An increasing number of robotic systems are using compliant actuators in which springs are placed in series with the actuator. The need for identification procedures tailored to these systems is consequently rising. When measurements of both the link side and the motor side of the spring are available the dynamic parameters can be identified independent of the spring model. The excitation of the identification procedure is optimized for the identification of the dynamical parameters to provide a rich data set by maximizing a reduced information matrix. Adopting this reduced matrix leads to a near-optimal excitation. A Fourier series is chosen as parametrization of the excitation, resulting in a periodic movement of the system under identification. This periodicity is exploited to develop an alternative weighting of the parameter estimation. This proposed weighting uses the motor torque variance at each time step of the trajectory, instead of one global torque variance. This identification procedure is demonstrated and validated on one leg of a lower limb exoskeleton. Only the dynamic model in the sagital plane (2D) is identified.