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

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.

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).

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%).

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.