Estimating human body segment parameters using motion capture data

Abstract

The estimation of human body segment properties (BSPs), including mass, centroid and moments of inertia, is required in the kinetic analysis of human motion. Nowadays, with the development of motion capture technology, motion capture data plays an important role in the kinetic analysis of human motion. An interesting problem is whether BSPs can be estimated using the motion capture data. It is well known that the motions of human body should obey Newton's laws of mechanics, which means that human BSPs and motion data should satisfy the motion equation. Then we build an optimization model according to this principle where the objective function measures the degree of mismatch between motion data, human BSPs and Newton's laws of mechanics. By solving this optimization model, we can estimate the human BSPs. To deal with this optimization, we adopt three strategies: variables block, adjustment on constraints and stochastic local search. This method has two advantages. First, given motion capture data, BSPs can be estimated directly without carrying out additional measurements, such as CT imaging. Second, BSPs and motion capture data can be analyzed together. Simulation experimental results show that the errors of mass, centroid and moment of inertia can be controlled within 10%, 5% and 15% respectively.