Road Sensing: Personal Sensing and Machine Learning for Development of Large Scale Accessibility Map
Yusuke Iwasawa, Koya Nagamine, Yutaka Matsuo and Ikuko E. Yairi
The 17th International ACM SIGACCESS Conference on Computers and Accessibility - Posters and Demos (ASSETS 2015)
Lisbon, Portugal, October 26-28, 2015
This paper proposes a methodology for developing large scale accessibility map with personal sensing by using smart phone and machine learning technologies. The strength of the proposed method is its low cost data collection, which is a key to break through stagnations of accessibility map that currently applied to limited areas. This paper developed and evaluated a prototype system that estimates types of ground surfaces by applying supervised learning techniques to activity sensing data of wheelchair users recorded by a three-axis accelerometer, focusing on knowledge extraction and visualization. As a result of evaluation using nine wheelchair users' data with Support Vector Machine, three ground surface types, curb, tactile indicator, and slope, were detected with f-score (and accuracy) of 0.63 (0.92), 0.65 (0.85), and 0.54 (0.97) respectively.
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