Longitudinal Classification of Mental Effort Using Electrodermal
Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor
Abstract
Recent studies show that physiological data can detect changes in mental
effort, making way for the development of wearable sensors to monitor
mental effort in school, work, and at home. We have yet to explore how
such a device would work with a single participant over an extended time
duration. We used a longitudinal case study design with
~38 hours of data to explore the efficacy of
electrodermal activity, skin temperature, and heart rate for classifying
mental effort. We utilized a 2-state Markov switching regression model
to understand the efficacy of these physiological measures for
predicting self-reported mental effort during logged activities. On
average, a model with state-dependent relationships predicted within one
unit of reported mental effort (training RMSE = 0.4, testing RMSE =
0.7). This automated sensing of mental effort can have applications in
various domains including student engagement detection and cognitive
state assessment in drivers, pilots, and caregivers.