Kinematics Modeling of Human Motion Using System Identification Technique

Image processing techniques from motion captured images are accurate and cost effective method to give a set of data that defines the location of specified limb at every sequence of human motion. From this set of data, system identification was done to model the human motion. This project is a study on how performance of a model is influenced by the type of model whether it is a linear model or non-linear model and a single variable model or multi variable model. Two types of parameter estimator was used which were the least square estimate and recursive least square estimate. The study also was conducted to see how the number of lags can give effects to the model. The objective is to formulate a predictive model to analyze human motion. Simulation was done through the model to see the result and performance of model whether it can be a model for human motion representation.