  Background: Studies have shown the potential adverse health effects, ranging
from headaches to cardiovascular disease, associated with long-term negative
emotions and chronic stress. Since many indicators of stress are imperceptible
to observers, the early detection and intervention of stress remains a pressing
medical need. Physiological signals offer a non-invasive method of monitoring
emotions and are easily collected by smartwatches. Existing research primarily
focuses on developing generalized machine learning-based models for emotion
classification. Objective: We aim to study the differences between personalized
and generalized machine learning models for three-class emotion classification
(neutral, stress, and amusement) using wearable biosignal data. Methods: We
developed a convolutional encoder for the three-class emotion classification
problem using data from WESAD, a multimodal dataset with physiological signals
for 15 subjects. We compared the results between a subject-exclusive
generalized, subject-inclusive generalized, and personalized model. Results:
For the three-class classification problem, our personalized model achieved an
average accuracy of 95.06% and F1-score of 91.71, our subject-inclusive
generalized model achieved an average accuracy of 66.95% and F1-score of 42.50,
and our subject-exclusive generalized model achieved an average accuracy of
67.65% and F1-score of 43.05. Conclusions: Our results emphasize the need for
increased research in personalized emotion recognition models given that they
outperform generalized models in certain contexts. We also demonstrate that
personalized machine learning models for emotion classification are viable and
can achieve high performance.
