stract. The management of health systems has been one of the main challenges in several European countries, especially where the ageing population is increasing. This led to the adoption of smarter technologies as a means to automate the processes within hospitals. One of the technologies adopted is active location solutions, which allows the staff within the hospital to quickly find any sort of entity, from key persons to equipment. In this work, we focus on developing a reliable method for active location based on Received Signal Strength Indication (RSSI) antennas, passive tags, and Machine Learning (ML) models. Since the tags are passive, the usage of RSSI is discouraged, since it does not vary sufficiently. We explored the usage of alternative features, such as the number of activations per tag within a time slot. Throughout our evaluation, we were able to reach an average error of 0.275 m which is similar to existing RSSI Indoor Positioning Systems (IPS)