Continuous Pupillography: A Case for Visual Health Ecosystem
This work addresses the need for a visual health ecosystem for medical diagnostics, but it is incremental as it builds on existing technologies without major breakthroughs.
The paper tackles the problem of continuous eye monitoring for ophthalmological diagnostics by proposing an IoT-based wearable pupillography system, comparing two mini-camera modules and testing a light algorithm under various lighting conditions, though no concrete performance numbers are provided.
This article aims to cover pupillography, and its potential use in a number of ophthalmological diagnostic applications in biomedical space. With the ever-increasing incorporation of technology within our daily lives and an ever-growing active research into smart devices and technologies, we try to make a case for a health ecosystem that revolves around continuous eye monitoring. We tend to summarize the design constraints & requirements for an IoT-based continuous pupil detection system, with an attempt at developing a pipeline for wearable pupillographic device, while comparing two compact mini-camera modules currently available in the market. We use a light algorithm that can be directly adopted to current micro-controllers, and share our results for different lighting conditions, and scenarios. Lastly, we present our findings, along with an analysis on the challenges faced and a way ahead towards successfully building this ecosystem.