Modeling User Experience of Large Display-Based Interaction with Physiological Indicators and Machine Learning Techniques

Da Tao, Yuzhuo Wu, Xiaoting Ma, Mingfu Qin*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

While large display-based interaction has gained increasing popularity, its user experience is usually assessed by self-reporting measure and subject to bias. This study proposed an alternative approach by modeling user experience of large display-based interaction with physiological indicators and machine learning techniques. Twenty-four participants attended an experiment where they were asked to interact with a large display under varied body postures and interaction distances. Both self-reporting user experience (i.e., perceived usability and workload) and electromyography measures during task performance were collected and trained by three different machine learning models, out of which the best model could predict over 70% of the variance of user experience. Several electromyography measures were identified as effective indicators for user experience. The study demonstrates the feasibility of modeling user experience with physiological indicators and machine learning techniques in large display-based interaction.

Original languageEnglish
Title of host publicationMan-Machine-Environment System Engineering
Subtitle of host publicationProceedings of the 24th Conference on MMESE
EditorsShengzhao Long, Balbir S. Dhillon, Long Ye
PublisherSpringer Singapore
Pages643-650
Number of pages8
Edition1st
ISBN (Electronic)9789819771394
ISBN (Print)9789819771387
DOIs
Publication statusPublished - 28 Sept 2024
Event24th Conference on Man-Machine-Environment System Engineering, MMESE 2024 - Beijing, China
Duration: 18 Oct 202420 Oct 2024
https://link.springer.com/book/10.1007/978-981-97-7139-4

Publication series

NameLecture Notes in Electrical Engineering
Volume1256
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119
NameMMESE: International Conference on Man-Machine-Environment System Engineering

Conference

Conference24th Conference on Man-Machine-Environment System Engineering, MMESE 2024
Country/TerritoryChina
CityBeijing
Period18/10/2420/10/24
Internet address

Scopus Subject Areas

  • Industrial and Manufacturing Engineering

User-Defined Keywords

  • Human-computer interaction
  • Large displays
  • Machine learning
  • Physiological indicators
  • User experience

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