TY - JOUR
T1 - Estimation of ground reaction forces and moments during gait using only inertial motion capture
AU - Karatsidis, Angelos
AU - Bellusci, Giovanni
AU - Schepers, H. Martin
AU - de Zee, Mark
AU - Andersen, Michael S.
AU - Veltink, Peter H.
PY - 2017/1
Y1 - 2017/1
N2 - Ground reaction forces and moments (GRF&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&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&M was categorized over normal walking speed as excellent for the vertical (Ͽ = 0.992, rRMSE = 5.3%), anterior (Ͽ = 0.965, rRMSE = 9.4%) and sagittal (Ͽ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (Ͽ = 0.862, rRMSE = 13.1%), frontal (Ͽ = 0.710, rRMSE = 29.6%), and transverse GRF&M (Ͽ = 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&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.
AB - Ground reaction forces and moments (GRF&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&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&M was categorized over normal walking speed as excellent for the vertical (Ͽ = 0.992, rRMSE = 5.3%), anterior (Ͽ = 0.965, rRMSE = 9.4%) and sagittal (Ͽ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (Ͽ = 0.862, rRMSE = 13.1%), frontal (Ͽ = 0.710, rRMSE = 29.6%), and transverse GRF&M (Ͽ = 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&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.
KW - Inertial motion capture
KW - Ground reaction force and moment
KW - EC Grant Agreement nr.: FP7/607510
KW - BSS-Biomechatronics and rehabilitation technology
KW - Gait analysis
KW - Inverse dynamics
U2 - 10.3390/s17010075
DO - 10.3390/s17010075
M3 - Article
SN - 1424-8220
VL - 17
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 1
M1 - 75
ER -