A new approach for improving estimates of the kinematic response of ATDs (anthropomorphic test devices) to vehicle crash events has been developed. This approach employs the Kalman Filter; a state model based estimation approach that has been widely applied to system dynamics problems ranging from navigation to missile guidance. The Kalman Filter approach combines measurements of crash event phenomena (acceleration and displacement), kinematic models of ATD behavior and statistics of sensor noise to create precise estimates of ATD motion during a crash. This paper presents an implementation of a state model and Kalman Filter for a sensor data collected from the chest of an ATD during an out-of-position airbag deployment test. Favorable comparisons are made between the Kalman Filter model approach and traditional methods involving numerical integration and differentiation.