Machine Learning Based Skill-Level Classification for Personal Mobility Devices Using Only Operational Characteristics
Machine Learning Based Skill-Level Classification for Personal Mobility Devices Using Only Operational Characteristics
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DOI：
10.1109/iros.2018.8593578
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发表时间：
2018-10
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通讯作者：
Yifan Huang;Taiga Mori;Udara E. Manawadu;Mitsuhiro Kamezaki;Tatsuya Ishihara;M. Nakano;Kohjun Koshiji;Naoki Higo;Toshimitsu Tubaki;S. Sugano
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文献类型：
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作者：
Yifan Huang;Taiga Mori;Udara E. Manawadu;Mitsuhiro Kamezaki;Tatsuya Ishihara;M. Nakano;Kohjun Koshiji;Naoki Higo;Toshimitsu Tubaki;S. Sugano
一些电动轮椅最近被重新定义为个人移动设备。它的使用者不仅是老年人或残疾人，还包括携带大件行李的乘客或从站到目的地的行人，即最后一英里运输。因此，具有不同操作技能和对个人移动性期望的人将成为这类设备的新用户。在人行道和机场等人类共存的环境中安全舒适的旅行是社会对个人机动性的期望。为了实现这一点，通过实用而简单的方法了解每个用户的操作技巧是必不可少的。因此，本文介绍了一种仅使用操纵杆数据作为输入的机器学习技能水平分类方法。为了确定技能水平聚类的数量，使用操纵杆操作数据的26个基本特征进行无监督聚类（单链接）。然后从速度、速度控制和方向控制三个方面制定了评价指标。对于使用梯度增强作为监督学习的五级分类，我们实现了67%的准确率（公差：0）和98%的准确率（公差：1）。进一步分析了梯度增强的特征重要性，揭示了实现良好操作的关键特征。结果还表明，不同驾驶经历的人的技能水平也不同。
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.