Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model Article Swipe
Brian Russell
,
Andrew McDaid
,
William B. Toscano
,
Patria Hume
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21165442
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21165442
Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 min) and cognitive (~10 min) fatigue on one healthy participant. The physical load was a 3.8 km, 200 m vertical gain, trail run, with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery included the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R2 0.71) and Jump Height (R2 0.78) were the most sensitive while the other tests were less sensitive (R2 values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). The best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for ‘walk up’ (MAE200 12.5%), and range of absolute error for ‘run down’ (RAE200 16.7%). Conclusions: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol, including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21165442
- https://www.mdpi.com/1424-8220/21/16/5442/pdf?version=1628822637
- OA Status
- gold
- Cited By
- 15
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3193201575
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3193201575Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s21165442Digital Object Identifier
- Title
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Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-08-12Full publication date if available
- Authors
-
Brian Russell, Andrew McDaid, William B. Toscano, Patria HumeList of authors in order
- Landing page
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https://doi.org/10.3390/s21165442Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/21/16/5442/pdf?version=1628822637Direct 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
- OA URL
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https://www.mdpi.com/1424-8220/21/16/5442/pdf?version=1628822637Direct OA link when available
- Concepts
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Stroop effect, Battery (electricity), Simulation, Standard deviation, Cognition, Computer science, Acceleration, Psychology, Statistics, Mathematics, Classical mechanics, Physics, Quantum mechanics, Power (physics), NeuroscienceTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 4, 2024: 1, 2023: 8, 2022: 2Per-year citation counts (last 5 years)
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76Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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