Research Article
TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags
@ARTICLE{10.4108/eai.22-7-2015.2260072, author={Wenjie Ruan and Lina Yao and Quan Z. Sheng and Nickolas Falkner and Xue Li and Tao Gu}, title={TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags}, journal={EAI Endorsed Transactions on Internet of Things}, volume={1}, number={2}, publisher={EAI}, journal_a={IOT}, year={2015}, month={8}, keywords={fall detection, device-free, rfid, anomaly detection, household monitoring}, doi={10.4108/eai.22-7-2015.2260072} }
- Wenjie Ruan
Lina Yao
Quan Z. Sheng
Nickolas Falkner
Xue Li
Tao Gu
Year: 2015
TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags
IOT
EAI
DOI: 10.4108/eai.22-7-2015.2260072
Abstract
Falls are among the leading causes of hospitalization for the elderly and illness individuals. Considering that the elderly often live alone and receive only irregular visits, it is essential to develop such a system that can effectively detect a fall or abnormal activities. However, previous fall detection systems either require to wear sensors or are able to detect a fall but fail to provide fine-grained contextual information (e.g., what is the person doing before falling, falling directions). In this paper, we propose a device-free, fine-grained fall detection system based on pure passive UHF RFID tags, which not only is capable of sensing regular actions and fall events simultaneously, but also provide caregivers the contexts of fall orientations. We first augment the Angle-based Outlier Detection Method (ABOD) to classify normal actions (e.g., standing, sitting, lying and walking) and detect a fall event. Once a fall event is detected, we first segment a fix-length RSSI data stream generated by the fall and then utilize Dynamic Time Warping (DTW) based kNN to distinguish the falling direction. The experimental results demonstrate that our proposed approach can distinguish the living status before fall happening, as well as the fall orientations with a high accuracy. The experiments also show that our device-free, fine-grained fall detection system offers a good overall performance and has the potential to better support the assisted living of older people.
Copyright © 2015 Wenjie Ruan et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.