Name
Title | ||
|---|---|---|
Road Sensing: Personal Sensing and Machine Learning for Development of Large Scale Accessibility Map |
Abstract | ||
|---|---|---|
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.
|
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2700648.2811366 | ACM Conference on Supporting Group Work |
Keywords | Field | DocType |
Personal Sensing,Machine Learning,Accessibility Map | Wheelchair,Data mining,Visualization,Computer science,Accelerometer,Sensing data,Support vector machine,Supervised learning,Artificial intelligence,Knowledge extraction,Machine learning,Cost database | Conference |
ISBN | Citations | PageRank |
978-1-4503-3400-6 | 2 | 0.36 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yusuke Iwasawa | 1 | 26 | 10.78 |
| Koya Nagamine | 2 | 2 | 0.36 |
| Yutaka Matsuo | 3 | 2966 | 193.76 |
| Ikuko Eguchi Yairi | 4 | 67 | 14.16 |