Enhancing hospital security and patient monitoring through WhoFi-inspired LiFi channel sensing with privacy preservation

Abstract

Ensuring both secure connectivity and patient safety has become a growing concern in modern hospitals. Although LiFi (Light Fidelity) provides high-speed and interference-free communication, the possibility of using it as a sensing platform has not been investigated to the full extent. This paper presents a LiFi-based system that integrates WHOFi for hospital security and patient monitoring. With simulations based on MATLAB, we simulate the change in LiFi channels due to human presence, movement, and falls and extract statistical and spectral characteristics of the machine learning classifier. The system has a high accuracy of around 94% in activity recognition (empty, movement, fall) and the Equal Error Rate (EER) of 5% in staff authentication. Such a solution is privacy-sensitive, non-invasive and inherently limited to the room boundaries, unlike camera-based or wearable systems, which increase the level of security and patient monitoring in healthcare settings. The findings point to the two-fold nature of LiFi as a communication and sensing technology, which opens the potential for smart hospital infrastructures. This numerical evaluation study will be expanded to hardware testbeds and deep learning models to be applicable in the real world in the future. The proposed system enhances hospital data security and patient tracking efficiency using optical wireless communication.