI: 10.1109/tnsre.2025.3643813

Authors: Sunguk Hong, Mingeun Cho, Sung-Min Park

Institution: Pohang University of Science and Technology

Language: English

Topic: Gaze Tracking and Assistive Technology

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075