title: Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture.
abstract: Ground reaction forces and moments (GRF&amp;M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&amp;M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&amp;M was categorized over normal walking speed as excellent for the vertical (ρ= 0.992, rRMSE = 5.3%), anterior (ρ= 0.965, rRMSE = 9.4%) and sagittal (ρ= 0.933, rRMSE = 12.4%) GRF&amp;M components and as strong for the lateral (ρ= 0.862, rRMSE = 13.1%), frontal (ρ= 0.710, rRMSE = 29.6%), and transverse GRF&amp;M (ρ= 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&amp;M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.
authors.affiliation: Xsens Technologies B.V., Enschede 7521 PR, The Netherlands. angelos.karatsidis@xsens.com.|Institute for Biomedical Technology and Technical Medicine (MIRA), University of Twente, Enschede 7500 AE, The Netherlands. angelos.karatsidis@xsens.com.
authors.forename: Angelos
authors.initials: A
authors.lastname: Karatsidis
authors.affiliation: Xsens Technologies B.V., Enschede 7521 PR, The Netherlands. giovanni.bellusci@xsens.com.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Bellusci
authors.affiliation: Xsens Technologies B.V., Enschede 7521 PR, The Netherlands. martin.schepers@xsens.com.
authors.forename: H Martin
authors.initials: HM
authors.lastname: Schepers
authors.affiliation: Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark. mdz@hst.aau.dk.
authors.forename: Mark
authors.initials: M
authors.lastname: de Zee
authors.affiliation: Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg 9220, Denmark. msa@m-tech.aau.dk.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Andersen
authors.affiliation: Institute for Biomedical Technology and Technical Medicine (MIRA), University of Twente, Enschede 7500 AE, The Netherlands. p.h.veltink@utwente.nl.
authors.forename: Peter H
authors.initials: PH
authors.lastname: Veltink
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D005684:Gait / Q000502:physiology*; D006801:Humans; D016138:Walking / Q000502:physiology
keywords: gait analysis; ground reaction force and moment; inertial motion capture; inverse dynamics