Towards application of non-invasive environmental sensors for risks and activity detection
Date Issued
2016-09-08
Author(s)
Dimitrievski, Ace
Trajkovikj, Vladmir
Abstract
One of the main goals of Ambient Assisted Living
(AAL) is to provide supportive environment for the elderly or
disabled. Such environments are not feasible without correctly
identifying states and activities of the persons receiving the care.
They rely on the interaction and processing of data originating
from many components and objects in the surrounding. In order
to collect the data, various sensors are used to monitor the
environment, as well as the person’s health parameters. One of
the main concerns in AAL is preservation of user’s privacy. In this
paper we address that by proposing a non-intrusive approach for
data collection and identification of daily activity and risks. We
describe the wiring of such system based on cheap non-intrusive
sensors, deployment in a real environment, the protocols for data
fusion and processing, and explain how machine learning could be
employed for detecting risks and activities. The main contribution
of this paper is development of non-intrusive sensor kits that can
be easily deployed in real-life environments and are capable of
collecting data that can reliable detect activities and risk.
(AAL) is to provide supportive environment for the elderly or
disabled. Such environments are not feasible without correctly
identifying states and activities of the persons receiving the care.
They rely on the interaction and processing of data originating
from many components and objects in the surrounding. In order
to collect the data, various sensors are used to monitor the
environment, as well as the person’s health parameters. One of
the main concerns in AAL is preservation of user’s privacy. In this
paper we address that by proposing a non-intrusive approach for
data collection and identification of daily activity and risks. We
describe the wiring of such system based on cheap non-intrusive
sensors, deployment in a real environment, the protocols for data
fusion and processing, and explain how machine learning could be
employed for detecting risks and activities. The main contribution
of this paper is development of non-intrusive sensor kits that can
be easily deployed in real-life environments and are capable of
collecting data that can reliable detect activities and risk.
Subjects
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