Smart cities and smart homes are driving economic growth and improving the quality of people's life by enabling local development and harnessing technologies, especially the ones leading to smart outcomes and connectivity. Application of smart solutions will enable human living environments to use technology, information and data to improve infrastructures and services in the behalf of human beings. The welfare of elderly people living alone, at their homes, or, in rest houses, can significantly be improved with the introduction of these smart solutions. Wireless indoor tracking of elderly people is an important feature together with the DLMS/COSEM protocol. Above all using low power devices to assist and monitoring them. The paper highlights implementation in the real world, proposing a method that can be suited in a large range of cases thanks to the scene analysis. An indoor scalable infrastructure allows the tracking method to be implemented incrementally with different performances, where optimal accuracy comes from an adaptable threshold. The method has been tested in different environments using both simulations and direct measurements. Result from the proposed method are compared with the KNN method.
Wireless indoor low power tracking system for elderly people assistance in an urban environment
PACIELLO, Vincenzo
2016-01-01
Abstract
Smart cities and smart homes are driving economic growth and improving the quality of people's life by enabling local development and harnessing technologies, especially the ones leading to smart outcomes and connectivity. Application of smart solutions will enable human living environments to use technology, information and data to improve infrastructures and services in the behalf of human beings. The welfare of elderly people living alone, at their homes, or, in rest houses, can significantly be improved with the introduction of these smart solutions. Wireless indoor tracking of elderly people is an important feature together with the DLMS/COSEM protocol. Above all using low power devices to assist and monitoring them. The paper highlights implementation in the real world, proposing a method that can be suited in a large range of cases thanks to the scene analysis. An indoor scalable infrastructure allows the tracking method to be implemented incrementally with different performances, where optimal accuracy comes from an adaptable threshold. The method has been tested in different environments using both simulations and direct measurements. Result from the proposed method are compared with the KNN method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
