畳み込みニューラルネットワークを用いた歩行時の膝関節負荷に関連する動作因子の解析

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: 2023/12/01

タイトル(英語): Identification of Motion Factors for Knee Joint Contact Force during Walking Using Convolutional Neural Network

著者名: 諏訪　聡（北九州市立大学），松岡　諒（北九州市立大学），井上　恒（香川大学／国立研究開発法人産業技術総合研究所四国センター）

著者名(英語): Satoshi Suwa (The University of Kitakyushu), Ryo Matsuoka (The University of Kitakyushu), Koh Inoue (Kagawa University/National Institute of Advanced Industrial Science and Technology (AIST))

キーワード: 歩行，関節負荷，モーションキャプチャ，畳み込みニューラルネットワーク，可視化，関節面接触力　　gait，joint load，motion capture，convolutional neural network，visualization，joint contact force

要約(英語): The analysis of knee motion during walking is essential for understanding the mechanisms of knee joint contact force (KJCF), a factor associated with knee joint pain and related joint diseases. A comprehensive analysis of whole-body motion can provide valuable insights and practical strategies for mitigating KJCF. This study aimed to identify body segments whose motion is related to KJCF using a Convolutional Neural Network (CNN). We used a gait database to obtain three-dimensional motion data and calculated their KJCF using a musculoskeletal model. In addition, the peak values of KJCF during gait were classified into five classes to develop a learning model with CNN. Visualization using Gradient-weighted Class Activation Mapping revealed that the regions of interest identified by the trained CNN model were the bases of the middle and ring fingers of the right hand, the outside of the right thigh, the left side of the hip, and the thumb side of the left wrist. These findings suggest that the motions of the hand markers, which change with the arm swings, impact the variation of KJCF.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.12 （2023） 特集：電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1163-1169 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/143/12/143_1163/_article/-char/ja/