Footsteps and inertial data-based road surface condition recognition method

Abstract

We propose a method to recognize road surface conditions using footsteps and inertial data. In areas where the road surface conditions change significantly with the seasons and weather, bad road conditions cause dangerous such as falls. If the road surface condition can be determined in advance, danger can be averted by selecting safe routes and suitable shoes. In this study, we focus on the footsteps and inertial data that change depending on road surface conditions, such as dry pavement, puddle, soil, and mud. We implemented the prototype device and evaluated the proposed method on six road surface conditions with eight participants. The evaluation results confirmed that the recognition accuracy was 83.0% in a low-noise environment. When there was noise, we compared the standard approach, which combines footsteps and inertial data, and the revised method, which changes the confidence of the result of footstep recognition by the signal-noise ratio (SNR). The evaluation results also confirmed that the recognition rate increased by a maximum of 16.4% using the revised method (when the SNR was 1 dB, the average accuracy was improved from 37.5% to 53.9%).