@article{ref1,
title="Recognition of falls using dense sensing in an ambient assisted living environment",
journal="Pervasive and mobile computing",
year="2016",
author="Wickramasinghe, Asanga and Torres, Roberto Luis Shinmoto and Ranasinghe, Damith C.",
volume="34",
number="",
pages="14-24",
abstract="Long-lie situations following a fall is detrimental, particularly for older people as they are not only affected physically but also psychologically. In this paper, we describe a dense sensing approach for falls detection in an ambient assisted living environment such as a room, hall or a walkway. We utilize a smart carpet consisting of an array of Radio Frequency Identification (RFID) tags arranged in a 2-dimensional grid to create an unobtrusive monitoring area and to detect falls among other activities. In particular, we propose an algorithm based on heuristic and machine learning to detect 'long-lie' situations. The proposed algorithm minimizes the effects of noise present in the RFID information by relying on 8 features extracted using only binary tag observation information from a possible location of a fall on the smart carpet. By evaluating the proposed approach with broadly scripted activities, which included a complex set of walking patterns, we show that the proposed algorithm depicted a good overall performance of 93% F-score.<p /> <p>Language: en</p>",
language="en",
issn="1574-1192",
doi="10.1016/j.pmcj.2016.06.004",
url="http://dx.doi.org/10.1016/j.pmcj.2016.06.004"
}