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Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 1 (2022)

DOI
10.11591/ijece.v12i1.pp785-792
ISSN
2722-2578, 2088-8708
Published
2022-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%).

Collaboration network of the authors

25 authors · 75 co-authorship links · 36 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Hanoi University of Industry
  • Trường ĐH Nguyễn Tất Thành
  • Hung Yen University of Technology and Education
  • Kalasalingam Academy of Research and Education
  • Electric Power University
  • Koneru Lakshmaiah Education Foundation
  • Vietnam Academy of Science and Technology
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Quoc-Anh GianAffiliation unknown4
Dinh-Chinh NguyenVNU University of Engineering and Technology4
Dinh-Chinh NguyenAffiliation unknown2
Tran Duc-TanAffiliation unknown2
Kieu Thi NguyenTrường ĐH Nguyễn Tất Thành2
Gian Quoc AnhNam Dinh University of Nursing1
Kumbesan Sandy SandrasegaranUniversity of Technology Sydney1
Phung Cοng Phi KhanhAffiliation unknown1
Trung HoangThai Nguyen University of Information and Communication Technology1
Nguyễn Văn ThăngThuyloi University1
Ninh Trung BuiVietnam National University, Hanoi1
Vu Khanh QuyHung Yen University of Technology and Education1
Pham Minh ChuanHung Yen University of Technology and Education1
Balakrishnan BaranitharanKalasalingam Academy of Research and Education1
Karthik ChandranAffiliation unknown1
Vaithilingam Subramaniyan MathanKalasalingam Academy of Research and Education1
Subrata ChowdhuraAffiliation unknown1
Thi-Thu NguyenHanoi University of Industry1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT</dc:title><dc:creator>Nguyen, Thi Thu</dc:creator><dc:creator>Doan, Phuc Thinh</dc:creator><dc:creator>Le, Anh-Ngoc</dc:creator><dc:creator>Prakash, Kolla Bhanu</dc:creator><dc:creator>Chowdhury, Subrata</dc:creator><dc:creator>Tran, Duc-Nghia</dc:creator><dc:creator>Tran, Duc-Tan</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span&amp;gt;In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%).&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2022-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v12i1.pp785-792</dc:identifier><dc:identifier>10.11591/ijece.v12i1.pp785-792</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 12 No 1 (2022); 785</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/24764/15385</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>