Boost Treadmills™, a company specializing in the use of differential air-pressure (DAP) technology in the realm of exercise and rehabilitation, is developing their second-generation micro-gravity treadmill™ named the Boost 2. This machine will allow individuals in all types of fitness environments to move and exercise at a fraction of their normal body weight, reducing the impact forces on lower body extremities and allowing for accelerated returns to activity. The purpose of this thesis was to create an algorithm to be implemented on the Boost 2 that related the percentage of body weight reduction of a user to a required pressure value within the Boost 2 pressure differential space created by the enclosure. This algorithm, when implemented, would allow a Boost 2 user to simply operate the machine at the appropriate pressure(s) automatically by entering their Boost shorts size, approximate height, and desired percentage of body weight reduction from a previously developed user interface screen. The calibration algorithm was developed using collected data and a regression analysis model created in Microsoft Excel®. This analysis creates an equation for a linear fit of predictor variables resulting in a line representing the body weight reduction pattern of a user as pressure is increased within the enclosure. On average, the difference between measured and predicted body weight percentages at measured pressure values was found to be less than 3%.