TY - GEN
T1 - BiLSTM-based Quality of Experience Prediction using Physiological Signals
AU - Vijayakumar, Sowmya
AU - Flynn, Ronan
AU - Corcoran, Peter
AU - Murray, Niall
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - ECG
KW - LSTM
KW - QoE
KW - deep learning
KW - physiological signals
KW - quality of experience
KW - respiration
KW - sense of presence
UR - https://www.scopus.com/pages/publications/85141713322
U2 - 10.1109/QoMEX55416.2022.9900877
DO - 10.1109/QoMEX55416.2022.9900877
M3 - Conference Publication
T3 - 2022 14th International Conference on Quality of Multimedia Experience, QoMEX 2022
BT - 2022 14th International Conference on Quality of Multimedia Experience, QoMEX 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Quality of Multimedia Experience, QoMEX 2022
Y2 - 5 September 2022 through 7 September 2022
ER -