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

DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data