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Information 2019, 10(3), 114;

Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies

Department of Science and Engineering, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japan
Department of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Author to whom correspondence should be addressed.
Received: 30 January 2019 / Revised: 1 March 2019 / Accepted: 4 March 2019 / Published: 15 March 2019
(This article belongs to the Special Issue MoDAT: Designing the Market of Data)
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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. View Full-Text
Keywords: human sensing; wheelchair; road accessibility; feature extraction; deep learning human sensing; wheelchair; road accessibility; feature extraction; deep learning

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Yairi, I.E.; Takahashi, H.; Watanabe, T.; Nagamine, K.; Fukushima, Y.; Matsuo, Y.; Iwasawa, Y. Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies. Information 2019, 10, 114.

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