文章基本信息

标题：Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies
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作者：Ikuko Eguchi Yairi ; Ikuko Eguchi Yairi ; Hiroki Takahashi 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2019
卷号：10
期号：3
页码：114
DOI：10.3390/info10030114
出版社：MDPI Publishing
摘要：Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis acceleration data obtained by a smart phone attached on a wheelchair seat, such as environmental factors, e.g., curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g., wheelchair users’ feelings of tiredness and strain. Our goal is to realize a system that provides the road accessibility visualization services to users by online/offline pattern matching using impersonal models, while gradually learning to improve service accuracy using new data provided by users. As the first step, this paper evaluates features acquired by the DCNN (deep convolutional neural network), which learns the state of the road surface from the data in supervised machine learning techniques. The evaluated results show that the features can capture the difference of the road surface condition in more detail than the label attached by us and are effective as the means for quantitatively expressing the road surface condition. This paper developed and evaluated a prototype system that estimated types of ground surfaces focusing on knowledge extraction and visualization.
关键词：human sensing; wheelchair; road accessibility; feature extraction; deep learning human sensing ; wheelchair ; road accessibility ; feature extraction ; deep learning