Development of mobile applications for battery capacity detection in smart contact lens

Abstract

With the development of wearable electronic devices, smart contact lenses (SCLs) have attracted increasing attention as a new tool for real-time health monitoring. One of the main challenges in advancing this technology is how to manage power effectively, especially when it comes to monitoring battery levels. Due to the small size and soft structure of SCLs, traditional battery monitoring methods are not suitable. Therefore, it is necessary to find new, simple, and non-invasive solutions. This study introduces a software-based method for battery monitoring in SCLs that uses Prussian Blue (PB) as the electrochromic material. PB can change color during charging and discharging, which provides a useful signal to estimate the battery status. Based on this principle, A MATLAB-based image analysis system was developed to recognize the color of the lens and predict its battery capacity. The system includes image preprocessing, color feature extraction, and regression modeling using machine learning. A graphical user interface (GUI) was also created to visually reflect the data, which makes the application easier to use. The method allows users to assess battery levels in real time without using complicated devices. It demonstrates a new way of combining electrochemical materials with digital tools and shows the potential for further development in wearable medical electronics.