Musculoskeletal model-based inverse dynamic analysis under ambulatory conditions using inertial motion capture. (March 2019)

• Record Type: Journal Article
• Authors: Karatsidis, Angelos; Jung, Moonki; Schepers, H. Martin; Bellusci, Giovanni; de Zee, Mark; Veltink, Peter H.; Andersen, Michael Skipper
• Is Part Of: Medical engineering & physics. Volume 65(2019)
• Page Start: 68
• Page End: 77
• Publication Date: 2019-03
• Subjects: Musculoskeletal modeling -- Inertial motion capture -- Inverse dynamics -- Ground reaction forces and moments -- Gait analysis; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2018.12.021
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Close match in joint angles between inertial and optical motion capture. Predicted ground and joint reaction forces and moments using only inertial sensors. Ground reaction forces and moments correlate highly to force plate reference. Kinetics prediction similar regardless of inertial or optical motion capture input. The inertial setup enables applications of musculoskeletal models outside the lab. Abstract: Inverse dynamic analysis using musculoskeletal modeling is a powerful tool, which is utilized in a range of applications to estimate forces in ligaments, muscles, and joints, non-invasively. To date, the conventional input used in this analysis is derived from optical motion capture (OMC) and force plate (FP) systems, which restrict the application of musculoskeletal models to gait laboratories. To address this problem, we propose the use of inertial motion capture to perform musculoskeletal model-based inverse dynamics by utilizing a universally applicable ground reaction force and moment (GRF&M) prediction method. Validation against a conventional laboratory-based method showed excellent Pearson correlations for sagittal plane joint angles of ankle, knee, and hip ( ρ = 0.95, 0.99, and 0.99, respectively) and root-mean-squared-differences (RMSD) of 4.1 ± 1.3°, 4.4 ± 2.0°, and 5.7 ± 2.1°, respectively. The GRF&M predicted using IMC input were found to have excellent correlations for three components (vertical: ρ = 0.97, RMSD = 9.3 ± 3.0 %BW, anteroposterior: ρ = 0.91, RMSD = 5.5 ± 1.2 %BW, sagittal: ρ = 0.91, RMSD = 1.6 ± 0.6 %BW*BH), and strong correlations for mediolateral ( ρ = 0.80, RMSD = 2.1 ± 0.6 %BW) and transverse ( ρ = 0.82, RMSD = 0.2 ± 0.1 %BW*BH). The proposed IMC-based method removes the complexity and space restrictions of OMC and FP systems and could enable applications of musculoskeletal models in either monitoring patients during their daily lives or in wider clinical practice.