BiLSTM-based Quality of Experience Prediction using Physiological Signals

Sowmya Vijayakumar, Ronan Flynn, Peter Corcoran, Niall Murray

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2022 14th International Conference on Quality of Multimedia Experience, QoMEX 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665487948
DOIs
Publication statusPublished - 2022
Event14th International Conference on Quality of Multimedia Experience, QoMEX 2022 - Lippstadt, Germany
Duration: 5 Sep 20227 Sep 2022

Publication series

Name2022 14th International Conference on Quality of Multimedia Experience, QoMEX 2022

Conference

Conference14th International Conference on Quality of Multimedia Experience, QoMEX 2022
Country/TerritoryGermany
CityLippstadt
Period5/09/227/09/22

Keywords

  • ECG
  • LSTM
  • QoE
  • deep learning
  • physiological signals
  • quality of experience
  • respiration
  • sense of presence

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