  Continuous high perceived workload has a negative impact on the individual's
well-being. Prior works focused on detecting the workload with medical-grade
wearable systems in the restricted settings, and the effect of applying deep
learning techniques for perceived workload detection in the wild settings is
not investigated. We present an unobtrusive, comfortable, pervasive and
affordable Long Short-Term Memory Network based continuous workload monitoring
system based on a smartwatch application that monitors the perceived workload
of individuals in the wild. We make use of modern consumer-grade smartwatches.
We have recorded physiological data from daily life with perceived workload
questionnaires from subjects in their real-life environments over a month. The
model was trained and evaluated with the daily-life physiological data coming
from different days which makes it robust to daily changes in the heart rate
variability, that we use with accelerometer features to asses low and high
workload. Our system has the capability of removing motion-related artifacts
and detecting perceived workload by using traditional and deep classifiers. We
discussed the problems related to in the wild applications with the
consumer-grade smartwatches. We showed that Long Short-Term Memory Network
outperforms traditional classifiers on discrimination of low and high workload
with smartwatches in the wild.
