Design of data collection and analysis method for a pleasant and safe user experience of personal mobility device

Sebastian Eio, Jo Yu Kuo, Chun Hsien Chen, Pai Zheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In recent years, the idea of personal mobility devices (PMD) has gained prominence globally for different contexts, for diverse types and extent of uses. The advantages of owning a PMD allows users to cover the short distance in between stops where they have access to long distance transportation, establishing a full end to end transport system for many. The rise in usage of PMDs also came along the rise in accidents. One of the reasons that could result in this phenomenon is the lack of calibration of PMD towards how users use it. Currently, most user experience (UX) methodologies are based on subjective questionnaires rather than by objective quantitative data. While there exists a few that studies wheelchair and electronic bicycles, UX concerning this specific device is a field not many studies have delved into. Therefore, in this project, we seek to propose a data-driven model to explore electronic scooter user’s riding profile based on psychophysiological data such as galvanic skin response (GSR) and kinematics data such as the speed and acceleration. Upon retrieving the stress status of the user when he or she is riding, the dataset undergoes a data analysis pipeline that cleans, process and analyse data with Random Forest machine learning algorithms. With the ability to create customised profiles, the model can be adopted to serve the needs of PMD sharing service stakeholders or PMD design companies to ensure good user experience for their customers in the future.

Original languageEnglish
Title of host publicationTransdisciplinary Engineering for Complex Socio-technical Systems - Proceedings of the 26th ISTE International Conference on Transdisciplinary Engineering
EditorsKazuo Hiekata, `Brian Moser, Brian Moser, Masato Inoue, Josip Stjepandic, Nel Wognum
PublisherIOS Press BV
Pages224-233
Number of pages10
ISBN (Electronic)9781614994398
DOIs
Publication statusPublished - 7 Oct 2019
Externally publishedYes
Event26th ISTE International Conference on Transdisciplinary Engineering, TE 2019 - Tokyo, Japan
Duration: 30 Jul 20191 Aug 2019

Publication series

NameAdvances in Transdisciplinary Engineering
Volume10

Conference

Conference26th ISTE International Conference on Transdisciplinary Engineering, TE 2019
Country/TerritoryJapan
CityTokyo
Period30/07/191/08/19

Keywords

  • Data-driven
  • Personal Mobility Devices (PMD)
  • Personalised product design
  • Psychophysiological model
  • User experience (UX)

ASJC Scopus subject areas

  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Software
  • Algebra and Number Theory
  • Strategy and Management

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