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Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments

Goal: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model. Methods: The protocol generated an unsupervised labelled dataset of various...

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Autores principales: Russell, Brian, McDaid, Andrew, Toscano, William, Hume, Patria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832872/
https://www.ncbi.nlm.nih.gov/pubmed/33477828
http://dx.doi.org/10.3390/s21020654
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author Russell, Brian
McDaid, Andrew
Toscano, William
Hume, Patria
author_facet Russell, Brian
McDaid, Andrew
Toscano, William
Hume, Patria
author_sort Russell, Brian
collection PubMed
description Goal: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model. Methods: The protocol generated an unsupervised labelled dataset of various long-term field-based activities including run, walk, stand, lay and obstacle climb. Activity was voluntary so transitions could not be determined a priori. Terrain variations included slope, crossing rivers, obstacles and surfaces including road, gravel, clay, mud, long grass and rough track. Fatigue levels were modulated between rested to physical exhaustion. The dataset was used to train a deep learning convolutional neural network (CNN) capable of being deployed on battery powered devices. The human activity recognition results were compared to a lab-based dataset with 1,098,204 samples and six features, uniform smooth surfaces, non-fatigued supervised participants and activity labelling defined by the protocol. Results: The trail run dataset had 3,829,759 samples with five features. The repetitive activities and single instance activities required hyper parameter tuning to reach an overall accuracy 0.978 with a minimum class precision for the one-off activity (climbing gate) of 0.802. Conclusion: The experimental results showed that the CNN deep learning model performed well with terrain and fatigue variations compared to the lab equivalents (accuracy 97.8% vs. 97.7% for trail vs. lab). Significance: To the authors knowledge this study demonstrated the first successful human activity recognition (HAR) in a mountain environment. A robust and repeatable protocol was developed to generate a validated trail running dataset when there were no observers present and activity types changed on a voluntary basis across variations in terrain surface and both cognitive and physical fatigue levels.
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spelling pubmed-78328722021-01-26 Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments Russell, Brian McDaid, Andrew Toscano, William Hume, Patria Sensors (Basel) Article Goal: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model. Methods: The protocol generated an unsupervised labelled dataset of various long-term field-based activities including run, walk, stand, lay and obstacle climb. Activity was voluntary so transitions could not be determined a priori. Terrain variations included slope, crossing rivers, obstacles and surfaces including road, gravel, clay, mud, long grass and rough track. Fatigue levels were modulated between rested to physical exhaustion. The dataset was used to train a deep learning convolutional neural network (CNN) capable of being deployed on battery powered devices. The human activity recognition results were compared to a lab-based dataset with 1,098,204 samples and six features, uniform smooth surfaces, non-fatigued supervised participants and activity labelling defined by the protocol. Results: The trail run dataset had 3,829,759 samples with five features. The repetitive activities and single instance activities required hyper parameter tuning to reach an overall accuracy 0.978 with a minimum class precision for the one-off activity (climbing gate) of 0.802. Conclusion: The experimental results showed that the CNN deep learning model performed well with terrain and fatigue variations compared to the lab equivalents (accuracy 97.8% vs. 97.7% for trail vs. lab). Significance: To the authors knowledge this study demonstrated the first successful human activity recognition (HAR) in a mountain environment. A robust and repeatable protocol was developed to generate a validated trail running dataset when there were no observers present and activity types changed on a voluntary basis across variations in terrain surface and both cognitive and physical fatigue levels. MDPI 2021-01-19 /pmc/articles/PMC7832872/ /pubmed/33477828 http://dx.doi.org/10.3390/s21020654 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Russell, Brian
McDaid, Andrew
Toscano, William
Hume, Patria
Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments
title Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments
title_full Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments
title_fullStr Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments
title_full_unstemmed Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments
title_short Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments
title_sort moving the lab into the mountains: a pilot study of human activity recognition in unstructured environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832872/
https://www.ncbi.nlm.nih.gov/pubmed/33477828
http://dx.doi.org/10.3390/s21020654
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