BiLSTM-based Quality of Experience Prediction using Physiological Signals

This paper presents an evaluation of a deep learning (DL) model to predict the user quality of experience (QoE) from physiological signals using a publicly available multimodal dataset, SoPMD. The subjective scores related to QoE factors, namely, perceptual video quality, immersion level, surrounding awareness, interest in video content and audio content are evaluated. A DL model, Bidirectional Long-short-term memory (BiLSTM), is trained on the fusion of electrocardiogram (ECG) and respiration features to predict subjective scores for the five QoE factors. This study achieved classification accuracies and Fl-scores ranging between 58% and 67% for different QoE factors. The results of the BiLSTM model were compared with machine learning techniques. The experimental results demonstrated that the proposed BiLSTM network has the potential to predict QoE from physiological signals.