@inproceedings{9bf939c41ced499d940a147188a5d735,
title = "Anti-fall: A non-intrusive and real-time fall detector leveraging CSI from commodity WIFI devices",
abstract = "Fall is one of the major health threats and obstacles to independent living for elders, timely and reliable fall detection is crucial for mitigating the effects of falls. In this paper, leveraging the fine-grained Channel State Information (CSI) and multi-antenna setting in commodity WiFi devices, we design and implement a real-time, non-intrusive, and low-cost indoor fall detector, called Anti-Fall. For the first time, the CSI phase difference over two antennas is identified as the salient feature to reliably segment the fall and fall-like activities, both phase and amplitude information of CSI is then exploited to accurately separate the fall from other fall-like activities. Experimental results in two indoor scenarios demonstrate that Anti-Fall consistently outperforms the stateof- the-art approach WiFall, with 10\% higher detection rate and 10\% less false alarm rate on average.",
keywords = "Activity recognition, CSI, Fall detection, Wifi",
author = "Daqing Zhang and Hao Wang and Yasha Wang and Junyi Ma",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th International Conference on Smart Homes and Health Telematics, ICOST 2015 ; Conference date: 10-06-2015 Through 12-06-2015",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-19312-0\_15",
language = "English",
isbn = "9783319193113",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "181--193",
editor = "Jacques Demongeot and Antoine Geissbuhler and Bessam Abdulrazak and Mounir Mokhtari and Hamdi Aloulou",
booktitle = "Inclusive Smart Cities and e-Health - 13th International Conference on Smart Homes and Health Telematics, ICOST 2015, Proceedings",
}