Personalization of Stress Mobile Sensing using Self-Supervised Learning
Tanvir Islam Peter Washington
Stress is widely recognized as a major contributor to a variety of health
issues. Stress prediction using biosignal data recorded by wearables is a key
area of study in mobile sensing research because real-time stress prediction
can enable digital interventions to immediately react at the onset of stress,
helping to avoid many psychological and physiological symptoms such as heart
rhythm irregularities. Electrodermal activity (EDA) is often used to measure
stress. However, major challenges with the prediction of stress using machine
learning include the subjectivity and sparseness of the labels, a large feature
space, relatively few labels, and a complex nonlinear and subjective
relationship between the features and outcomes. To tackle these issues, we
examine the use of model personalization: training a separate stress prediction
model for each user. To allow the neural network to learn the temporal dynamics
of each individual's baseline biosignal patterns, thus enabling personalization
with very few labels, we pre-train a 1-dimensional convolutional neural network
(CNN) using self-supervised learning (SSL). We evaluate our method using the
Wearable Stress and Affect prediction (WESAD) dataset. We fine-tune the
pre-trained networks to the stress prediction task and compare against
equivalent models without any self-supervised pre-training. We discover that
embeddings learned using our pre-training method outperform supervised
baselines with significantly fewer labeled data points: the models trained with
SSL require less than 30% of the labels to reach equivalent performance without
personalized SSL. This personalized learning method can enable precision health
systems which are tailored to each subject and require few annotations by the
end user, thus allowing for the mobile sensing of increasingly complex,
heterogeneous, and subjective outcomes such as stress.