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A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
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2019 (English)In: International Conference on Modern Intelligent Systems Concepts MISC'18, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates and implements six Machine Learning (ML) algorithms, i.e. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Gradient Boosted Trees (GBT) to classify different Pedestrians’ events based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) signals. Pedestrians’ events are pedestrian movements as the first step of H2020 project called SimuSafe1 with a goal to reduce traffic fatalities by doing risk assessments of the pedestrians. The movements the MLs’ models are attempting to classify are standing, walking, and running. Data, i.e. IMU, GPS sensor signals and other contextual information are collected by a smartphone through a controlled procedure. The smartphone is placed in five different positions onto the body of participants, i.e. arm, chest, ear, hand and pocket. The recordings are filtered, trimmed, and labeled. Next, samples are generated from small overlapping sections from which time and frequency domain features are extracted. Three different experiments are conducted to evaluate the performances in term of accuracy of the MLs’ models in different circumstances. The best performing MLs’ models determined by the average accuracy across all experiments is Extra Tree (ET) with a classification accuracy of 91%. 

Place, publisher, year, edition, pages
2019.
Keywords [en]
Machine Learning (ML), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree(DT), Random Forest (RF), Extra Tree (ET), Gradient Boosted Trees (GBT), classification, Pedestrians’ events, Inertial Measurement Unit (IMU), Global Positioning System (GPS) signals
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-41724OAI: oai:DiVA.org:mdh-41724DiVA, id: diva2:1273320
Conference
International Conference on Modern Intelligent Systems Concepts MISC'18, 12 Dec 2018, Rabat, Morocco
Projects
SimuSafe : Simulator of Behavioural Aspects for Safer TransportAvailable from: 2018-12-20 Created: 2018-12-20 Last updated: 2019-06-04Bibliographically approved

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Ahmed, Mobyen Uddin

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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