Machine Learning Based Skill-Level Classification for Personal Mobility Devices Using Only Operational Characteristics
| Autor: | Toshimitsu Tubaki, Kohjun Koshiji, Yifan Huang, Higo Naoki, Udara E. Manawadu, Tatsuya Ishihara, Mitsuhiro Kamezaki, Masahiro Nakano, Taiga Mori, Shigeki Sugano |
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| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
Boosting (machine learning) Computer science business.industry 0206 medical engineering 05 social sciences Control (management) Supervised learning Feature extraction Personal mobility 02 engineering and technology Machine learning computer.software_genre 020601 biomedical engineering 0502 economics and business Feature (machine learning) Gradient boosting Artificial intelligence business computer |
| Zdroj: | IROS |
| DOI: | 10.1109/iros.2018.8593578 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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