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Human Gait Activity Recognition Machine Learning Methods

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper p...

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Autores principales: Slemenšek, Jan, Fister, Iztok, Geršak, Jelka, Bratina, Božidar, van Midden, Vesna Marija, Pirtošek, Zvezdan, Šafarič, Riko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865094/
https://www.ncbi.nlm.nih.gov/pubmed/36679546
http://dx.doi.org/10.3390/s23020745
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author Slemenšek, Jan
Fister, Iztok
Geršak, Jelka
Bratina, Božidar
van Midden, Vesna Marija
Pirtošek, Zvezdan
Šafarič, Riko
author_facet Slemenšek, Jan
Fister, Iztok
Geršak, Jelka
Bratina, Božidar
van Midden, Vesna Marija
Pirtošek, Zvezdan
Šafarič, Riko
author_sort Slemenšek, Jan
collection PubMed
description Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject’s quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm’s robustness was also verified with the successful detection of freezing gait episodes in a Parkinson’s disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.
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spelling pubmed-98650942023-01-22 Human Gait Activity Recognition Machine Learning Methods Slemenšek, Jan Fister, Iztok Geršak, Jelka Bratina, Božidar van Midden, Vesna Marija Pirtošek, Zvezdan Šafarič, Riko Sensors (Basel) Article Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject’s quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm’s robustness was also verified with the successful detection of freezing gait episodes in a Parkinson’s disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization. MDPI 2023-01-09 /pmc/articles/PMC9865094/ /pubmed/36679546 http://dx.doi.org/10.3390/s23020745 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Slemenšek, Jan
Fister, Iztok
Geršak, Jelka
Bratina, Božidar
van Midden, Vesna Marija
Pirtošek, Zvezdan
Šafarič, Riko
Human Gait Activity Recognition Machine Learning Methods
title Human Gait Activity Recognition Machine Learning Methods
title_full Human Gait Activity Recognition Machine Learning Methods
title_fullStr Human Gait Activity Recognition Machine Learning Methods
title_full_unstemmed Human Gait Activity Recognition Machine Learning Methods
title_short Human Gait Activity Recognition Machine Learning Methods
title_sort human gait activity recognition machine learning methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865094/
https://www.ncbi.nlm.nih.gov/pubmed/36679546
http://dx.doi.org/10.3390/s23020745
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