Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Memorable Vibration Pattern Design Based on Writing Pattern

Authors
Wong, Zi YingYoo, YongjaeKim, Sang-Youn
Issue Date
Nov-2024
Publisher
Springer Science and Business Media Deutschland GmbH
Keywords
Digits; Memorable Vibration Pattern; Writing Pattern
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , v.14768 LNCS, pp 449 - 463
Pages
15
Indexed
SCOPUS
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
14768 LNCS
Start Page
449
End Page
463
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/121192
DOI
10.1007/978-3-031-70058-3_37
ISSN
0302-9743
1611-3349
Abstract
In this paper, we presented memorable vibration patterns representing digit 0 to 9, which were designed based on writing patterns. Based on the collection of 50 participants’ handwriting pattern of 10 digits we gathered, we designed two different types of vibration patterns to generate the digits: vibrotactile flows and discrete vibrotactile simulations. In the user study, we evaluated identifiability and learnability of the patterns we generated. First, participants successfully identified 69.4% of vibrotactile flow patterns and 77.5% with discrete vibrotactile simulations in their first session of 30 trials without training. The average recognition rate in their last session 30 trials increased to 83.6% for vibrotactile flows and 91.1% for discrete vibrotactile simulations after two sessions (60 trials), shows the ease of learning the vibration pattern. We also observed a lasting learning effect of both types of vibrotactile patterns in a delayed recall test was conducted 72–96 h after the first user study – 90.0% success rate for vibrotactile flows and 91.4% for discrete vibrations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Files in This Item
Go to Link
Appears in
Collections
COLLEGE OF COMPUTING > DEPARTMENT OF ARTIFICIAL INTELLIGENCE > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Yoo, Yongjae photo

Yoo, Yongjae
ERICA 소프트웨어융합대학 (DEPARTMENT OF ARTIFICIAL INTELLIGENCE)
Read more

Altmetrics

Total Views & Downloads

BROWSE