--- 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@ ...