  Fatigue is a loss in cognitive or physical performance due to physiological
factors such as insufficient sleep, long work hours, stress, and physical
exertion. It adversely affects the human body and can slow reaction times,
reduce attention, and limit short-term memory. Hence, there is a need to
monitor a person's state to avoid extreme fatigue conditions that can result in
physiological complications. However, tools to understand and assess fatigue
are minimal. This paper primarily focuses on building an experimental setup
that induces cognitive fatigue (CF) and physical fatigue (PF) through multiple
cognitive and physical tasks while simultaneously recording physiological data.
First, we built a prototype sensor suit embedded with numerous physiological
sensors for easy use during data collection. Second, participants'
self-reported visual analog scores (VAS) are reported after each task to
confirm fatigue induction. Finally, an evaluation system is built that utilizes
machine learning (ML) models to detect states of CF and PF from sensor data,
thus providing an objective measure. Our methods beat state-of-the-art
approaches, where Random Forest performs the best in detecting PF with an
accuracy of 80.5% while correctly predicting the true PF condition 88% of the
time. On the other hand, the long short-term memory (LSTM) recurrent neural
network produces the best results in detecting CF in the subjects (with 84.1%
accuracy, 0.9 recall).
