Affiliation:
1. Nimbus Centre Cork Institute of Technology, Cork, Ireland
2. United Technologies Research Centre, Cork, Ireland
Abstract
Falling or tripping among elderly people living on their own is recognized as a major public health worry that can even lead to death. Fall detection systems that alert caregivers, family members or neighbours can potentially save lives. In the past decade, an extensive amount of research has been carried out to develop fall detection systems based on a range of different detection approaches, i.e, wearable and non-wearable sensing and detection technologies. In this paper, we consider an emerging non-wearable fall detection approach based on WiFi Channel State Information (CSI). Previous CSI based fall detection solutions have considered only time domain approaches. Here, we take an altogether different direction, time-frequency analysis as used in radar fall detection. We use the conventional Short-Time Fourier Transform (STFT) to extract time-frequency features and a sequential forward selection algorithm to single out features that are resilient to environment changes while maintaining a higher fall detection rate. When our system is pre-trained, it has a 93% accuracy and compared to RTFall and CARM, this is a 12% and 15% improvement respectively. When the environment changes, our system still has an average accuracy close to 80% which is more than a 20% to 30% and 5% to 15% improvement respectively.
Funder
United Technologies Research Centre, Cork, Ireland
Irish Research Council
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
Reference43 articles.
1. United Nations Department of Economic and Social Affairs Population Division (2015). World Population Prospects: The 2015 Revision custom data acquired via website. https://esa.un.org/unpd/wpp/. Accessed: 2017-04-24. United Nations Department of Economic and Social Affairs Population Division (2015). World Population Prospects: The 2015 Revision custom data acquired via website. https://esa.un.org/unpd/wpp/. Accessed: 2017-04-24.
2. Wideband radar based fall motion detection for a generic elderly
3. Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring
4. Doppler Radar Fall Activity Detection Using the Wavelet Transform
5. Radar and RGB-Depth Sensors for Fall Detection: A Review
Cited by
153 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献