Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments Article Swipe
Brian Russell
,
Andrew McDaid
,
William B. Toscano
,
Patria Hume
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21020654
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21020654
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.
Related Topics
Concepts
Artificial intelligence
Terrain
Climbing
Activity recognition
Convolutional neural network
Computer science
Deep learning
Accelerometer
Machine learning
Protocol (science)
Climb
Simulation
Engineering
Cartography
Medicine
Alternative medicine
Operating system
Pathology
Aerospace engineering
Geography
Structural engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21020654
- https://www.mdpi.com/1424-8220/21/2/654/pdf?version=1611043801
- OA Status
- gold
- Cited By
- 17
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3125481752
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3125481752Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21020654Digital Object Identifier
- Title
-
Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured EnvironmentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-19Full publication date if available
- Authors
-
Brian Russell, Andrew McDaid, William B. Toscano, Patria HumeList of authors in order
- Landing page
-
https://doi.org/10.3390/s21020654Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/2/654/pdf?version=1611043801Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/21/2/654/pdf?version=1611043801Direct OA link when available
- Concepts
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Artificial intelligence, Terrain, Climbing, Activity recognition, Convolutional neural network, Computer science, Deep learning, Accelerometer, Machine learning, Protocol (science), Climb, Simulation, Engineering, Cartography, Medicine, Alternative medicine, Operating system, Pathology, Aerospace engineering, Geography, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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17Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 4, 2023: 5, 2022: 3, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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