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Modeling individual-specific gait dynamics based on kinematic data could aid development of gait rehabilitation robotics by enabling robots to predict the user’s gait kinematics with and without external inputs, such as mechanical or electrical perturbations. Here we address a current limitation of data driven gait models, which do not yet predict human responses to perturbations. We used Switched Linear Dynamical Systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill during normal gait and during gait perturbed by functional electrical stimulation (FES) to the ankle muscles. Our SLDS models were able to predict the time-evolution of joint kinematics in each of four gait phases, as well as across an entire gait cycle. Because the SLDS dynamics matrices encoded significant coupling across joints, we compared the SLDS predictions to that of a kinematic model, where the joint angles were independent. Gait kinematics predicted by SLDS and kinematic models were similar over time horizons of a few milliseconds, but SLDS models provided better predictions of gait kinematics over time horizons of up to a second. We also demonstrated that SLDS models can infer and predict individual-specific responses to FES during swing phase. As such, SLDS models may be a promising approach for online estimation and control of and human gait dynamics, allowing robotic control strategies to be tailored to an individual’s specific gait coordination patterns.