Impaired gait in Parkinson's disease is marked by slow, arrhythmic stepping, and often includes freezing of gait episodes where alternating stepping halts completely. Wearable inertial sensors offer a way to detect these gait changes and novel deep brain stimulation (DBS) systems can respond with clinical therapy in a real-time, closed-loop fashion. In this paper, we present two novel closed-loop DBS algorithms, one using gait arrhythmicity and one using a logistic-regression model of freezing of gait detection as control signals. Benchtop validation results demonstrate the feasibility of running these algorithms in conjunction with a closed-loop DBS system by responding to real-time human subject kinematic data and pre-recorded data from leg-worn inertial sensors from a participant with Parkinson's disease. We also present a novel control policy algorithm that changes neurostimulator frequency in response to the kinematic inputs. These results provide a foundation for further development, iteration, and testing in a clinical trial for the first closed-loop DBS algorithms using kinematic signals to therapeutically improve and understand the pathophysiological mechanisms of gait impairment in Parkinson's disease.