Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
Doi
Abstract
Stress detection is an active area of research with important implications for personal, occupational, and social health.
Most modern approaches use features computed from multiple sensor modalities, i.e., grouping different types of
data from multiple sources for processing. These include electrocardiogram, electrodermal activity, electromyogram,
skin temperature, respiration, accelerometer data, etc. Also, traditional machine learning algorithms (decision tree,
discriminant analysis, support vector machine, etc.) or fully-connected neural networks are mostly used. Using these
methods requires large amounts of data. Researchers are considering different approaches to personalization or
generalization of models relative to subjects, namely subject-independent and subject-dependent (initially personal or
adapted) models. The aim of the presented work is to develop a method for detecting stress based on heart rate variability
data, taking into account the process of personalization of neural networks. The use of a convolutional neural network
is proposed. The dependence of accuracy on the length of the input signal is studied. The dependence of accuracy on
the data dimensionality reduction layer (one-dimensional convolutional layer, maximizing and averaging pooling) used
in the network is also considered. The importance of personalizing models is demonstrated to significantly increase the
accuracy of models of specific subjects. It is shown that the proposed method, based on 60 intervals between heartbeats,
makes it possible to binary determine whether a person is under stress. Personalization allowed increasing the accuracy
from 91.8 % to 98.9 ± 2.6 %. The F1-score value increased from 0.907 to 0.983 ± 0.038. The proposed personalized
networks can be used in systems for monitoring the functional state of a person. They can also be used as part of a system
that grants or restricts access to private resources based on whether a person is currently at rest