AUT LibraryAUT
View Item 
  •   Open Research
  • Faculties
  • Faculty of Health and Environmental Sciences
  • School of Sport and Recreation
  • View Item
  •   Open Research
  • Faculties
  • Faculty of Health and Environmental Sciences
  • School of Sport and Recreation
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

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

Russell, B; McDaid, A; Toscano, W; Hume, P
Thumbnail
View/Open
Journal article (1.663Mb)
Permanent link
http://hdl.handle.net/10292/13940
Metadata
Show full metadata
Abstract
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.
Keywords
Human activity recognition; Accelerometer; Inertial measurement unit; Wearable sensor; Artificial intelligence; Biomechanics; Deep learning; Convolutional neural network
Date
2021
Source
Sensors, 21(2), 654. doi:10.3390/s21020654
Item Type
Journal Article
Publisher
MDPI AG
DOI
10.3390/s21020654
Publisher's Version
https://www.mdpi.com/1424-8220/21/2/654
Rights Statement
© 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/).

Contact Us
  • Admin

Hosted by Tuwhera, an initiative of the Auckland University of Technology Library

 

 

Browse

Open ResearchTitlesAuthorsDateSchool of Sport and RecreationTitlesAuthorsDate

Alternative metrics

 

Statistics

For this itemFor all Open Research

Share

 
Follow @AUT_SC

Contact Us
  • Admin

Hosted by Tuwhera, an initiative of the Auckland University of Technology Library