Abstract
This work illustrates an innovative localisation sensor network that uses multiple PIR and ultrasonic sensors installed on a mobile social robot to localise occupants in indoor environments. The system presented aims to measure movement direction and distance to reconstruct the movement of a person in an indoor environment by using sensor activation strategies and data processing techniques. The data collected are then analysed using both a supervised (Decision Tree) and an unsupervised (K-Means) machine learning algorithm to extract the direction and distance of occupant movement from the measurement system, respectively. Tests in a controlled environment have been conducted to assess the accuracy of the methodology when multiple PIR and ultrasonic sensor systems are used. In addition, a qualitative evaluation of the system's ability to reconstruct the movement of the occupant has been performed. The system proposed can reconstruct the direction of an occupant with an accuracy of 70.7% and uncertainty in distance measurement of 6.7%.