DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data
Creators
- 1. IBM Research – Zurich, Säumerstrasse 4, 8803 Rüschlikon, Switzerland
- 2. IBM Denmark –KONGEVEJEN 495B HOLTE, 2840, Denmark
- 3. IBMResearch – Zurich, Säumerstrasse 4, 8803 Rüschlikon, Switzerland
Description
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.
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