Development of AI medical care system that predicts / prevents locomotive syndrome from the characteristics of knee joint acoustic and body-balance.

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K12655
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 90110:Biomedical engineering-related
• Research Institution: Nihon University
• Principal Investigator: NAGAO Mitsuo 日本大学, 工学部, 研究員 (90139064)
• Co-Investigator(Kenkyū-buntansha): 紺野 愼一 福島県立医科大学, 医学部, 博士研究員 (70254018) ; 荊 雷 会津大学, コンピュータ理工学部, 上級准教授 (30595509)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 運動器症候群 / 膝関節音 / WOMAC / Small Data / バランス能力 / AI

Outline of Final Research Achievements

A strong correlation was observed between wobbly knee joint and trunk balance. The AI data is virtual data based on responses to standard disease-specific rating scales for knee osteoarthritis and clinical data. We proposed a machine learning technology that efficiently achieves high accuracy with a small amount of data, and improved the recognition rate from 60% to over 94%. As a result, we obtained results that contribute to the prediction/prevention of locomotive syndrome. The wobbling motion of the knee joint is measured using a combined inertial and acoustic pasted sensor.

Free Research Field

計測・生体医工学

Academic Significance and Societal Importance of the Research Achievements

学術的意義：体幹バランスと膝関節揺動運動の相関を認める計測因子と解析方法の成果は、生体信号計測デバイスの小型化に利する。診療データの標準化によりAI技術の進化で診療に工学的なエビデンスの活用が期待できる。
社会的意義：予知/予防の視点から、フレイル・介護予防等のリスク管理、包括的な循環型リスク診療管理・健康づくりに関わる医療・ヘルスケア事業関係者には簡便に計測できるデバイスとして活用できる。