The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2020.0110976
PDF

Using Wearable Sensors for Human Activity Recognition in Logistics: A Comparison of Different Feature Sets and Machine Learning Algorithms

Author 1: Abbas Shah Syed
Author 2: Zafi Sherhan Syed
Author 3: Muhammad Shehram Shah
Author 4: Salahuddin Saddar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 9, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The topic of human activity recognition has gained a lot of attention due to its usage for exercise monitoring, smart health and assisted living. Even though the aforementioned domains have received significant interest by researchers, activity recognition for industrial settings has received little attention in comparison. Industry 4.0 involves the assimilation of industrial workers with robots and other equipment used in the industry and necessitates the development of recognition methodologies for activities being performed in industries. In this regard, this paper presents a comparison in performance of various time/frequency domain features and popular machine learning algorithms for use in activity recognition in a logistics scenario. Experiments were conducted on inertial measurement sensor data from the recently released LARa dataset which involved three feature sets being used with four machine learning algorithms; Support Vector Machines, Decision Trees, Random Forests and Extreme Gradient Boost (XGBoost). The best result achieved in the experiments was an average accuracy of 78.61% using the XGBoost classifier while using both time and frequency domain features. This work serves as a baseline for activity recognition in logistics using IMU sensors and enables the development of solutions to support fulfillment of Industry 4.0 goals.

Keywords: Human Activity Recognition (HAR); inertial sen-sors; LARa dataset; smart industry

Abbas Shah Syed, Zafi Sherhan Syed, Muhammad Shehram Shah and Salahuddin Saddar, “Using Wearable Sensors for Human Activity Recognition in Logistics: A Comparison of Different Feature Sets and Machine Learning Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110976

@article{Syed2020,
title = {Using Wearable Sensors for Human Activity Recognition in Logistics: A Comparison of Different Feature Sets and Machine Learning Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110976},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110976},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {Abbas Shah Syed and Zafi Sherhan Syed and Muhammad Shehram Shah and Salahuddin Saddar}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org