Volume 18 Number 4 (Nov. 2023)
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JSW 2023 Vol 18(4): 218-227
doi: 10.17706/jsw.18.4.218-227

Extensions of Exercise and Performance Learning Assistant System for Usability Improvements

Irin Tri Anggraini1*, Nobuo Funabiki1*, Wan-Chia Huang2, Cheng-Liang Shih2, Chih-Peng Fan2

1Department of Information and Communication Systems, Okayama University, Okayama, Japan.
2Department of Electrical Engineering National Chung Hsing University, Taichung, Taiwan.

Abstract—To support exercise or performance learning at home, we have developed an Exercise and Performance Learning Assistant System (EPLAS) as a web application system using Node.js. EPLAS provides the model performance video from the instructor to be followed, and the feedback function of rating the user performance using OpenPose. Currently, Yoga performances are implemented in EPLAS as a popular exercise. However, the current implementation of EPLAS is not sufficient in making the user immersed in practicing the performance. Besides, it is not friendly for a user to control the exercise from a distant place. In this paper, we implement two extensions of EPLAS for improving usability by adopting the mood-elevating background using scenery videos of tourist spots and the remote-control function using Quick Response (QR) code. For evaluations, we prepared 10 Yoga videos and 10 tourist videos for the extended EPLAS and asked 20 students in Okayama University, Japan, to use them. We confirmed the effectiveness of the questionnaire results.

Index Terms—EPLAS, QR code, yoga, python, entertainment


Cite: Irin Tri Anggraini, Nobuo Funabiki, Wan-Chia Huang, Cheng-Liang Shih, Chih-Peng Fan, "Extensions of Exercise and Performance Learning Assistant System for Usability Improvements ," Journal of Software vol. 18, no. 4, pp. 218-227, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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