Cloud-based recognition of complex activities for ambient assisted living in smart homes with non-invasive sensors
Date Issued
2017-07-06
Author(s)
Zdravevska, Aleksandra
Dimitrievski, Ace
Abstract
Automatic recognition of complex activities can aid
in finding correlations between the daily habits of people and
their health state, and can further lead to early detection of
diseases or accidents. In this paper we propose a cloud-based
system for recognition of complex activities by detecting series of
atomic actions with non-invasive sensors. Collected data from
non-invasive, non-intrusive and privacy preserving sensors is
streamed into a cloud-based system, where automated feature
extraction and activity recognition is performed. The prototype
of the proposed system is evaluated with an experiment. Five
activities performed by a person in a room were monitored by a
sensor kit and streamed to the cloud, where the built classification
models could recognize the activities with accuracy of 80% to
95%, depending on the length of segmentation windows which
varied from 5 to 20 seconds, respectively.
in finding correlations between the daily habits of people and
their health state, and can further lead to early detection of
diseases or accidents. In this paper we propose a cloud-based
system for recognition of complex activities by detecting series of
atomic actions with non-invasive sensors. Collected data from
non-invasive, non-intrusive and privacy preserving sensors is
streamed into a cloud-based system, where automated feature
extraction and activity recognition is performed. The prototype
of the proposed system is evaluated with an experiment. Five
activities performed by a person in a room were monitored by a
sensor kit and streamed to the cloud, where the built classification
models could recognize the activities with accuracy of 80% to
95%, depending on the length of segmentation windows which
varied from 5 to 20 seconds, respectively.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
2017_AAL_cloud-based-privacy_AZ_AD_PL_EZ_VT_new.pdf
Size
600.67 KB
Format
Adobe PDF
Checksum
(MD5):c09b979a0e0ff632b6ba3c4635d27698