[4A1] [General Session] 2. Machine Learning

[4A1-03] Study on data labeling method using GPS for DCNN learning to extract road surface characteristics from wheelchair sensing data

〇Hiroki Takahashi¹, Yusuke Iwasawa², Koya Nagamine¹, Ikuko Eguchi Yairi¹ (1. Graduate School of Science and Engineering, Sophia University, 2. University of Tokyo)

Keywords:Deep Learning, GPS, wheelchair, Human Sensing

Recent expansion of intelligent gadgets, such as smartphones and smartwatches with vital sensors, make it easy to sense a human behavior. We are developing a road accessibility evaluation system inspired by human behavior sensing technologies. Our proposed system aims to estimate road accessibility as environmental factors, e.g. curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g. wheelchair users’ feeling tired and strain, which are results of the environmental factors. This paper introduces a data labeling method using GPS for DCNN learning to extract road surface characteristics from wheelchair sensing data. As a conventional method, the manpower based labeling have been used by comparing wheelchair sensing data with recorded video of wheelchair traveling. This paper evaluates and reports the effectiveness of the proposed method.