Abstract
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.
Highlights
Providing accessibility information of the road for people with difficulties in moving, such as elderly people, mobility impaired people, and visually impaired people, is one of the important social issues
Focusing on the fact that the observed values of acceleration sensors installed in wheelchairs were influenced by the condition of the road surface, we have been proposing a system which evaluates road surface condition by machine learning from acceleration
The purpose of this paper is to propose a system for providing this road accessibility information by human sensing and machine learning techniques
Summary
Providing accessibility information of the road for people with difficulties in moving, such as elderly people, mobility impaired people, and visually impaired people, is one of the important social issues. The conventional method for gathering accessibility information on a large scale is as follows: a method for experts to evaluate sidewalks and their images for each case [4]; crowdsourcing methods to recruit information from volunteers [5,6]; and so on. In all these methods, human labor is indispensable. Focusing on the fact that the observed values of acceleration sensors installed in wheelchairs were influenced by the condition of the road surface, we have been proposing a system which evaluates road surface condition by machine learning from acceleration
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