WiFall: Device-free Fall Detection by Wireless Networks

MPhil Thesis Defence


Title: "WiFall: Device-free Fall Detection by Wireless Networks"

By

Miss Chunmei HAN


Abstract

The world population is in the midst of a unique and irreversible process 
of aging. Fall, which is one of the major health threats and obstacles to 
independent living of elders, will aggravate the global pressure in 
elders’ health care and injury rescue. Thus, automatic fall detection is 
highly in need. Current proposed fall detection systems either have not 
comprehensively satisfied performance or interfere people's daily life. 
These limitations make it hard to widely deploy fall detection systems in 
reality. In this work, we first studied the wireless signal propagation 
model by considering human activities influence.

We then propose a novel and truly unobtrusive detection method based on 
the advanced wireless technologies, which we call as WiFall. WiFall 
employs the variance pattern of Channel State Information (CSI) as the 
indicator of human activities. As CSI is readily available in prevalent 
in-use wireless infrastructures, WiFall withdraws the need for hard-ware 
modification, environmental setup and worn or taken devices. The proposed 
system mainly consists of two parts: local outlier detection and activity 
classification. The local outlier detection finds abnormal signal patterns 
which can eliminate stable and walk patterns. Activity classification 
algorithm distinguishes fall from sit. Our experiments are conducted on a 
HP laptop equipped with a three-antenna Intel WiFi Link 5300. Two 
environment scenarios and three layout schemes are examined. As 
demonstrated by the experimental results, our system yielded 94% detection 
precision with false alarm rate of 14% in the best case.


Date:			Thursday, 1 August 2013

Time:			10:30am - 12:30pm

Venue:			Room 3501
 			Lifts 25/26

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Dr. Raymond Wong (Chairperson)
 			Dr. Lei Chen


**** ALL are Welcome ****