足圧分布画像を用いた足部アーチ分類手法の提案と検証

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/05/01

タイトル(英語): Proposal and Verification of a Foot Arch Classification Technique using Foot Pressure Distribution Images

著者名: 岩上　優美（電気通信大学），安在　絵美（お茶の水女子大学），中嶋　香奈子（お茶の水女子大学），今泉　一哉（東京医療保健大学），山下　和彦（東京医療保健大学），岡部　誠（電気通信大学／JST CREST），尾内理　紀夫（電気通信大学）

著者名(英語): Yumi Iwakami (The University of Electro-Communications), Emi Anzai (Ochanomizu University), Kanako Nakajima (Ochanomizu University), Kazuya Imaizumi (Tokyo Healthcare University), Kazuhiko Yamashita (Tokyo Healthcare University), Makoto Okabe (The University of Electro-Communications/JST CREST), Rikio Onai (The University of Electro-Communications)

キーワード: 足部アーチ分類，足圧分布画像，機械学習　　foot arch classification，foot pressure distribution images，machine learning

要約(英語): The shape of our feet is important for our good health. If the feet are an abnormal shape, such as ‘high arch' and ‘flat foot', it may adversely affect the performance of walking and maintaining the posture, and damage our health. Therefore, we want to develop a system that allows even non-expert users to measure the shape of their feet and make a diagnosis. Our system takes the image of pressure distribution of feet as an input, and classifies it into four categories: ‘normal', ‘high arch', ‘flat foot', and ‘suspected of an abnormal shape'. We build our classifier using the Adaboost algorithm and decision tree algorithm. Our training data consists of 200 images of pressure distribution of feet that are labeled by the experts in advance. We use the pressure and area information obtained from each input image as the image features. We conduct the verification based on the cross validation on our training data. The resulting accuracy that our classifier achieves is 95.5%. The recalls of the four categories of ‘high arch', ‘normal', ‘flat foot', and ‘suspected of an abnormal shape' are 100%, 94.2%, 96.3%, and 95.8%, which are best performance compared with the previous methods.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.5 （2015） 特集：看護現場の安全性確保のための支援技術

本誌掲載ページ: 505-512 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/5/135_505/_article/-char/ja/