---
_id: '10208'
abstract:
- lang: eng
text: It is practical to collect a huge amount of movement data and environmental
context information along with the health signals of individuals because there
is the emergence of new generations of positioning and tracking technologies and
rapid advancements of health sensors. The study of the relations between these
datasets and their sequence similarity analysis is of interest to many applications
such as health monitoring and recommender systems. However, entering all movement
parameters and health signals can lead to the complexity of the problem and an
increase in its computational load. In this situation, dimension reduction techniques
can be used to avoid consideration of simultaneous dependent parameters in the
process of similarity measurement of the trajectories. The present study provides
a framework, named CaDRAW, to use spatial–temporal data and movement parameters
along with independent context information in the process of measuring the similarity
of trajectories. In this regard, the omission of dependent movement characteristic
signals is conducted by using an unsupervised feature selection dimension reduction
technique. To evaluate the effectiveness of the proposed framework, it was applied
to a real contextualized movement and related health signal datasets of individuals.
The results indicated the capability of the proposed framework in measuring the
similarity and in decreasing the characteristic signals in such a way that the
similarity results -before and after reduction of dependent characteristic signals-
have small differences. The mean differences between the obtained results before
and after reducing the dimension were 0.029 and 0.023 for the round path, respectively.
acknowledgement: The third author acknowledges the funding received from the Wittgenstein
Prize, Austrian Science Fund (FWF), grant no. Z 342-N31.
article_processing_charge: No
article_type: original
author:
- first_name: Samira
full_name: Goudarzi, Samira
last_name: Goudarzi
- first_name: Mohammad
full_name: Sharif, Mohammad
last_name: Sharif
- first_name: Farid
full_name: Karimipour, Farid
id: 2A2BCDC4-CF62-11E9-BE5E-3B1EE6697425
last_name: Karimipour
orcid: 0000-0001-6746-4174
citation:
ama: Goudarzi S, Sharif M, Karimipour F. A context-aware dimension reduction framework
for trajectory and health signal analyses. Journal of Ambient Intelligence
and Humanized Computing. 2022;13:2621–2635. doi:10.1007/s12652-021-03569-z
apa: Goudarzi, S., Sharif, M., & Karimipour, F. (2022). A context-aware dimension
reduction framework for trajectory and health signal analyses. Journal of Ambient
Intelligence and Humanized Computing. Springer Nature. https://doi.org/10.1007/s12652-021-03569-z
chicago: Goudarzi, Samira, Mohammad Sharif, and Farid Karimipour. “A Context-Aware
Dimension Reduction Framework for Trajectory and Health Signal Analyses.” Journal
of Ambient Intelligence and Humanized Computing. Springer Nature, 2022. https://doi.org/10.1007/s12652-021-03569-z.
ieee: S. Goudarzi, M. Sharif, and F. Karimipour, “A context-aware dimension reduction
framework for trajectory and health signal analyses,” Journal of Ambient Intelligence
and Humanized Computing, vol. 13. Springer Nature, pp. 2621–2635, 2022.
ista: Goudarzi S, Sharif M, Karimipour F. 2022. A context-aware dimension reduction
framework for trajectory and health signal analyses. Journal of Ambient Intelligence
and Humanized Computing. 13, 2621–2635.
mla: Goudarzi, Samira, et al. “A Context-Aware Dimension Reduction Framework for
Trajectory and Health Signal Analyses.” Journal of Ambient Intelligence and
Humanized Computing, vol. 13, Springer Nature, 2022, pp. 2621–2635, doi:10.1007/s12652-021-03569-z.
short: S. Goudarzi, M. Sharif, F. Karimipour, Journal of Ambient Intelligence and
Humanized Computing 13 (2022) 2621–2635.
date_created: 2021-11-02T09:28:55Z
date_published: 2022-05-01T00:00:00Z
date_updated: 2023-08-02T13:31:48Z
day: '01'
ddc:
- '000'
department:
- _id: HeEd
doi: 10.1007/s12652-021-03569-z
external_id:
isi:
- '000712198000001'
file:
- access_level: open_access
checksum: 0a8961416a9bb2be5a1cebda65468bcf
content_type: application/pdf
creator: fkarimip
date_created: 2021-11-12T19:38:05Z
date_updated: 2022-12-20T23:30:08Z
embargo: 2022-11-12
file_id: '10279'
file_name: A Context‑aware Dimension Reduction Framework - Journal of Ambient Intelligence
2021 (Preprint version).pdf
file_size: 1634958
relation: main_file
file_date_updated: 2022-12-20T23:30:08Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
keyword:
- general computer science
language:
- iso: eng
month: '05'
oa: 1
oa_version: Submitted Version
page: 2621–2635
project:
- _id: 268116B8-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00342
name: The Wittgenstein Prize
publication: Journal of Ambient Intelligence and Humanized Computing
publication_identifier:
eissn:
- 1868-5145
issn:
- 1868-5137
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: A context-aware dimension reduction framework for trajectory and health signal
analyses
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 13
year: '2022'
...