In order to reduce a road monitoring cost, we propose a system to monitor extensively road condition by cyclists with a smartphone. In this paper, we propose two methods towards road monitoring. First is to classify road signals to four road conditions. Second is to extract road signal from a smartphone's accelerometer in three positions: pants' side pocket, chest pocket and a bag in a front basket. In pants' side pocket, road signal is extracted by Independent Component Analysis. In chest pocket and bag in a front basket, road signal is extracted by selecting 1-axis affected from gravitational acceleration. In the experiment of the classification method, overall accuracy was 75%. The experimental results of the extraction methods with correlation coefficient showed the overall accuracy were more than 0.7 in pants' side pocket and chest pocket, the overall accuracy was less than 0.3 in bag in a front basket.

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We propose a novel road monitoring system named YKOB (Your Kinetic Observation Bike) based on participatory sensing. YKOB collects acceleration signals using smartphones worn by cyclists, and analyzes the collected signals to investigate road surface condition. When a bicycle passes on a bump or a dimple, its wheels vibrate. The vibrations are transmitted to the smartphone via the bicycle frame or the cyclist body, and registered as acceleration signals. Conversely, by analyzing the acceleration signals we can estimate the road surface condition. There are mainly two research issues in this system. The first issue is that the acceleration registered at the smartphone includes cyclist motion signal as well as road surface signal. The second issue is that it is necessary to distinguish abnormality of road surface from artificial differences in level, such as a difference between streets and sidewalks. We developed a signal separation algorithm based on independent component analysis to solve the first issue. We also developed a bump classification algorithm using real mother wavelet. These two proposed algorithms were evaluated with 640 trials in total of experimental data conducted by eight cyclists. The classification accuracy of 0.68 validates the simultaneous utilization of our proposed algorithms. C⃝ 2018 Wiley Periodicals, Inc. Electron Comm Jpn, 101(4): 3-14, 2018; Published online in Wiley Online Library (wileyonlinelibrary.com).

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SUMMARY We propose a novel road monitoring system named YKOB (Your Kinetic Observation Bike) based on participatory sensing. YKOB collects acceleration signals using smartphones worn by cyclists, and analyzes the collected signals to investigate road surface condition. When a bicycle passes on a bump or a dimple, its wheels vibrate. The vibrations are transmitted to the smartphone via the bicycle frame or the cyclist body, and registered as acceleration signals. Conversely, by analyzing the acceleration signals we can estimate the road surface condition. There are mainly two research issues in this system. The first issue is that the acceleration registered at the smartphone includes cyclist motion signal as well as road surface signal. The second issue is that it is necessary to distinguish abnormality of road surface from artificial differences in level, such as a difference between streets and sidewalks. We developed a signal separation algorithm based on independent component analysis to solve the first issue. We also developed a bump classification algorithm using real mother wavelet. These two proposed algorithms were evaluated with 640 trials in total of experimental data conducted by eight cyclists. The classification accuracy of 0.68 validates the simultaneous utilization of our proposed algorithms.