Machine Learning Based Skill-Level Classification for Personal Mobility Devices Using only Operational Characteristics

Yifan Huang, Taiga Mori, Udara E. Manawadu, Mitsuhiro Kamezaki, Tatsuya Ishihara, Masahiro Nakano, Kohjun Koshiji, Naoki Higo, Toshimitsu Tubaki, Shigeki Sugano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Some electric-powered wheelchairs are recently redefined as personal mobility devices. Their users are not only elderly or handicapped people, but also passengers with large baggage or pedestrians going from station to destination, i.e., last-mile transport. Consequently, people with different operation skills and expectations on personal mobility would become new users of this kind of devices. Safe and comfort travel in human co-existing environment such as sidewalks and airports is a social expectation for personal mobility. In order to realize this, understanding the operation skill of each user by a practical and simple method is essential. This paper thus introduced a skill level classification method by machine learning using only joystick data as input. In order to determine the number of skill level clusters, basic 26 features of joystick operation data are used for unsupervised clustering (single-linkage). We then made evaluation indexes by using speed, speed control, and direction control. For a five-level classification by using gradient boosting as supervised learning, we achieved a 67% accuracy (tolerance: 0) and a 98% accuracy (tolerance: 1). Further analysis of the feature importance of gradient boosting revealed key features to a good operation. Results also show that skill level differed among people with different driving experiences.

Original languageEnglish
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5469-5476
Number of pages8
ISBN (Electronic)9781538680940
DOIs
Publication statusPublished - 2018 Dec 27
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain
Duration: 2018 Oct 12018 Oct 5

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
CountrySpain
CityMadrid
Period18/10/118/10/5

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Huang, Y., Mori, T., Manawadu, U. E., Kamezaki, M., Ishihara, T., Nakano, M., Koshiji, K., Higo, N., Tubaki, T., & Sugano, S. (2018). Machine Learning Based Skill-Level Classification for Personal Mobility Devices Using only Operational Characteristics. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 (pp. 5469-5476). [8593578] (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2018.8593578