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Article

Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model

1
Sports Performance Research Institute, Auckland University of Technology, Auckland 0632, New Zealand
2
National Aeronautics and Space Administration, Ames Research Center, Moffett Field, CA 94043, USA
3
Department of Mechanical Engineering, University of Auckland, Auckland 1142, New Zealand
*
Author to whom correspondence should be addressed.
Academic Editor: Massimo Sacchetti
Sensors 2021, 21(16), 5442; https://doi.org/10.3390/s21165442
Received: 14 May 2021 / Revised: 31 July 2021 / Accepted: 7 August 2021 / Published: 12 August 2021
(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)
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. View Full-Text
Keywords: fatigue; cognitive; physical; executive decision-making; psychophysiology; artificial intelligence; deep learning; multi-day missions fatigue; cognitive; physical; executive decision-making; psychophysiology; artificial intelligence; deep learning; multi-day missions
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MDPI and ACS Style

Russell, B.; McDaid, A.; Toscano, W.; Hume, P. Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model. Sensors 2021, 21, 5442. https://doi.org/10.3390/s21165442

AMA Style

Russell B, McDaid A, Toscano W, Hume P. Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model. Sensors. 2021; 21(16):5442. https://doi.org/10.3390/s21165442

Chicago/Turabian Style

Russell, Brian, Andrew McDaid, William Toscano, and Patria Hume. 2021. "Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model" Sensors 21, no. 16: 5442. https://doi.org/10.3390/s21165442

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