ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals

TL;DR

ReLearn is a robust machine learning framework designed to accurately detect stress from multimodal physiological signals despite missing data, outperforming traditional methods by effectively handling data gaps during both training and inference.

Contribution

The paper introduces ReLearn, a novel framework that manages missing data and outliers in physiological signals for stress detection, enabling accurate predictions even with substantial data gaps.

Findings

Achieves up to 78% accuracy with missing data

Maintains 86.8% cross-validation accuracy with over 50% missing features

Outperforms simple data discarding approaches

Abstract

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.