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 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.
Highlights
Levels of performance may be modulated by physical load, sleep, nutrition, and psychological factors based on mission duration, pain, levels of perceived exertion [14,15,16,17], intensity, and time on task [18]
Physical load was provided by a trail run (3.8 km, 200 m vertical gain), and cognitive load was provided by 10 min Multi Attribute Test Battery (MATB) [53] (Figure 1)
A protocol for cognitive and physical fatigue was performed in the field, with voluntary activity selection and voluntary pacing over various terrain slopes and surfaces
Summary
Why We Need to Measure Physical and Cognitive Fatigue in the Field. Measures of physical and cognitive fatigue are needed in the field to improve performance and help improve safe participation in outdoor environments. Physiological and cognitive fatigue in field environments directly affects performance as a person modulates decisions based on contextual input to maintain resources [1]. Various fields where operational safety is related to fatigue have been investigated, including pilots [2,3], motor vehicle drivers [4,5,6,7,8,9], firefighters [10,11], and shift workers [12]. Physical fatigue relates to reduced force, endurance, level of effort, strength, speed, and coordination [13]. Hill [19] won the Noble prize for his work on skeletal muscle and maximum oxygen uptake
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