We have developed a three-dimensional (3D) biomechanical model of human standing that enables us to study the mechanisms of posture and balance simultaneously in various directions in space. Since the two feet are on the ground, the system defines a kinematically closed-chain which has redundancy problems that cannot be resolved using the laws of mechanics alone. We have developed a computational (optimization) technique that avoids the problems with the closed-chain formulation thus giving users of such models the ability to make predictions of joint moments, and potentially, muscle activations using more sophisticated musculoskeletal models. This paper describes the experimental verification of the computational technique that is used to estimate the ground reaction vector acting on an unconstrained foot while the other foot is attached to the ground, thus allowing human bipedal standing to be analyzed as an open-chain system. The computational approach was verified in terms of its ability to predict lower extremity joint moments derived from inverse dynamic simulations performed on data acquired from four able-bodied volunteers standing in various postures on force platforms. Sensitivity analyses performed with model simulations indicated which ground reaction force (GRF) and center of pressure (COP) components were most critical for providing better estimates of the joint moments. Overall, the joint moments predicted by the optimization approach are strongly correlated with the joint moments computed using the experimentally measured GRF and COP (0.78 < or = r(2) < or = 0.99,median,0.96) with a best-fit that was not statistically different from a straight line with unity slope (experimental=computational results) for postures of the four subjects examined. These results indicate that this model-based technique can be relied upon to predict reasonable and consistent estimates of the joint moments using the predicted GRF and COP for most standing postures.