Comparison of Machine Learning Algorithms for Human Activity Recognition

Hassan Ashraf, Olivier Brüls, Cédric Schwartz, Mohamed Boutaayamou

2023

Abstract

Human activity recognition (HAR) is utilized to automatically identify the daily-life activities of people for the effective management of age-related health conditions. Classical machine learning (ML) algorithms are used to design HAR systems, in a subject-specific or population-based configuration depending on the application. In this study, the performance of 8 classical and ensemble-learning-based ML classifiers has been studied for both HAR configurations. Inertial measurement unit (IMU) signals from 10 healthy participants, corresponding to various static, dynamic, and transitional daily-life activities, were acquired. Random forest (RF), ensemble adaptive boosting (EAB), ensemble subspace (ES), decision tree (DT), k-nearest neighbors (KNN), linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN) were used to classify these activities. The performance of the classifiers was measured in terms of mean classification accuracy (MCA). The results showed that, for a subject-specific HAR system, ES (97.78%) has achieved the highest MCA followed by RF (96.61%) and SVM (96.11%) while outperforming the DT, KNN, and LDA (P-value < 0.05). For a population-based HAR system, SVM (95.18%) achieved the highest MCA, however, no significant difference has been observed among the MCA of all the investigated classifiers (P-value > 0.05). Also, the class-wise comparison reveals that SVM outperformed the other investigated classifiers in terms of MCAs for each of the distinct activities. Based on the HAR configuration incorporating diverse static, dynamic, and transitional daily-life activities, the findings may be used to develop a customized HAR system for the effective management of movement disorders.

Paper Citation

in Harvard Style

Ashraf H., Brüls O., Schwartz C. and Boutaayamou M. (2023). Comparison of Machine Learning Algorithms for Human Activity Recognition. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 162-169. DOI: 10.5220/0011631500003414

in Bibtex Style

@conference{biosignals23,
author={Hassan Ashraf and Olivier Brüls and Cédric Schwartz and Mohamed Boutaayamou},
title={Comparison of Machine Learning Algorithms for Human Activity Recognition},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={162-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011631500003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Comparison of Machine Learning Algorithms for Human Activity Recognition
SN - 978-989-758-631-6
AU - Ashraf H.
AU - Brüls O.
AU - Schwartz C.
AU - Boutaayamou M.
PY - 2023
SP - 162
EP - 169
DO - 10.5220/0011631500003414
PB - SciTePress