mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Data Analysis on Powered Two Wheelers Riders’ Behaviour using Machine Learning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
UPE-IFSTTAR/TS2/SIMU&MOTO, F-77447 Marne la Vallée Cedex, France.
Show others and affiliations
2019 (English)In: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, Barcelona, Spain, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Analyzing powered two-wheeler rider behavior, i.e. classification of riding patterns based on 3-D accelerometer/gyroscope sensors mounted on motorcycles is challenging. This paper presents machine learning approach to classify four different riding events performed by powered two wheeler riders’ as a step towards increasing traffic safety. Three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been used to classify riding patterns. The classification is conducted based on features extracted in time and frequency domains from accelerometer/gyroscope sensors signals. A comparison result between different filter frequencies, window sizes, features sets, as well as machine learning algorithms is presented. According to the results, the Random Forest method performs most consistently through the different data sets and scores best.

Place, publisher, year, edition, pages
Barcelona, Spain, 2019.
Keywords [en]
machine learning, Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), powered two-wheeler, classification of riding patterns, accelerometer/gyroscope.
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-43909OAI: oai:DiVA.org:mdh-43909DiVA, id: diva2:1325085
Conference
First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, 20 Mar 2019, Barcelona, Spain
Projects
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the roadAvailable from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-06-14Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Ahmed, Mobyen Uddin

Search in DiVA

By author/editor
Ahmed, Mobyen Uddin
By organisation
Embedded Systems
Engineering and TechnologyComputer Systems

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 43 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf