Quantifying dynamic stability is important to assessment of falling risk or functional recovery for leg injured people. Human locomotion is complex and known to exhibit nonlinear dynamical behaviors. The purpose of this study is to quantify major joints of the body using chaos analysis during walking. Time series of the chaotic signals show how gait patterns change over time. The gait experiments were carried out for ten young males walking on a motorized treadmill. Joint motions were captured using eight video cameras, and then three dimensional kinematics of the neck and the upper and lower extremities were computed by KWON 3D motion analysis software. The correlation dimension and the largest Lyapunov exponent were calculated from the time series to quantify stabilities of the joints. This study presents a data set of nonlinear dynamic characteristics for eleven joints engaged in normal level walking.