How to predict position using linear acceleration and angular velocity by Unscented Kalman Filter?

I am trying to predict the three-dimensional position (displacement) of human gait using an inertial measurement unit (IMU) fixed to the waist to acquire three-dimensional linear acceleration and angular velocity data. I've tried to adapt a function available on the MathWorks website that uses the "Unscented Kalman Filter" (UKF). https://www.mathworks.com/matlabcentral/fileexchange/18217-learning-the-unscented-kalman-filter). My biggest challenge is in replacing the nonlinear state equations of this MATLAB code (for simulated data) with my real acceleration and angular velocity data. My data are structured in six columns, the first three being the linear acceleration (x, y, and z) and the last three being angular velocity data (x, y, and z). I would be very grateful if someone could shed some light on how to adapt this MATLAB code to use with my data.