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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.
Using Wearable Sensors for Human Activity Recognition in Logistics: A Comparison of Different Feature Sets and Machine Learning Algorithms
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110976
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 9, 2020.
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}
}
