The Role of ECG and Respiration in Predicting Quality of Experience

The field of user quality of experience (QoE) in multimedia communications has become increasingly important due to the widespread use of digital technology in our everyday lives. The ability to accurately predict user QoE by processing physiological signals has significant applications. This study proposes a machine learning (ML) approach for predicting user QoE using physiological signals. It focuses on perceived overall quality and perceived audio quality by processing electrocardiogram (ECG) and respiration signals from the SoPMD Dataset 2. The study evaluated various ML models on individual and fused modalities while implementing dimensionality reduction, feature selection, and hyperparameter tuning. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML models' outputs, which helped identify the features providing the most utility. The results show that the random forest model provided the best performance, with an F1-score of 87.91% for fusion data and 80.49% for ECG data in classifying perceived audio quality and overall quality, respectively. These results imply that physiological signals, such as ECG and respiration, hold great potential for predicting user QoE in multimedia experiences.