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

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aut.relation.articlenumber654en_NZ
aut.relation.issue2en_NZ
aut.relation.journalSensorsen_NZ
aut.relation.volume21en_NZ
aut.researcherHume, Patria
dc.contributor.authorRussell, Ben_NZ
dc.contributor.authorMcDaid, Aen_NZ
dc.contributor.authorToscano, Wen_NZ
dc.contributor.authorHume, Pen_NZ
dc.date.accessioned2021-01-25T21:14:59Z
dc.date.available2021-01-25T21:14:59Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractGoal: 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.en_NZ
dc.identifier.citationSensors, 21(2), 654. doi:10.3390/s21020654
dc.identifier.doi10.3390/s21020654en_NZ
dc.identifier.issn1424-8220en_NZ
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/13940
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/1424-8220/21/2/654
dc.rights© 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 (https:// creativecommons.org/licenses/by/ 4.0/).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectHuman activity recognition; Accelerometer; Inertial measurement unit; Wearable sensor; Artificial intelligence; Biomechanics; Deep learning; Convolutional neural network
dc.titleMoving the Lab Into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environmentsen_NZ
dc.typeJournal Article
pubs.elements-id397339
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Health & Environmental Science
pubs.organisational-data/AUT/Health & Environmental Science/SPRINZ
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HS Sports & Recreation 2018 PBRF
pubs.organisational-data/AUT/PVC - Research & Innovation
pubs.organisational-data/AUT/zTest
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