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Estimation of Sidewalk Surface Type with a Smartphone

In this study, we developed a method for estimating the type of sidewalk surface with acceleration data measured by a smartphone's accelerometer while the user is walking. If the type of sidewalk surface can be detected automatically, a large amount of sidewalk information, such as which sidewalks are easy to walk on and which are difficult, can be collected just by many people walking while carrying their smartphones. We can contribute to the creation of a safe society by sharing the accumulated sidewalk information with a map application. We propose a method that estimates type of sidewalk by machine learning using feature values extracted from acceleration data. The proposed method detects six types of sidewalk: asphalt, gravel, lawn, grass, sand, and mat, imitating snowy sidewalk. Through experiments, we considered how far the shape of the sidewalk surface can be classified from smartphone sensor data. In this study, we used Random Forest as the machine learning algorithm. We conducted experiments on seven subjects and evaluated the method's accuracy using Leave-One-Subject-Out Cross Validation (Subject-CV) and Leave-One-Session-Out Cross Validation (Session-CV). The results showed that the proposed method can detect the type of sidewalk surface with 44.9% accuracy when evaluated with Subject-CV and 83.5% accuracy when evaluated with Session-CV.