ArticleSensors (Basel, Switzerland)2021
Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- The fatigue status feature of bicycle movement based on deep learning and signal processing technology.Scientific reports · 2025Article
- Accuracy of smartwatches in predicting distance running performance.Frontiers in sports and active living · 2025Article
- The value of a spaceflight clinical decision support system for earth-independent medical operations.NPJ microgravity · 2023Review
- Sensors for Human Physical Behaviour Monitoring.Sensors (Basel, Switzerland) · 2023Article
- A single 1,500 m freestyle at maximal speed decreases cognitive function in athletes.Frontiers in psychology · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run.
methodsA 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).
resultsWhen the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R
conclusionsWe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.