---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Samira
      foaf_name: Goudarzi, Samira
      foaf_surname: Goudarzi
  - foaf_Person:
      foaf_givenName: Mohammad
      foaf_name: Sharif, Mohammad
      foaf_surname: Sharif
  - foaf_Person:
      foaf_givenName: Farid
      foaf_name: Karimipour, Farid
      foaf_surname: Karimipour
      foaf_workInfoHomepage: http://www.librecat.org/personId=2A2BCDC4-CF62-11E9-BE5E-3B1EE6697425
    orcid: 0000-0001-6746-4174
  bibo_doi: 10.1007/s12652-021-03569-z
  bibo_volume: 13
  dct_date: 2022^xs_gYear
  dct_identifier:
  - UT:000712198000001
  dct_isPartOf:
  - http://id.crossref.org/issn/1868-5137
  - http://id.crossref.org/issn/1868-5145
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_subject:
  - general computer science
  dct_title: A context-aware dimension reduction framework for trajectory and health
    signal analyses@
...