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YKOB: Participatory Sensing-Based Road Condition Monitoring Using Smartphones Worn by Cyclist

机译:YKOB:使用骑自行车者佩戴的智能手机进行基于参与感知的道路状况监控

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摘要

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.
机译:我们提出了一种基于参与感测的新型道路监控系统YKOB(您的动力学观察自行车)。 YKOB使用骑自行车的人佩戴的智能手机收集加速度信号,并分析收集的信号以调查路面状况。当自行车经过颠簸或酒窝时,其车轮会振动。振动通过自行车车架或骑车者的身体传递到智能手机,并记录为加速度信号。相反,通过分析加速度信号,我们可以估算路面状况。该系统主要有两个研究问题。第一个问题是,智能手机上记录的加速度包括骑自行车的人的运动信号以及路面信号。第二个问题是,有必要将路面的异常与人为的水平差异(例如街道和人行道之间的差异)区分开来。我们开发了基于独立分量分析的信号分离算法来解决第一个问题。我们还开发了使用真实母小波的凹凸分类算法。由八个骑车人进行的总共640项试验数据对这两种拟议算法进行了评估。 0.68的分类精度验证了我们提出的算法的同时利用。

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