  In this work we perform a study of various unsupervised methods to identify
mental stress in firefighter trainees based on unlabeled heart rate variability
data. We collect RR interval time series data from nearly 100 firefighter
trainees that participated in a drill. We explore and compare three methods in
order to perform unsupervised stress detection: 1) traditional K-Means
clustering with engineered time and frequency domain features 2) convolutional
autoencoders and 3) long short-term memory (LSTM) autoencoders, both trained on
the raw RRI measurements combined with DBSCAN clustering and
K-Nearest-Neighbors classification. We demonstrate that K-Means combined with
engineered features is unable to capture meaningful structure within the data.
On the other hand, convolutional and LSTM autoencoders tend to extract varying
structure from the data pointing to different clusters with different sizes of
clusters. We attempt at identifying the true stressed and normal clusters using
the HRV markers of mental stress reported in the literature. We demonstrate
that the clusters produced by the convolutional autoencoders consistently and
successfully stratify stressed versus normal samples, as validated by several
established physiological stress markers such as RMSSD, Max-HR, Mean-HR and
LF-HF ratio.
