Emergency Semantic Feature Vector Extraction From WiFi Signals for In-Home Monitoring of Elderly

The elderly population worldwide is growing drastically. Most elders prefer to live independently in their own homes, which makes them more vulnerable to emergencies and leads to higher death rates. Timely detection and notification of emergencies can minimize the impact of the adversity. In this paper, we try to extract the Emergency Semantic Feature Vector (
$ESFV$
) from the ubiquitous WiFi signals in indoor environments for non-intrusive, cost-effective monitoring of the elderly and effective emergency notification. Common emergencies of the elderly are analyzed and an
$ESFV$
is defined as
$ESFV = ({\lbrace \pi \rbrace,\lbrace \beta \rbrace, \lbrace \rho \rbrace })$
, which is a combination of the
P
osition, (abnormal)
B
ehavior and (abnormal)
R
espiration of the elderly. To extract the
$ESFV$
from WiFi signals, we design a WiFi signal structuring approach, which converts WiFi signals into two structured features, i.e., keypoint maps and respiration graphs, using WiFi Channel State Information (CSI). For keypoint map construction, we extract human-related WiFi signals, based on which we generate CSI maps. Meanwhile, a synchronized camera is used to extract human keypoints and annotate WiFi signals. A neural network is designed to transform the CSI maps into keypoint maps. For respiration graph extraction, we design a respiration-related WiFi signal extraction method and a subcarrier selection algorithm to obtain the respiration graphs. Then we extract the
$\lbrace \pi \rbrace$
,
$\lbrace \beta \rbrace$
,
$\lbrace \rho \rbrace$
from the two structured features:
$\lbrace \pi \rbrace$
and
$\lbrace \beta \rbrace$
are extracted from the keypoint maps while
$\lbrace \rho \rbrace$
is extracted from the respiration graphs. Finally, the feature vector
$(\lbrace \pi \rbrace,\lbrace \beta \rbrace, \lbrace \rho \rbrace)$
of an emergency semantic is formed for notification. We build a prototype system and conduct extensive experiments. Experiment results show that our approaches perform well in both signal structuring (keypoint map construction and respiration graph extraction) and
$ESFV$
extraction. These demonstrate the effectiveness of our approaches for in-home monitoring and emergency notification.