Recognition of falls using dense sensing in an ambient assisted living environment
详细信息 查看全文
- 作者：Asanga Wickramasinghe ; asanga.wickramasinghe@adelaide.edu.au ; Roberto Luis Shinmoto Torres roberto.shinmototorres@adelaide.edu.au ; Damith C. Ranasinghe damith.ranasinghe@adelaide.edu.au
- 关键词：Falls detection ; Passive RFID ; Ambient assisted living ; Healthcare ; Dense sensing
- 刊名：Pervasive and Mobile Computing
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：34
- 期：Complete
- 页码：14-24
- 全文大小：1340 K
- 卷排序：34
文摘
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 a 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 eight 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 depicts a good overall performance of 93% F-score.