Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 31 July 2021 / Approved: 2 August 2021 / Online: 2 August 2021 (22:55:32 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Sensors 2021, 21, 5442
DOI: 10.3390/s21165442

Abstract

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 minutes) and cognitive (~10 minutes) fatigue on one healthy participant. 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 including 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). 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%). Conclusion: 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.

Keywords

Fatigue; cognitive; physical; executive decision making; psychophysiology; artificial intelligence; deep learning; multi-day missions

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