Computation of ground reaction force using Zero Moment Point. Issue 14 (5th November 2015)

• Record Type: Journal Article
• Authors: Dijkstra, Erik J.; Gutierrez-Farewik, Elena M.
• Is Part Of: Journal of biomechanics. Volume 48:Issue 14(2015)
• Page Start: 3776
• Page End: 3781
• Publication Date: 2015-11-05
• Subjects: Prediction -- Inverse dynamics -- Forward dynamics -- Gait; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2015.08.027
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Motion analysis is a common clinical assessment and research tool that uses a camera system or motion sensors and force plates to collect kinematic and kinetic information of a subject performing an activity of interest. The use of force plates can be challenging and sometimes even impossible. Over the past decade, several computational methods have been developed that aim to preclude the use of force plates. Useful in particular for predictive simulations, where a new motion or change in control strategy inherently means different external contact loads. These methods, however, often depend on prior knowledge of common observed ground reaction force (GRF) patterns, are computationally expensive, or difficult to implement. In this study, we evaluated the use of the Zero Moment Point as a computationally inexpensive tool to obtain the GRFs for normal human gait. The method was applied on ten healthy subjects walking in a motion analysis laboratory and predicted GRFs are evaluated against the simultaneously measured force plate data. Apart from the antero–posterior forces, GRFs are well-predicted and errors fall within the error ranges from other published methods. Joint extension moments were underestimated at the ankle and hip but overestimated at the knee, attributable to the observed discrepancy in the predicted application points of the GRFs. The computationally inexpensive method evaluated in this study can reasonably well predict the GRFs for normal human gait without using prior knowledge of common gait kinetics.