This study aims to examine the effects of cognitive fatigue on heart rate variability and skin conductance and develop a machine learning model.
We hypothesize that increased cognitive fatigue would vary as a function of heart rate variability and skin conductance. A machine learning model will be developed that predicts cognitive fatigue through these physiological responses (Lee et al., 2021). Individual differences (i.e., age, gender, caffeine and food intake, body mass index, skin temperature, sleep quality, baseline physiology and behavioural performance) will be examined and accounted for.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
162
5-min urban park video clip (Presented on a TV)
20-min 2-back task (Presented on a computer)
Cultural Science Innovations, Nanyang Technological University
Singapore, Singapore
RECRUITING2-Back Task (Change)
Change in Accuracy over time
Time frame: during fatigue manipulation
2-Back Task (Change)
Change in Reaction Time over time
Time frame: during fatigue manipulation
Fatigue State Questionnaire
Fatigue State Questionnaire Score
Time frame: up to 5 mins after fatigue manipulation
Electrocardiograph (Change)
Change in Heart Rate Variability over time
Time frame: during fatigue manipulation
Electrodermal Activity (Change)
Change in Skin Conductance Level over time
Time frame: during fatigue manipulation
Electrodermal Activity (Change)
Change in Skin Conductance Response over time
Time frame: during fatigue manipulation
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