  Continuous and multimodal stress detection has been performed recently
through wearable devices and machine learning algorithms. However, a well-known
and important challenge of working on physiological signals recorded by
conventional monitoring devices is missing data due to sensors insufficient
contact and interference by other equipment. This challenge becomes more
problematic when the user/patient is mentally or physically active or stressed
because of more frequent conscious or subconscious movements. In this paper, we
propose ReLearn, a robust machine learning framework for stress detection from
biomarkers extracted from multimodal physiological signals. ReLearn effectively
copes with missing data and outliers both at training and inference phases.
ReLearn, composed of machine learning models for feature selection, outlier
detection, data imputation, and classification, allows us to classify all
samples, including those with missing values at inference. In particular,
according to our experiments and stress database, while by discarding all
missing data, as a simplistic yet common approach, no prediction can be made
for 34% of the data at inference, our approach can achieve accurate
predictions, as high as 78%, for missing samples. Also, our experiments show
that the proposed framework obtains a cross-validation accuracy of 86.8% even
if more than 50% of samples within the features are missing.
