A novel damage characterization technique based on adaptive deconvolution extraction algorithm of multivariate AE signals for accurate diagnosis of osteoarthritic knees

Research Project

Project/Area Number	24K07389
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Multi-year Fund
Section	一般
Review Section	Basic Section 20010:Mechanics and mechatronics-related
Research Institution	Saga University
Principal Investigator	KHAN M.I. 佐賀大学, 理工学部, 准教授 (50423603)
Project Period (FY)	2024-04-01 – 2027-03-31
Project Status	Granted (Fiscal Year 2024)
Budget Amount *help	¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000) Fiscal Year 2026: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000) Fiscal Year 2025: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000) Fiscal Year 2024: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Keywords	適応型デコンボリューション抽出アルゴリズム / 多変量 AE 信号 / 知能機械 / 人間機械システム / 診断評価膝関節炎症診断
Outline of Research at the Start	The main feature of the proposed research is that it considers the dynamically self-generated multivariate AE signals as passive input sources for the adaptive deconvolution in extracting the dominating source signal which reveals exact damage information making the inhomogeneous knee joint with adaptively sized damaged source. Therefore, it works as a novel technique for damage characterization of osteoarthritic knees. Hence, the proposed procedure becomes a worthy in accurately dealing with osteoarthritis at knee joint, even in early stage.