Nonlinear state-space modeling of human motion using 2-D marker observations.
A novel method for the estimation of human kinematics, based on state-space modeling, is proposed. The state consists of the positions, orientations, velocities, and accelerations of an articulated model. Estimation is performed using the unscented Kalman filter (UKF) algorithm with a fixed-interval smoother. Impulsive acceleration at floor contact of the foot is estimated by implementing a contact constraint in the UKF evolution model. The constraint inserts an acceleration impulse into the model state. The estimation method was applied to marker-based motion analysis in a motion laboratory. Validation measurements were performed with a rigid test device and with human gait. A triaxial accelerometer was used to evaluate acceleration estimates. Comparison between the proposed method and the extended Kalman smoother showed a clear difference in the quality of estimates during impulsive accelerations. The proposed approach enables estimation of human kinematics during both continuous and transient accelerations. The approach provides a novel way of estimating acceleration at foot initial contact, and thus enables more accurate evaluation of loading from the beginning of the floor contact.
Paavo
P
Vartiainen
Timo
T
Bragge
Jari P
JP
Arokoski
Pasi A
PA
Karjalainen
D000054:Acceleration; D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena / Q000502:physiology*; D005684:Gait / Q000502:physiology*; D006801:Humans; D035002:Lower Extremity / Q000033:anatomy & histology / Q000502:physiology; D008954:Models, Biological; D017711:Nonlinear Dynamics