The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls or suspicious wanderings. Such pervasive monitoring functionality offers potentials for a number of applications, such as the potential for elders to live at home for longer periods of their lives with minimal human supervision, and the potential for security monitoring within buildings.
The focus of this project is on the investigation and development of an indoor localisation technique which can be readily deployed in a realistic indoor environment with minimal hardware requirements. The location information will be visualized in 3D models of the indoor environment. The project will investigate a number of technologies including wearable sensors, blue-tooth, radio and smartphones. New machine learning based approaches will be developed to support data analysis and location detection.
1. D. Kelly, S. McLoone, and T. Dishongh, "A Bluetooth-based Minimum Infrastructure Home Localisation System". Proceedings of 5th IEEE International Symposium on Wireless Communication Systems (ISWCS 2008), 2008.
2. Y. Guo, S. Pan; HY. Wang; H. Zheng; , "A hybrid classification approach to improving location accuracy in a Bluetooth-based room localisation system," Machine Learning and Cybernetics (ICMLC), 2010 International Conference on , vol.1, pp.345-350, 11-14 July 2010
3. E Martin, O. Vinyals, G. Friedland and R. Bajcsy, “Precise indoor localization using smart phones”, MM’10, pp. 787-790, Oct 2010, Firenze, Italy.
First Supervisor: Wang, HY Dr
Second Supervisor: Zheng, H Dr
Third Supervisor: Wang, H Dr
Collaboration: This project does not involve collaboration with another establishment