Asphalt pavement classification using smartphone accelerometer and Complexity Invariant Distance

Most modern smartphones have a variety of built-in sensors, as an accelerometer, gyroscope, GPS, proximity and a magnetic sensor. The large variety of sensors makes these devices powerful measurement tools, allowing the emergence of new systems and applications. In this paper, is presented a real data stream application related to asphalt pavement evaluation using acceleration data gathered by the accelerometer sensor of smartphones. The quality of the pavement has a significant influence on the final price of goods and services, the safety of drivers, pedestrians and passengers, and driver's comfort. Thus, it is essential the use of tools, as proposed in this work, that allows the constant monitoring of pavement conditions by the Government authorities or private entities for more precise interventions in the maintenance planning with fewer expenses. This task is mainly important in developing countries where there is a lack of technology and a reduced budget for maintenance. Due to the popularity of smartphones, this tool can make possible that different users help to monitor the pavement quality ubiquitously during driving periods without effort. The application of asphalt evaluation is modeled in this work as a multi-dimensional time series classification problem, where the time series are the data from the three-axis accelerometer sensor. Given the characteristics of the data, we discuss and propose the combination of some classical distance measure for time series as Dynamic Time Warping or Longest Common Subsequence Similarity with the Complexity Invariant Distance. A comprehensive experimental evaluation was performed on three datasets that represent different scenarios of asphalt pavement classification. The proposed approach reaches a classification accuracy of 80% to 98% in the three evaluated problems and we experimentally show that the complexity invariance substantially improves the results achieved by classical distance measures given our classification tasks.