足音と慣性データに基づく路面状況認識手法

季節や天候によって路面状況が大きく変化する地域では，路面状況が悪いと転倒等の危険が生じる． 路面状況を事前に把握できれば，安全なルートと適切な靴を選択することで危険を回避できる． 本研究では，乾燥した舗装，水たまり，土，泥等の路面状況に応じて変化する足音と慣性データに着目し，足音と 慣性データを用いた路面状況認識手法を提案する． プロトタイプを実装し，8人の被検者に対して，6つの路面状況下で提案手法を評価した． 低雑音環境下では，提案手法の認識精度が83.0%であることを確認した． 雑音環境下の場合，足音と慣性データを組み合わせた標準手法と，信号対雑音比（SNR）により足音認識結果の信頼性を変更する改善手法を比較した． 評価実験の結果，車の走行音や他人の足音が混入した場合では，改善手法を用いてすべてのSNR環境下の認識精度の改善を確認した．

A Method for Recognizing Road Surface Condition based on Footsteps and Inertial Data

In areas where the road surface conditions change significantly with the seasons and weather, bad road surface 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, and propose a method for recognizing road surface conditions using footsteps and inertial data. 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 method, 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 confirmed that when the driving sounds of cars or the footstep of the other person were mixed, the recognition accuracy was improved in all SNR environments using the revised method.