@inproceedings{976272c565324e8f8e721ffd74b7048b,
title = "Modeling User Experience of Large Display-Based Interaction with Physiological Indicators and Machine Learning Techniques",
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.",
keywords = "Human-computer interaction, Large displays, Machine learning, Physiological indicators, User experience",
author = "Da Tao and Yuzhuo Wu and Xiaoting Ma and Mingfu Qin",
note = "Funding Information: This work was partly supported by the National Natural Science Foundation of China (grant no. 32271130 and 72101161), the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province (grant no. 2024B1515020007) and the Foundation of Shenzhen Science and Technology Innovation Committee (grant no. JCYJ20230808105219038). Publisher Copyright: {\textcopyright} 2024 Beijing KeCui Man-Machine-Environment System Engineering Technology Research Academy; 24th Conference on Man-Machine-Environment System Engineering, MMESE 2024 ; Conference date: 18-10-2024 Through 20-10-2024",
year = "2024",
month = sep,
day = "28",
doi = "10.1007/978-981-97-7139-4_87",
language = "English",
isbn = "9789819771387",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Singapore",
pages = "643--650",
editor = "Shengzhao Long and Dhillon, {Balbir S.} and Long Ye",
booktitle = "Man-Machine-Environment System Engineering",
address = "Singapore",
edition = "1st",
url = "https://link.springer.com/book/10.1007/978-981-97-7139-4",
}