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We present a reality-based locomotion study directly applicable to video game interfaces; specifically, locomotion control of the quarterback in American football. Focusing on American football drives requirements and ecologically grounds the interface tasks of: running down the field, maneuvering in a small area, and evasive gestures such as spinning, jumping, and the Â¿juke. The locomotion interface is constructed by exploring data interpretation methods on two commodity hardware configurations. The choices represent a comparison between hardware available to video game designers, trading off traditional 3D interface data for greater hardware availability. Configuration one matches traditional 3D interface data, with a commodity head tracker and leg accelerometers for running in place. Configuration two uses a spatially convenient device with a single accelerometer and infrared camera. Data interpretation methods on configuration two use two elementary approaches and a third hybrid approach, making use of the disparate and intermittent input data combined with a Kalman filter. Methods incorporating gyroscopic data are used to further improve the interpretation. Our results show spatially convenient hardware, currently in many gamers' homes, when properly interpreted can lead to more robust interfaces. We support this by a user evaluation on the metrics of position and orientation accuracy, range and gesture recognition.