Challenges in data collection in real-world environments for activity recognition
Date Issued
2019-07-01
Author(s)
Dimitrievski, Ace
Trajkovikj, Vladmir
Koceski, Saso
Abstract
Detecting and recognizing activities of daily living
is an important part of ambient assisted living (AAL) systems.
This part of the system has the highest impact on the overall
system efficiency because it directly provides insights into the
user’s health state. One of the main challenges that AAL systems
are facing are the privacy concerns and the intrusiveness of the
sensors that are being deployed. In an ideal scenario, an aged
person should be able to continue his or her normal life without
noticing that they are being monitored. Another issue for such
systems is the data collection. The current approaches usually
use data generated in labs and data from end-users users is
usually unavailable due to ethical concerns and the inability to
deploy them in their living environments. Publications that rely
on real-life scenario data are scarce. In this paper, we present
the challenges one faces when trying to produce a sound dataset
for further analysis and suggest ideas for overcoming them.
is an important part of ambient assisted living (AAL) systems.
This part of the system has the highest impact on the overall
system efficiency because it directly provides insights into the
user’s health state. One of the main challenges that AAL systems
are facing are the privacy concerns and the intrusiveness of the
sensors that are being deployed. In an ideal scenario, an aged
person should be able to continue his or her normal life without
noticing that they are being monitored. Another issue for such
systems is the data collection. The current approaches usually
use data generated in labs and data from end-users users is
usually unavailable due to ethical concerns and the inability to
deploy them in their living environments. Publications that rely
on real-life scenario data are scarce. In this paper, we present
the challenges one faces when trying to produce a sound dataset
for further analysis and suggest ideas for overcoming them.
Subjects
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