The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Integration of Smart Home Technologies in a Health Monitoring System for the Elderly
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
A review of smart homes-Present state and future challenges
Computer Methods and Programs in Biomedicine
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
A pyroelectric infrared sensor-based indoor location-aware system for the smart home
IEEE Transactions on Consumer Electronics
Genetic algorithm and pure random search for exosensor distribution optimisation
International Journal of Bio-Inspired Computation
Hi-index | 0.00 |
In current Smart Home implementations pressure sensors within the environment are normally deployed in a uniform pattern. Nevertheless, in order to create an optimised pressure sensor deployment paradigm it is necessary to correlate the positions of sensors with the high frequency movement behaviours of the inhabitant. The locations of furniture and other objects in the environment should also be taken into consideration. To create a paradigm for optimised sensor deployment, data pertaining to inhabitant movement behaviour first needs to be collected. This paper outlines the evaluation of two movement behaviour capture methods and assesses them for practical issues such as ease of installation and feasibility of use.