---
_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. <i>Journal of Ambient Intelligence
    and Humanized Computing</i>. 2022;13:2621–2635. doi:<a href="https://doi.org/10.1007/s12652-021-03569-z">10.1007/s12652-021-03569-z</a>
  apa: Goudarzi, S., Sharif, M., &#38; Karimipour, F. (2022). A context-aware dimension
    reduction framework for trajectory and health signal analyses. <i>Journal of Ambient
    Intelligence and Humanized Computing</i>. Springer Nature. <a href="https://doi.org/10.1007/s12652-021-03569-z">https://doi.org/10.1007/s12652-021-03569-z</a>
  chicago: Goudarzi, Samira, Mohammad Sharif, and Farid Karimipour. “A Context-Aware
    Dimension Reduction Framework for Trajectory and Health Signal Analyses.” <i>Journal
    of Ambient Intelligence and Humanized Computing</i>. Springer Nature, 2022. <a
    href="https://doi.org/10.1007/s12652-021-03569-z">https://doi.org/10.1007/s12652-021-03569-z</a>.
  ieee: S. Goudarzi, M. Sharif, and F. Karimipour, “A context-aware dimension reduction
    framework for trajectory and health signal analyses,” <i>Journal of Ambient Intelligence
    and Humanized Computing</i>, 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.” <i>Journal of Ambient Intelligence and
    Humanized Computing</i>, vol. 13, Springer Nature, 2022, pp. 2621–2635, doi:<a
    href="https://doi.org/10.1007/s12652-021-03569-z">10.1007/s12652-021-03569-z</a>.
  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'
...