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

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aut.relation.issue16en_NZ
aut.relation.journalSensorsen_NZ
aut.relation.volume21en_NZ
aut.researcherHume, Patria
dc.contributor.authorRussell, Ben_NZ
dc.contributor.authorMcDaid, Aen_NZ
dc.contributor.authorToscano, Wen_NZ
dc.contributor.authorHume, Pen_NZ
dc.date.accessioned2021-09-01T03:37:12Z
dc.date.available2021-09-01T03:37:12Z
dc.date.copyright2021-08-02en_NZ
dc.date.issued2021-08-02en_NZ
dc.description.abstractAim: 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%). 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.en_NZ
dc.identifier.citationSensors 2021, 21, 5442. https://doi.org/10.3390/ s21165442
dc.identifier.doi10.3390/s21165442en_NZ
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14466
dc.relation.urihttps://www.mdpi.com/1424-8220/21/16/5442
dc.rights© 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/
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectFatigue; Cognitive; Physical; Executive decision-making; Psychophysiology; Artificial intelligence; Deep learning; Multi-day missions
dc.titlePredicting Fatigue in Long Duration Mountain Events With a Single Sensor and Deep Learning Modelen_NZ
dc.typeJournal Article
pubs.elements-id439315
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Health & Environmental Science
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Sports Performance Research Institute New Zealand
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Sports Performance Research Institute New Zealand/Sports Kinesiology Injury Prevention & Performance Research Group
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HS Sports & Recreation 2018 PBRF
pubs.organisational-data/AUT/PVC - Research & Innovation
pubs.organisational-data/AUT/zAcademic Progression
pubs.organisational-data/AUT/zAcademic Progression/Professor
pubs.organisational-data/AUT/zAcademic Progression/Professor/AP - Prof - Health and Environmental Sciences
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