Haptic Dataset Augmentation with Subjective QoE Labels using Conditional Generative Adversarial Network

Zican Wang, Xiao Xu, Dong Yang, Zhenyu Wang, Sarah Shtaierman, Eckehard Steinbach

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

Abstract

This paper proposes a novel Generative Adversarial Network (GAN)-based strategy to augment subjective haptic Quality of Experience (QoE) datasets for bilateral teleoperation with haptic feedback without conducting time-consuming subjective experiments. In our previous work, we proposed a multi-assessment fusion approach to predict subjective haptic quality using a collection of objective metrics. This method requires a sufficiently large haptic dataset with QoE labels. The proposed generative approach automatically expands the existing haptic quality dataset by combining a modified conditional GAN (CGAN) and Style GAN (StyleGAN) architecture. The most important feature of our method is that it learns from the labeled training data and focuses on synthesizing signals with artifacts according to new input labels containing the QoE score, time delay, control method, and data reduction information. Extensive experiments are conducted to validate the suitability of the expanded dataset. The results show that our approach is able to generate new data, which match the label and signal distribution of the original data with categorical rank and linear correlation of over 0.85.

Original languageEnglish
Title of host publication2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5072-5078
Number of pages7
ISBN (Electronic)9781665491907
DOIs
StatePublished - 2023
Event2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 - Detroit, United States
Duration: 1 Oct 20235 Oct 2023

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Country/TerritoryUnited States
CityDetroit
Period1/10/235/10/23

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