Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/143448
Title: WiFall: Device-Free Fall Detection by Wireless Networks
Authors: Yuxi Wang;Kaishun Wu;Lionel M. Ni
Year: 2017
Publisher: IEEE
Abstract: Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.
URI: http://localhost/handle/Hannan/143448
volume: 16
issue: 2
More Information: 581,
594
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7458186.pdf1.64 MBAdobe PDF
Title: WiFall: Device-Free Fall Detection by Wireless Networks
Authors: Yuxi Wang;Kaishun Wu;Lionel M. Ni
Year: 2017
Publisher: IEEE
Abstract: Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.
URI: http://localhost/handle/Hannan/143448
volume: 16
issue: 2
More Information: 581,
594
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7458186.pdf1.64 MBAdobe PDF
Title: WiFall: Device-Free Fall Detection by Wireless Networks
Authors: Yuxi Wang;Kaishun Wu;Lionel M. Ni
Year: 2017
Publisher: IEEE
Abstract: Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.
URI: http://localhost/handle/Hannan/143448
volume: 16
issue: 2
More Information: 581,
594
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7458186.pdf1.64 MBAdobe PDF