Machine Learning-Based Wi-Fi RSS Fingerprinting for Indoor Zone Classification of Biomedical Assets in Hospitals
Keywords:
Wi-Fi RSS fingerprinting, indoor zone classification, biomedical asset tracking, healthcare facilities, machine learningAbstract
The frequent relocation of biomedical equipment in healthcare facilities delays maintenance and reduces operational efficiency. While dedicated positioning infrastructure can mitigate these delays, its deployment often incurs high financial costs. This work proposes an indoor zone classification system that leverages existing Wi-Fi received signal strength (RSS) fingerprints and machine learning. The methodology evaluates five supervised classifiers (Naive Bayes, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Multilayer Perceptron) using an empirical dataset of 360 observations. These RSS measurements were collected by an ESP8266-based sensing module across six functional zones covered by four access points. Under a 5-fold stratified cross-validation protocol, the Random Forest model achieved a mean validation accuracy of 97.57%. These results demonstrate that existing Wi-Fi infrastructure combined with low-cost edge sensing provides a viable solution for biomedical asset tracking in hospitals.
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