Key result
A BiLSTM model trained on ECG and respiration features predicted user quality of experience factors with classification accuracies and F1-scores ranging between 58% and 67%.
Why the study?
Does a BiLSTM deep learning model accurately predict user quality of experience from physiological signals compared to traditional machine learning techniques?
Does a BiLSTM deep learning model accurately predict user quality of experience from physiological signals compared to traditional machine learning techniques?
A BiLSTM deep learning model utilizing ECG and respiration features demonstrates potential in predicting user quality of experience with moderate accuracy.
Answers are grounded in this paper and cite their sources.
Moderate accuracy limits clinical adoption; leaves open whether BiLSTM advances QoE prediction research pending validation.
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.
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Vijayakumar et al. (2022) studied this question. BiLSTM model using ECG and respiration features vs. Machine learning techniques was evaluated on Prediction of subjective scores for five Quality of Experience (QoE) factors (Classification accuracies and F1-scores ranging between 58% and 67%). A BiLSTM model trained on ECG and respiration features predicted user quality of experience factors with classification accuracies and F1-scores ranging between 58% and 67%.
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